refs #24 and #25

Last RMI occurences have been removed. Free Willy is officially dead. Hooray!

Also, code formatting :)

src/eva2/client/EvAClient.java

@@ -772,15 +772,15 @@ public class EvAClient extends JFrame implements OptimizationStateListener {
         menuBar = new JMenuBar();
         setJMenuBar(menuBar);
         menuModule = new JExtMenu("&Module");
-        menuModule.add(actModuleLoad);
+        //menuModule.add(actModuleLoad);
 
         menuSelHosts = new JExtMenu("&Select Hosts");
-        menuSelHosts.setToolTipText("Select a host for the server application");
-        menuSelHosts.add(actHost);
-        menuSelHosts.add(actAvailableHost);
-        menuSelHosts.addSeparator();
-        menuSelHosts.add(actKillHost);
-        menuSelHosts.add(actKillAllHosts);
+        //menuSelHosts.setToolTipText("Select a host for the server application");
+        //menuSelHosts.add(actHost);
+        //menuSelHosts.add(actAvailableHost);
+        //menuSelHosts.addSeparator();
+        //menuSelHosts.add(actKillHost);
+        //menuSelHosts.add(actKillAllHosts);
 
         menuHelp = new JExtMenu("&Help");
         menuHelp.add(actHelp);
@@ -790,13 +790,13 @@ public class EvAClient extends JFrame implements OptimizationStateListener {
 
         menuOptions = new JExtMenu("&Options");
         menuOptions.add(actPreferences);
-        menuOptions.add(menuSelHosts);
+        //menuOptions.add(menuSelHosts);
         menuOptions.addSeparator();
         menuOptions.add(actQuit);
         // this is accessible if no default module is given
-        if (showLoadModules) {
-            menuBar.add(menuModule);
-        }
+        //if (showLoadModules) {
+        //    menuBar.add(menuModule);
+        //}
 
         menuBar.add(menuOptions);
         menuBar.add(((JExtDesktopPane) desktopPane).getWindowMenu());

src/eva2/server/go/InterfaceGOStandalone.java

@@ -8,6 +8,6 @@ package eva2.server.go;
  * To change this template use Options | File Templates.
  */
 public interface InterfaceGOStandalone {
-    public void startExperiment();
-    public void setShow(boolean t);
+    void startExperiment();
+    void setShow(boolean t);
 }

src/eva2/server/go/InterfacePopulationChangedEventListener.java

@@ -14,5 +14,5 @@ public interface InterfacePopulationChangedEventListener {
      * @param source        The source of the event.
      * @param name          Could be used to indicate the nature of the event.
      */
-    public void registerPopulationStateChanged(Object source, String name);
+    void registerPopulationStateChanged(Object source, String name);
 }

src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java

@@ -1,6 +1,5 @@
 package eva2.server.go.strategies;
 
-
 import eva2.server.go.InterfacePopulationChangedEventListener;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.InterfaceGAIndividual;
@@ -15,50 +14,49 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 import java.util.BitSet;
 
-/** This is an implementation of the CHC Adaptive Search Algorithm by Eshelman. It is
- * limited to binary data and is based on massively disruptive crossover. I'm not
- * sure whether i've implemented this correctly, but i definitely wasn't able to make
- * it competitive to a standard GA.. *sigh*
- * This is a implementation of the CHC Adaptive Search Algorithm (Cross generational
+/**
+ * This is an implementation of the CHC Adaptive Search Algorithm by Eshelman.
+ * It is limited to binary data and is based on massively disruptive crossover.
+ * I'm not sure whether i've implemented this correctly, but i definitely wasn't
+ * able to make it competitive to a standard GA.. *sigh* This is a
+ * implementation of the CHC Adaptive Search Algorithm (Cross generational
  * elitist selection, Heterogeneous recombination and Cataclysmic mutation).
  * Citation:
  *
- * Copyright:       Copyright (c) 2003
- * Company:         University of Tuebingen, Computer Architecture
- * @author          Felix Streichert
- * @version:  $Revision: 307 $
- *            $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
- *            $Author: mkron $
+ * Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
+ * Architecture
+ *
+ * @author Felix Streichert
+ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
+ * 2007) $ $Author: mkron $
  */
-
 public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.Serializable {
 
-    private double                          m_InitialDifferenceThreshold = 0.25;
-    private int                             m_DifferenceThreshold;
-    private double                          m_DivergenceRate    = 0.35;
-    private boolean                         m_UseElitism        = true;
-    private int                             m_NumberOfPartners  = 1;
-    private Population                      m_Population        = new Population();
-    private InterfaceOptimizationProblem    m_Problem           = new B1Problem();
-    private InterfaceSelection              m_RecombSelectionOperator = new SelectRandom();
-    private InterfaceSelection              m_PopulSelectionOperator = new SelectBestSingle();
-
-    transient private String                m_Identifier = "";
+    private double m_InitialDifferenceThreshold = 0.25;
+    private int m_DifferenceThreshold;
+    private double m_DivergenceRate = 0.35;
+    private boolean m_UseElitism = true;
+    private int m_NumberOfPartners = 1;
+    private Population m_Population = new Population();
+    private InterfaceOptimizationProblem m_Problem = new B1Problem();
+    private InterfaceSelection m_RecombSelectionOperator = new SelectRandom();
+    private InterfaceSelection m_PopulSelectionOperator = new SelectBestSingle();
+    transient private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
 
     public CHCAdaptiveSearchAlgorithm() {
     }
 
     public CHCAdaptiveSearchAlgorithm(CHCAdaptiveSearchAlgorithm a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_InitialDifferenceThreshold   = a.m_InitialDifferenceThreshold;
-        this.m_DifferenceThreshold          = a.m_DifferenceThreshold;
-        this.m_DivergenceRate               = a.m_DivergenceRate;
-        this.m_NumberOfPartners             = a.m_NumberOfPartners;
-        this.m_UseElitism                   = a.m_UseElitism;
-        this.m_RecombSelectionOperator      = (InterfaceSelection)a.m_RecombSelectionOperator.clone();
-        this.m_PopulSelectionOperator       = (InterfaceSelection)a.m_PopulSelectionOperator.clone();
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_InitialDifferenceThreshold = a.m_InitialDifferenceThreshold;
+        this.m_DifferenceThreshold = a.m_DifferenceThreshold;
+        this.m_DivergenceRate = a.m_DivergenceRate;
+        this.m_NumberOfPartners = a.m_NumberOfPartners;
+        this.m_UseElitism = a.m_UseElitism;
+        this.m_RecombSelectionOperator = (InterfaceSelection) a.m_RecombSelectionOperator.clone();
+        this.m_PopulSelectionOperator = (InterfaceSelection) a.m_PopulSelectionOperator.clone();
     }
 
     @Override
@@ -69,9 +67,9 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
     @Override
     public void init() {
         this.m_Problem.initPopulation(this.m_Population);
-        AbstractEAIndividual tmpIndy = ((AbstractEAIndividual)(this.m_Population.get(0)));
+        AbstractEAIndividual tmpIndy = ((AbstractEAIndividual) (this.m_Population.get(0)));
         if (tmpIndy instanceof InterfaceGAIndividual) {
-            this.m_DifferenceThreshold = (int)(((InterfaceGAIndividual)tmpIndy).getGenotypeLength()*this.m_InitialDifferenceThreshold);
+            this.m_DifferenceThreshold = (int) (((InterfaceGAIndividual) tmpIndy).getGenotypeLength() * this.m_InitialDifferenceThreshold);
         } else {
             System.out.println("Problem does not apply InterfaceGAIndividual, which is the only individual type valid for CHC!");
         }
@@ -80,31 +78,34 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
-        this.m_Population = (Population)pop.clone();
+        this.m_Population = (Population) pop.clone();
         if (reset) {
             this.m_Population.init();
         }
-        AbstractEAIndividual tmpIndy = ((AbstractEAIndividual)(this.m_Population.get(0)));
+        AbstractEAIndividual tmpIndy = ((AbstractEAIndividual) (this.m_Population.get(0)));
         if (tmpIndy instanceof InterfaceGAIndividual) {
-            this.m_DifferenceThreshold = (int)(((InterfaceGAIndividual)tmpIndy).getGenotypeLength()*this.m_InitialDifferenceThreshold);
+            this.m_DifferenceThreshold = (int) (((InterfaceGAIndividual) tmpIndy).getGenotypeLength() * this.m_InitialDifferenceThreshold);
         } else {
             System.out.println("Problem does not apply InterfaceGAIndividual, which is the only individual type valid for CHC!");
         }
 
         if (reset) {
-        	this.evaluatePopulation(this.m_Population);
+            this.evaluatePopulation(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
-    /** This method will evaluate the current population using the
-     * given problem.
+    /**
+     * This method will evaluate the current population using the given problem.
+     *
      * @param population The population that is to be evaluated
      */
     private void evaluatePopulation(Population population) {
@@ -112,29 +113,30 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         population.incrGeneration();
     }
 
-    /** This method will generate the offspring population from the
-     * given population of evaluated individuals.
+    /**
+     * This method will generate the offspring population from the given
+     * population of evaluated individuals.
      */
     private Population generateChildren() {
-        Population                  result = this.m_Population.cloneWithoutInds(), parents, partners;
-        AbstractEAIndividual[]     offSprings;
-        AbstractEAIndividual       tmpIndy;
+        Population result = this.m_Population.cloneWithoutInds(), parents, partners;
+        AbstractEAIndividual[] offSprings;
+        AbstractEAIndividual tmpIndy;
 
         result.clear();
 //        this.m_NormationOperator.computeSelectionProbability(this.m_Population, "Fitness");
         //System.out.println("Population:"+this.m_Population.getSolutionRepresentationFor());
         this.m_PopulSelectionOperator.prepareSelection(this.m_Population);
         this.m_RecombSelectionOperator.prepareSelection(this.m_Population);
-        parents     = this.m_PopulSelectionOperator.selectFrom(this.m_Population, this.m_Population.getTargetSize());
+        parents = this.m_PopulSelectionOperator.selectFrom(this.m_Population, this.m_Population.getTargetSize());
         //System.out.println("Parents:"+parents.getSolutionRepresentationFor());
 
         for (int i = 0; i < parents.size(); i++) {
-            tmpIndy =  ((AbstractEAIndividual)parents.get(i));
+            tmpIndy = ((AbstractEAIndividual) parents.get(i));
             if (tmpIndy == null) {
-                System.out.println("Individual null "+i);
+                System.out.println("Individual null " + i);
             }
             if (this.m_Population == null) {
-                System.out.println("population null "+i);
+                System.out.println("population null " + i);
             }
 
             partners = this.m_RecombSelectionOperator.findPartnerFor(tmpIndy, this.m_Population, this.m_NumberOfPartners);
@@ -149,20 +151,22 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         return result;
     }
 
-    /** This method computes the Hamming Distance between n-Individuals
+    /**
+     * This method computes the Hamming Distance between n-Individuals
+     *
      * @param dad
      * @param partners
      * @return The maximal Hamming Distance between dad and the partners
      */
     private int computeHammingDistance(AbstractEAIndividual dad, Population partners) {
-        int     result = 0, tmpDist;
-        BitSet  tmpB1, tmpB2;
+        int result = 0, tmpDist;
+        BitSet tmpB1, tmpB2;
 
-        tmpB1 = ((InterfaceGAIndividual)dad).getBGenotype();
+        tmpB1 = ((InterfaceGAIndividual) dad).getBGenotype();
         for (int i = 0; i < partners.size(); i++) {
             tmpB2 = ((InterfaceGAIndividual) partners.get(i)).getBGenotype();
             tmpDist = 0;
-            for (int j = 0; j < ((InterfaceGAIndividual)dad).getGenotypeLength(); j++) {
+            for (int j = 0; j < ((InterfaceGAIndividual) dad).getGenotypeLength(); j++) {
                 if (tmpB1.get(j) == tmpB2.get(j)) {
                     tmpDist++;
                 }
@@ -172,25 +176,26 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         return result;
     }
 
-    /** This method method replaces the current population with copies of the current
-     * best individual but all but one are randomized with a very high mutation rate.
+    /**
+     * This method method replaces the current population with copies of the
+     * current best individual but all but one are randomized with a very high
+     * mutation rate.
      */
     private void diverge() {
-        AbstractEAIndividual    best = this.m_Population.getBestEAIndividual();
-        InterfaceGAIndividual   mutant;
-        BitSet                  tmpBitSet;
+        AbstractEAIndividual best = this.m_Population.getBestEAIndividual();
+        InterfaceGAIndividual mutant;
+        BitSet tmpBitSet;
 
         this.m_Population.clear();
         this.m_Population.add(best);
         for (int i = 1; i < this.m_Population.getTargetSize(); i++) {
-            mutant      = (InterfaceGAIndividual)best.clone();
-            tmpBitSet   = mutant.getBGenotype();
+            mutant = (InterfaceGAIndividual) best.clone();
+            tmpBitSet = mutant.getBGenotype();
             for (int j = 0; j < mutant.getGenotypeLength(); j++) {
                 if (RNG.flipCoin(this.m_DivergenceRate)) {
                     if (tmpBitSet.get(j)) {
                         tmpBitSet.clear(j);
-                    }
-                    else {
+                    } else {
                         tmpBitSet.set(j);
                     }
                 }
@@ -199,7 +204,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
             this.m_Population.add(mutant);
         }
         if (best instanceof InterfaceGAIndividual) {
-            this.m_DifferenceThreshold = (int)(this.m_DivergenceRate* (1-this.m_DivergenceRate) * ((InterfaceGAIndividual)best).getGenotypeLength());
+            this.m_DifferenceThreshold = (int) (this.m_DivergenceRate * (1 - this.m_DivergenceRate) * ((InterfaceGAIndividual) best).getGenotypeLength());
 
         }
         this.evaluatePopulation(this.m_Population);
@@ -237,39 +242,46 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
-    @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
 
-    /** Something has changed
+    @Override
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -277,41 +289,42 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         String result = "";
         result += "CHC Adaptive Search Algorithm:\n";
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
 
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
 
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "This is an implementation of the CHC Adaptive Search Algorithm by Eselman.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -319,26 +332,30 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         return "CHC";
     }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Edit the properties of the population used.";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
 
 //    /** This method will set the normation method that is to be used.
@@ -353,21 +370,26 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
 //    public String normationMethodTipText() {
 //        return "Select the normation method.";
 //    }
-
-    /** Enable/disable elitism.
+    /**
+     * Enable/disable elitism.
+     *
      * @param elitism
      */
-    public void setElitism (boolean elitism) {
+    public void setElitism(boolean elitism) {
         this.m_UseElitism = elitism;
     }
+
     public boolean getElitism() {
         return this.m_UseElitism;
     }
+
     public String elitismTipText() {
         return "Enable/disable elitism.";
     }
 
-    /** The number of mating partners needed to create offsprings.
+    /**
+     * The number of mating partners needed to create offsprings.
+     *
      * @param partners
      */
     public void setNumberOfPartners(int partners) {
@@ -376,9 +398,11 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         }
         this.m_NumberOfPartners = partners;
     }
+
     public int getNumberOfPartners() {
         return this.m_NumberOfPartners;
     }
+
     public String numberOfPartnersTipText() {
         return "The number of mating partners needed to create offsprings.";
     }

src/eva2/server/go/strategies/ClusteringHillClimbing.java

@@ -16,287 +16,303 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.Pair;
 import java.io.Serializable;
 
-
 /**
- * The clustering hill climber is similar to a multi-start hill climber. In addition so optimizing
- * a set of individuals in parallel using a (1+1) strategy, the population is clustered in regular
- * intervals. If several individuals have gathered together in the sense that they are interpreted
- * as a cluster, only a subset of representatives of the cluster is taken over to the next HC step
- * while the rest is discarded. This means that the population size may be reduced.
+ * The clustering hill climber is similar to a multi-start hill climber. In
+ * addition so optimizing a set of individuals in parallel using a (1+1)
+ * strategy, the population is clustered in regular intervals. If several
+ * individuals have gathered together in the sense that they are interpreted as
+ * a cluster, only a subset of representatives of the cluster is taken over to
+ * the next HC step while the rest is discarded. This means that the population
+ * size may be reduced.
  *
- * As soon as the improvement by HC lies below a threshold, the mutation step size is decreased.
- * If the step size is decreased below a certain threshold, the current population is stored to 
- * an archive and reinitialized. Thus, the number of optima that may be found and returned by
- * getAllSolutions is higher than the population size.
+ * As soon as the improvement by HC lies below a threshold, the mutation step
+ * size is decreased. If the step size is decreased below a certain threshold,
+ * the current population is stored to an archive and reinitialized. Thus, the
+ * number of optima that may be found and returned by getAllSolutions is higher
+ * than the population size.
  *
  * @author mkron
  *
  */
 public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener,
-InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
-    transient private InterfacePopulationChangedEventListener   m_Listener;
+        InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
+
+    transient private InterfacePopulationChangedEventListener m_Listener;
     public static final boolean TRACE = false;
-    
-    transient private String 				m_Identifier = "";
-    private Population						m_Population = new Population();
-    private transient Population			archive = new Population();
-    private InterfaceOptimizationProblem	m_Problem	= new F1Problem();
-    private int								hcEvalCycle	= 1000;
-    private int								initialPopSize = 100;
-   	private int								loopCnt = 0;
+    transient private String m_Identifier = "";
+    private Population m_Population = new Population();
+    private transient Population archive = new Population();
+    private InterfaceOptimizationProblem m_Problem = new F1Problem();
+    private int hcEvalCycle = 1000;
+    private int initialPopSize = 100;
+    private int loopCnt = 0;
 //   	private int								baseEvalCnt = 0;
-   	private int								notifyGuiEvery		= 50;
-   	private double							sigmaClust 			= 0.01;
-   	private double							minImprovement 		= 0.000001;
-   	private double 							stepSizeThreshold 	= 0.000001;
-   	private double							initialStepSize 	= 0.1;
-   	// reduce the step size when there is hardy improvement. 
-   	private double							reduceFactor		= 0.2;
-   	private MutateESFixedStepSize				mutator = new MutateESFixedStepSize(0.1);
-	private PostProcessMethod localSearchMethod = PostProcessMethod.nelderMead;
-	private boolean doReinitialization = true;
+    private int notifyGuiEvery = 50;
+    private double sigmaClust = 0.01;
+    private double minImprovement = 0.000001;
+    private double stepSizeThreshold = 0.000001;
+    private double initialStepSize = 0.1;
+    // reduce the step size when there is hardy improvement. 
+    private double reduceFactor = 0.2;
+    private MutateESFixedStepSize mutator = new MutateESFixedStepSize(0.1);
+    private PostProcessMethod localSearchMethod = PostProcessMethod.nelderMead;
+    private boolean doReinitialization = true;
 
-	public ClusteringHillClimbing() {
-		hideHideable();
-	}
+    public ClusteringHillClimbing() {
+        hideHideable();
+    }
 
-	public ClusteringHillClimbing(int initialPopSize, PostProcessMethod lsMethod) {
-		this();
-		setInitialPopSize(initialPopSize);
-		setLocalSearchMethod(lsMethod);
-	}
+    public ClusteringHillClimbing(int initialPopSize, PostProcessMethod lsMethod) {
+        this();
+        setInitialPopSize(initialPopSize);
+        setLocalSearchMethod(lsMethod);
+    }
 
-	public ClusteringHillClimbing(ClusteringHillClimbing other) {
-		hideHideable();
-		m_Population = (Population)other.m_Population.clone();
-		m_Problem = (InterfaceOptimizationProblem)other.m_Problem.clone();
+    public ClusteringHillClimbing(ClusteringHillClimbing other) {
+        hideHideable();
+        m_Population = (Population) other.m_Population.clone();
+        m_Problem = (InterfaceOptimizationProblem) other.m_Problem.clone();
 
-		hcEvalCycle = other.hcEvalCycle;
-		initialPopSize = other.initialPopSize;
-		notifyGuiEvery = other.notifyGuiEvery;
-		sigmaClust = other.sigmaClust;
-		minImprovement = other.minImprovement;
-		stepSizeThreshold = other.stepSizeThreshold;
-		initialStepSize = other.initialStepSize;
-		reduceFactor = other.reduceFactor;
-		mutator = (MutateESFixedStepSize)other.mutator.clone();
-		loopCnt = 0;		
-	}
+        hcEvalCycle = other.hcEvalCycle;
+        initialPopSize = other.initialPopSize;
+        notifyGuiEvery = other.notifyGuiEvery;
+        sigmaClust = other.sigmaClust;
+        minImprovement = other.minImprovement;
+        stepSizeThreshold = other.stepSizeThreshold;
+        initialStepSize = other.initialStepSize;
+        reduceFactor = other.reduceFactor;
+        mutator = (MutateESFixedStepSize) other.mutator.clone();
+        loopCnt = 0;
+    }
 
     @Override
     public Object clone() {
         return (Object) new ClusteringHillClimbing(this);
     }
 
-	/**
-	 * Hide the population.
-	 */
-	public void hideHideable() {
-		GenericObjectEditor.setHideProperty(getClass(), "population", true);
-		setDoReinitialization(isDoReinitialization());
-		setLocalSearchMethod(getLocalSearchMethod());
-	}
+    /**
+     * Hide the population.
+     */
+    public void hideHideable() {
+        GenericObjectEditor.setHideProperty(getClass(), "population", true);
+        setDoReinitialization(isDoReinitialization());
+        setLocalSearchMethod(getLocalSearchMethod());
+    }
 
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-     /** This method will set the problem that is to be optimized
-      * @param problem
-      */
     @Override
-     public void setProblem (InterfaceOptimizationProblem problem) {
-         this.m_Problem = problem;
-     }
+    public String getIdentifier() {
+        return this.m_Identifier;
+    }
+
+    /**
+     * This method will set the problem that is to be optimized
+     *
+     * @param problem
+     */
     @Override
-     public InterfaceOptimizationProblem getProblem () {
-         return this.m_Problem;
-     }
+    public void setProblem(InterfaceOptimizationProblem problem) {
+        this.m_Problem = problem;
+    }
+
+    @Override
+    public InterfaceOptimizationProblem getProblem() {
+        return this.m_Problem;
+    }
 
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
     @Override
     public void init() {
-       	loopCnt = 0;
-       	mutator = new MutateESFixedStepSize(initialStepSize);
-       	archive = new Population();
-       	hideHideable();
-       	m_Population.setTargetSize(initialPopSize);
+        loopCnt = 0;
+        mutator = new MutateESFixedStepSize(initialStepSize);
+        archive = new Population();
+        hideHideable();
+        m_Population.setTargetSize(initialPopSize);
         this.m_Problem.initPopulation(this.m_Population);
         m_Population.addPopulationChangedEventListener(null); // noone will be notified directly on pop changes
         this.m_Problem.evaluate(this.m_Population);
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
-       	loopCnt = 0;
-        this.m_Population = (Population)pop.clone();
+        loopCnt = 0;
+        this.m_Population = (Population) pop.clone();
         m_Population.addPopulationChangedEventListener(null);
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
-    /** Something has changed
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
     @Override
-	public void optimize() {
-		double improvement;
+    public void optimize() {
+        double improvement;
 
-		loopCnt++;
-		m_Population.addPopulationChangedEventListener(this);
-		m_Population.setNotifyEvalInterval(notifyGuiEvery);
-		Pair<Population, Double> popD;
-		int funCallsBefore=m_Population.getFunctionCalls();
-		int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle); 
-		if (lastOverhead>0) {
-                evalsNow = (2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle));
+        loopCnt++;
+        m_Population.addPopulationChangedEventListener(this);
+        m_Population.setNotifyEvalInterval(notifyGuiEvery);
+        Pair<Population, Double> popD;
+        int funCallsBefore = m_Population.getFunctionCalls();
+        int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle);
+        if (lastOverhead > 0) {
+            evalsNow = (2 * hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle));
+        } else {
+            evalsNow = hcEvalCycle;
+        }
+        do {
+            if (TRACE) {
+                System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + evalsNow);
             }
-		else {
-                evalsNow = hcEvalCycle;
+            popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem) m_Problem, sigmaClust, evalsNow, 0.5, mutator);
+            //		(m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5);
+            if (popD.head().getFunctionCalls() == funCallsBefore) {
+                System.err.println("Bad case, increasing allowed evaluations!");
+                evalsNow = Math.max(evalsNow++, (int) (evalsNow * 1.2));
             }
-		do {
-			if (TRACE) {
-                        System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + evalsNow);
-                    }
-			popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, evalsNow, 0.5, mutator);
-			//		(m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5);
-			if (popD.head().getFunctionCalls()==funCallsBefore) {
-				System.err.println("Bad case, increasing allowed evaluations!");
-				evalsNow=Math.max(evalsNow++, (int)(evalsNow*1.2));
-			}
-		} while (popD.head().getFunctionCalls()==funCallsBefore);
-		improvement = popD.tail();
-		m_Population = popD.head();
-		if (TRACE) {
-                System.out.println("num inds after clusterLS: " + m_Population.size());
+        } while (popD.head().getFunctionCalls() == funCallsBefore);
+        improvement = popD.tail();
+        m_Population = popD.head();
+        if (TRACE) {
+            System.out.println("num inds after clusterLS: " + m_Population.size());
+        }
+
+        popD.head().setGenerationTo(m_Population.getGeneration() + 1);
+
+        if (doReinitialization && (improvement < minImprovement)) {
+            if (TRACE) {
+                System.out.println("improvement below " + minImprovement);
             }
+            if ((localSearchMethod != PostProcessMethod.hillClimber) || (mutator.getSigma() < stepSizeThreshold)) { // reinit!
+                // is performed for nm and cma, and if hc has too low sigma
+                if (TRACE) {
+                    System.out.println("REINIT!!");
+                }
 
-		popD.head().setGenerationTo(m_Population.getGeneration()+1);
+                if (localSearchMethod == PostProcessMethod.hillClimber) {
+                    mutator.setSigma(initialStepSize);
+                }
 
-		if (doReinitialization  && (improvement < minImprovement)) {
-			if (TRACE) {
-                        System.out.println("improvement below " + minImprovement);
-                    }
-			if ((localSearchMethod != PostProcessMethod.hillClimber) || (mutator.getSigma() < stepSizeThreshold)) { // reinit!
-				// is performed for nm and cma, and if hc has too low sigma
-				if (TRACE) {
-                                System.out.println("REINIT!!");
-                            }
+                // store results
+                archive.SetFunctionCalls(m_Population.getFunctionCalls());
+                archive.addPopulation(m_Population);
 
-				if (localSearchMethod == PostProcessMethod.hillClimber) {
-                                mutator.setSigma(initialStepSize);
-                            }
+                Population tmpPop = new Population();
+                tmpPop.addPopulationChangedEventListener(null);
+                tmpPop.setTargetSize(initialPopSize);
+                this.m_Problem.initPopulation(tmpPop);
+                tmpPop.setSameParams(m_Population);
+                tmpPop.setTargetSize(initialPopSize);
+                this.m_Problem.evaluate(tmpPop);
 
-				// store results
-				archive.SetFunctionCalls(m_Population.getFunctionCalls());
-				archive.addPopulation(m_Population);
+                // reset population while keeping function calls etc.
+                m_Population.clear();
+                m_Population.addPopulation(tmpPop);
+                m_Population.incrFunctionCallsBy(tmpPop.size());
 
-				Population tmpPop = new Population();
-				tmpPop.addPopulationChangedEventListener(null);
-				tmpPop.setTargetSize(initialPopSize);
-				this.m_Problem.initPopulation(tmpPop);
-				tmpPop.setSameParams(m_Population);
-				tmpPop.setTargetSize(initialPopSize);
-				this.m_Problem.evaluate(tmpPop);
-				
-				// reset population while keeping function calls etc.
-				m_Population.clear();
-				m_Population.addPopulation(tmpPop);
-				m_Population.incrFunctionCallsBy(tmpPop.size());
-		
-			} else  {  // decrease step size for hc
-				if (localSearchMethod != PostProcessMethod.hillClimber) {
-                                System.err.println("Invalid case in ClusteringHillClimbing!");
-                            }
-				mutator.setSigma(mutator.getSigma()*reduceFactor);
-				if (TRACE) {
-                                System.out.println("mutation stepsize reduced to " + mutator.getSigma());
-                            }
-			}
-		}
+            } else {  // decrease step size for hc
+                if (localSearchMethod != PostProcessMethod.hillClimber) {
+                    System.err.println("Invalid case in ClusteringHillClimbing!");
+                }
+                mutator.setSigma(mutator.getSigma() * reduceFactor);
+                if (TRACE) {
+                    System.out.println("mutation stepsize reduced to " + mutator.getSigma());
+                }
+            }
+        }
 //		System.out.println("funcalls: " + evalCnt);
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 
-	}
+    }
 
     @Override
-	public void registerPopulationStateChanged(Object source, String name) {
-		// The events of the interim hill climbing population will be caught here 
-		if (name.compareTo(Population.funCallIntervalReached) == 0) {
+    public void registerPopulationStateChanged(Object source, String name) {
+        // The events of the interim hill climbing population will be caught here 
+        if (name.compareTo(Population.funCallIntervalReached) == 0) {
 //			if ((((Population)source).size() % 50) > 0) {
 //				System.out.println("bla");
 //			}
-			// set funcalls to real value
-			m_Population.SetFunctionCalls(((Population)source).getFunctionCalls());
+            // set funcalls to real value
+            m_Population.SetFunctionCalls(((Population) source).getFunctionCalls());
 //			System.out.println("FunCallIntervalReached at " + (((Population)source).getFunctionCalls()));
-			this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-		} 
-		// do not react to NextGenerationPerformed
-		//else System.err.println("ERROR, event was " + name);
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+        }
+        // do not react to NextGenerationPerformed
+        //else System.err.println("ERROR, event was " + name);
 
-	}
+    }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Change the number of starting individuals stored (Cluster-HC).";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	Population tmp = new Population();
-    	tmp.addPopulation(archive);
-    	tmp.addPopulation(m_Population);
-    	tmp.SetFunctionCalls(m_Population.getFunctionCalls());
-    	tmp.setGenerationTo(m_Population.getGeneration());
+        Population tmp = new Population();
+        tmp.addPopulation(archive);
+        tmp.addPopulation(m_Population);
+        tmp.SetFunctionCalls(m_Population.getFunctionCalls());
+        tmp.setGenerationTo(m_Population.getGeneration());
 //    	tmp = PostProcessInterim.clusterBest(tmp, sigma, 0, PostProcessInterim.KEEP_LONERS, PostProcessInterim.BEST_ONLY);
-    	return new SolutionSet(m_Population, tmp);
+        return new SolutionSet(m_Population, tmp);
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -311,183 +327,180 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
     }
 
     @Override
-	public void freeWilly() {}
+    public String getName() {
+        return "ClustHC-" + initialPopSize + "-" + localSearchMethod;
+    }
+
+    public static String globalInfo() {
+        return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency."
+                + "If the local search step does not achieve a minimum improvement, the population may be reinitialized.";
+    }
+
+    /**
+     * @return the hcEvalCycle
+     */
+    public int getEvalCycle() {
+        return hcEvalCycle;
+    }
+
+    /**
+     * @param hcEvalCycle the hcEvalCycle to set
+     */
+    public void setEvalCycle(int hcEvalCycle) {
+        this.hcEvalCycle = hcEvalCycle;
+    }
+
+    public String evalCycleTipText() {
+        return "The number of evaluations between two clustering/adaption steps.";
+    }
+
+    /**
+     * @return the initialPopSize
+     */
+    public int getInitialPopSize() {
+        return initialPopSize;
+    }
+
+    /**
+     * @param initialPopSize the initialPopSize to set
+     */
+    public void setInitialPopSize(int initialPopSize) {
+        this.initialPopSize = initialPopSize;
+    }
+
+    public String initialPopSizeTipText() {
+        return "Population size at the start and at reinitialization times.";
+    }
+
+    /**
+     * @return the sigma
+     */
+    public double getSigmaClust() {
+        return sigmaClust;
+    }
+
+    /**
+     * @param sigma the sigma to set
+     */
+    public void setSigmaClust(double sigma) {
+        this.sigmaClust = sigma;
+    }
+
+    public String sigmaClustTipText() {
+        return "Defines the sigma distance parameter for density based clustering.";
+    }
+
+    /**
+     * @return the notifyGuiEvery
+     */
+    public int getNotifyGuiEvery() {
+        return notifyGuiEvery;
+    }
+
+    /**
+     * @param notifyGuiEvery the notifyGuiEvery to set
+     */
+    public void setNotifyGuiEvery(int notifyGuiEvery) {
+        this.notifyGuiEvery = notifyGuiEvery;
+    }
+
+    public String notifyGuiEveryTipText() {
+        return "How often to notify the GUI to plot the fitness etc.";
+    }
+
+    /**
+     * @return the minImprovement
+     */
+    public double getMinImprovement() {
+        return minImprovement;
+    }
+
+    /**
+     * @param minImprovement the minImprovement to set
+     */
+    public void setMinImprovement(double minImprovement) {
+        this.minImprovement = minImprovement;
+    }
+
+    public String minImprovementTipText() {
+        return "Improvement threshold below which the mutation step size is reduced or the population reinitialized.";
+    }
+
+    /**
+     * @return the reinitForStepSize
+     */
+    public double getStepSizeThreshold() {
+        return stepSizeThreshold;
+    }
+
+    /**
+     * @param reinitForStepSize the reinitForStepSize to set
+     */
+    public void setStepSizeThreshold(double reinitForStepSize) {
+        this.stepSizeThreshold = reinitForStepSize;
+    }
+
+    public String stepSizeThresholdTipText() {
+        return "Threshold for the mutation step size below which the population is seen as converged and reinitialized.";
+    }
+
+    /**
+     * @return the initialStepSize
+     */
+    public double getStepSizeInitial() {
+        return initialStepSize;
+    }
+
+    /**
+     * @param initialStepSize the initialStepSize to set
+     */
+    public void setStepSizeInitial(double initialStepSize) {
+        this.initialStepSize = initialStepSize;
+    }
+
+    public String stepSizeInitialTipText() {
+        return "Initial mutation step size for hill climbing, relative to the problem range.";
+    }
+
+    public PostProcessMethod getLocalSearchMethod() {
+        return localSearchMethod;
+    }
+
+    public void setLocalSearchMethod(PostProcessMethod localSearchMethod) {
+        this.localSearchMethod = localSearchMethod;
+        GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeInitial", localSearchMethod == PostProcessMethod.hillClimber);
+        GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeThreshold", localSearchMethod == PostProcessMethod.hillClimber);
+    }
+
+    public String localSearchMethodTipText() {
+        return "Set the method to be used for the hill climbing as local search";
+    }
 
     @Override
-	public String getName() {
-		return "ClustHC-"+initialPopSize+"-"+localSearchMethod;
-	}
-
-	public static String globalInfo() {
-		return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency." +
-				"If the local search step does not achieve a minimum improvement, the population may be reinitialized.";
-	}
-	
-	/**
-	 * @return the hcEvalCycle
-	 */
-	public int getEvalCycle() {
-		return hcEvalCycle;
-	}
-
-	/**
-	 * @param hcEvalCycle the hcEvalCycle to set
-	 */
-	public void setEvalCycle(int hcEvalCycle) {
-		this.hcEvalCycle = hcEvalCycle;
-	}
-	
-	public String evalCycleTipText() {
-		return "The number of evaluations between two clustering/adaption steps.";
-	}
-
-	/**
-	 * @return the initialPopSize
-	 */
-	public int getInitialPopSize() {
-		return initialPopSize;
-	}
-
-	/**
-	 * @param initialPopSize the initialPopSize to set
-	 */
-	public void setInitialPopSize(int initialPopSize) {
-		this.initialPopSize = initialPopSize;
-	}
-
-	public String initialPopSizeTipText() {
-		return "Population size at the start and at reinitialization times.";
-	}
-	
-	/**
-	 * @return the sigma
-	 */
-	public double getSigmaClust() {
-		return sigmaClust;
-	}
-
-	/**
-	 * @param sigma the sigma to set
-	 */
-	public void setSigmaClust(double sigma) {
-		this.sigmaClust = sigma;
-	}
-	
-	public String sigmaClustTipText() {
-		return "Defines the sigma distance parameter for density based clustering.";
-	}
-
-	/**
-	 * @return the notifyGuiEvery
-	 */
-	public int getNotifyGuiEvery() {
-		return notifyGuiEvery;
-	}
-
-	/**
-	 * @param notifyGuiEvery the notifyGuiEvery to set
-	 */
-	public void setNotifyGuiEvery(int notifyGuiEvery) {
-		this.notifyGuiEvery = notifyGuiEvery;
-	}
-
-	public String notifyGuiEveryTipText() {
-		return "How often to notify the GUI to plot the fitness etc.";
-	}
-	
-	/**
-	 * @return the minImprovement
-	 */
-	public double getMinImprovement() {
-		return minImprovement;
-	}
-
-	/**
-	 * @param minImprovement the minImprovement to set
-	 */
-	public void setMinImprovement(double minImprovement) {
-		this.minImprovement = minImprovement;
-	}
-
-	public String minImprovementTipText() {
-		return "Improvement threshold below which the mutation step size is reduced or the population reinitialized.";
-	}
-	
-	/**
-	 * @return the reinitForStepSize
-	 */
-	public double getStepSizeThreshold() {
-		return stepSizeThreshold;
-	}
-
-	/**
-	 * @param reinitForStepSize the reinitForStepSize to set
-	 */
-	public void setStepSizeThreshold(double reinitForStepSize) {
-		this.stepSizeThreshold = reinitForStepSize;
-	}
-
-	public String stepSizeThresholdTipText() {
-		return "Threshold for the mutation step size below which the population is seen as converged and reinitialized.";
-	}
-	
-	/**
-	 * @return the initialStepSize
-	 */
-	public double getStepSizeInitial() {
-		return initialStepSize;
-	}
-
-	/**
-	 * @param initialStepSize the initialStepSize to set
-	 */
-	public void setStepSizeInitial(double initialStepSize) {
-		this.initialStepSize = initialStepSize;
-	}
-	
-	public String stepSizeInitialTipText() {
-		return "Initial mutation step size for hill climbing, relative to the problem range.";
-	}
-
-	public PostProcessMethod getLocalSearchMethod() {
-		return localSearchMethod;
-	}
-
-	public void setLocalSearchMethod(PostProcessMethod localSearchMethod) {
-		this.localSearchMethod = localSearchMethod;
-		GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeInitial", localSearchMethod==PostProcessMethod.hillClimber);
-		GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeThreshold", localSearchMethod==PostProcessMethod.hillClimber);
-	}
-	
-	public String localSearchMethodTipText() {
-		return "Set the method to be used for the hill climbing as local search";
-	}
+    public String[] getAdditionalDataHeader() {
+        return new String[]{"numIndies", "sigma", "numArchived", "archivedMeanDist"};
+    }
 
     @Override
-	public String[] getAdditionalDataHeader() {
-		return new String[]{"numIndies", "sigma", "numArchived", "archivedMeanDist"};
-	}
+    public String[] getAdditionalDataInfo() {
+        return new String[]{"The current population size", "Current step size in case of stochastic HC", "Number of archived solutions", "Mean distance of archived solutions"};
+    }
 
     @Override
-	public String[] getAdditionalDataInfo() {
-		return new String[]{"The current population size", "Current step size in case of stochastic HC", "Number of archived solutions", "Mean distance of archived solutions"};
-	}
+    public Object[] getAdditionalDataValue(PopulationInterface pop) {
+        return new Object[]{m_Population.size(), mutator.getSigma(), archive.size(), archive.getPopulationMeasures()[0]};
+    }
 
-    @Override
-	public Object[] getAdditionalDataValue(PopulationInterface pop) {
-		return new Object[]{m_Population.size(), mutator.getSigma(), archive.size(), archive.getPopulationMeasures()[0]};
-	}
+    public boolean isDoReinitialization() {
+        return doReinitialization;
+    }
 
-	public boolean isDoReinitialization() {
-		return doReinitialization;
-	}
+    public void setDoReinitialization(boolean doReinitialization) {
+        this.doReinitialization = doReinitialization;
+        GenericObjectEditor.setShowProperty(this.getClass(), "minImprovement", doReinitialization);
+    }
 
-	public void setDoReinitialization(boolean doReinitialization) {
-		this.doReinitialization = doReinitialization;
-		GenericObjectEditor.setShowProperty(this.getClass(), "minImprovement", doReinitialization); 
-	}
-
-	public String doReinitializationTipText() {
-		return "Activate reinitialization if no improvement was achieved.";
-	}
+    public String doReinitializationTipText() {
+        return "Activate reinitialization if no improvement was achieved.";
+    }
 }

src/eva2/server/go/strategies/EsDpiNiching.java

@@ -284,6 +284,8 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
      * with the given parameters. If windowLen <= 0, the deactivation mechanism
      * is disabled. This provides for semi-sequential niching with DPI-ES
      *
+     *
+     *
 
      *
      * @param threshold
@@ -948,7 +950,8 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
     /**
      * Calculate the dynamic population size, which is the number of individuals
      * that are currently "alive" in the peak set. This must be implemented in
-     * analogy to {@link #collectPopulationIncGen(Population, EvolutionStrategies[], Population)}
+     * analogy to
+     * {@link #collectPopulationIncGen(Population, EvolutionStrategies[], Population)}
      *
      * @return
      */
@@ -1048,10 +1051,6 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
         }
     }
 
-    @Override
-    public void freeWilly() {
-    }
-
     @Override
     public InterfaceSolutionSet getAllSolutions() {
         Population peaks = new Population(peakOpts.length);

src/eva2/server/go/strategies/EvolutionStrategies.java

@@ -12,20 +12,20 @@ import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-/** Evolution strategies by Rechenberg and Schwefel, but please remember that
+/**
+ * Evolution strategies by Rechenberg and Schwefel, but please remember that
  * this only gives the generation strategy and not the coding. But this is the
- * only stategy that is able to utilize the 1/5 success rule mutation. Unfortunately,
- * there is a minor problem with the interpretation of the population size in constrast
- * to the parameters mu and lambda used by Rechenberg and Schwefel. Therefore, i'm
- * afraid that the interpretation of the population size may be subject to future
- * changes.
- * This is a implementation of Evolution Strategies.
- * Copyright:       Copyright (c) 2003
- * Company:         University of Tuebingen, Computer Architecture
- * @author          Felix Streichert
- * @version:  $Revision: 307 $
- *            $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
- *            $Author: mkron $
+ * only stategy that is able to utilize the 1/5 success rule mutation.
+ * Unfortunately, there is a minor problem with the interpretation of the
+ * population size in constrast to the parameters mu and lambda used by
+ * Rechenberg and Schwefel. Therefore, i'm afraid that the interpretation of the
+ * population size may be subject to future changes. This is a implementation of
+ * Evolution Strategies. Copyright: Copyright (c) 2003 Company: University of
+ * Tuebingen, Computer Architecture
+ *
+ * @author Felix Streichert
+ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
+ * 2007) $ $Author: mkron $
  */
 public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializable {
 
@@ -339,18 +339,9 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
         return this.identifier;
     }
 
-    /**
-     * This method is required to free the memory on a RMIServer, but there is
-     * nothing to implement.
-     */
-    @Override
-    public void freeWilly() {
-    }
-
     /**
      * These are for GUI
      */
-    
     /**
      * This method returns a global info string
      *

src/eva2/server/go/strategies/EvolutionaryProgramming.java

@@ -10,37 +10,36 @@ import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.F1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-/** Evolutionary programming by Fogel. Works fine but is actually a quite greedy local search
- * strategy solely based on mutation. To prevent any confusion, the mutation rate is temporaily
- * set to 1.0.
- * Potential citation: the PhD thesis of David B. Fogel (1992).
+/**
+ * Evolutionary programming by Fogel. Works fine but is actually a quite greedy
+ * local search strategy solely based on mutation. To prevent any confusion, the
+ * mutation rate is temporaily set to 1.0. Potential citation: the PhD thesis of
+ * David B. Fogel (1992).
  *
- * Copyright:       Copyright (c) 2003
- * Company:         University of Tuebingen, Computer Architecture
- * @author          Felix Streichert
- * @version:  $Revision: 307 $
- *            $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
- *            $Author: mkron $
+ * Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
+ * Architecture
+ *
+ * @author Felix Streichert
+ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
+ * 2007) $ $Author: mkron $
  */
-
 public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Serializable {
 
-    private int                             m_PopulationSize    = 0;
-    private Population                      m_Population        = new Population();
-    private InterfaceOptimizationProblem    m_Problem           = new F1Problem();
-    private InterfaceSelection              m_EnvironmentSelection = new SelectEPTournaments();
-
-    private String                m_Identifier = "";
+    private int m_PopulationSize = 0;
+    private Population m_Population = new Population();
+    private InterfaceOptimizationProblem m_Problem = new F1Problem();
+    private InterfaceSelection m_EnvironmentSelection = new SelectEPTournaments();
+    private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
 
     public EvolutionaryProgramming() {
     }
 
     public EvolutionaryProgramming(EvolutionaryProgramming a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_Identifier                   = a.m_Identifier;
-        this.m_EnvironmentSelection         = (InterfaceSelection)a.m_EnvironmentSelection.clone();
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_Identifier = a.m_Identifier;
+        this.m_EnvironmentSelection = (InterfaceSelection) a.m_EnvironmentSelection.clone();
     }
 
     @Override
@@ -49,109 +48,123 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
     }
 
     @Override
-        public void init() {
-            this.m_Problem.initPopulation(this.m_Population);
+    public void init() {
+        this.m_Problem.initPopulation(this.m_Population);
+        this.evaluatePopulation(this.m_Population);
+        this.m_PopulationSize = this.m_Population.size();
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+    }
+
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param reset If true the population is reset.
+     */
+    @Override
+    public void initByPopulation(Population pop, boolean reset) {
+        this.m_Population = (Population) pop.clone();
+        if (reset) {
+            this.m_Population.init();
             this.evaluatePopulation(this.m_Population);
-            this.m_PopulationSize = this.m_Population.size();
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
+    }
 
-        /** This method will init the optimizer with a given population
-         * @param reset     If true the population is reset.
-         */
-    @Override
-        public void initByPopulation(Population pop, boolean reset) {
-            this.m_Population = (Population)pop.clone();
-            if (reset) {
-            	this.m_Population.init();
-                this.evaluatePopulation(this.m_Population);
-                this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-            }
-        }
-
-        /** This method will evaluate the current population using the
-         * given problem.
-         * @param population The population that is to be evaluated
-         */
-        private void evaluatePopulation(Population population) {
-            this.m_Problem.evaluate(population);
-            population.incrGeneration();
-        }
-
-        /** This method will generate the offspring population from the
-         * given population of evaluated individuals.
-         */
-        private Population generateChildren() {
-            Population                 result = (Population)this.m_Population.cloneWithoutInds();
-            AbstractEAIndividual       mutant;
-
-            result.clear();
-            for (int i = 0; i < this.m_Population.size(); i++) {
-                mutant = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Population.get(i)).clone();
-                double tmpD = mutant.getMutationProbability();
-                mutant.setMutationProbability(1.0);
-                mutant.mutate();
-                mutant.setMutationProbability(tmpD);
-                result.add(mutant);
-            }
-            return result;
+    /**
+     * This method will evaluate the current population using the given problem.
+     *
+     * @param population The population that is to be evaluated
+     */
+    private void evaluatePopulation(Population population) {
+        this.m_Problem.evaluate(population);
+        population.incrGeneration();
+    }
+
+    /**
+     * This method will generate the offspring population from the given
+     * population of evaluated individuals.
+     */
+    private Population generateChildren() {
+        Population result = (Population) this.m_Population.cloneWithoutInds();
+        AbstractEAIndividual mutant;
+
+        result.clear();
+        for (int i = 0; i < this.m_Population.size(); i++) {
+            mutant = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(i)).clone();
+            double tmpD = mutant.getMutationProbability();
+            mutant.setMutationProbability(1.0);
+            mutant.mutate();
+            mutant.setMutationProbability(tmpD);
+            result.add(mutant);
         }
+        return result;
+    }
 
     @Override
-        public void optimize() {
-            Population nextGeneration, parents;
+    public void optimize() {
+        Population nextGeneration, parents;
 
-            this.m_EnvironmentSelection.prepareSelection(this.m_Population);
-            parents = this.m_EnvironmentSelection.selectFrom(this.m_Population, this.m_PopulationSize);
-            this.m_Population.clear();
-            this.m_Population.addPopulation(parents);
-            nextGeneration = this.generateChildren();
-            this.evaluatePopulation(nextGeneration);
-            nextGeneration.addPopulation(this.m_Population);
-            this.m_Population = nextGeneration;
+        this.m_EnvironmentSelection.prepareSelection(this.m_Population);
+        parents = this.m_EnvironmentSelection.selectFrom(this.m_Population, this.m_PopulationSize);
+        this.m_Population.clear();
+        this.m_Population.addPopulation(parents);
+        nextGeneration = this.generateChildren();
+        this.evaluatePopulation(nextGeneration);
+        nextGeneration.addPopulation(this.m_Population);
+        this.m_Population = nextGeneration;
 
-            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-        }
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+    }
 
-    /** This method allows you to add the LectureGUI as listener to the Optimizer
+    /**
+     * This method allows you to add the LectureGUI as listener to the Optimizer
+     *
      * @param ea
      */
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    /** Something has changed
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -159,39 +172,42 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
         String result = "";
         result += "Evolutionary Programming:\n";
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
     public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
+
     @Override
     public String getIdentifier() {
         return this.m_Identifier;
     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
-    @Override
-    public void freeWilly() {
-
-    }
-    /**********************************************************************************************************************
+    /**
+     * ********************************************************************************************************************
      * These are for GUI
      */
-    /** This method returns a global info string
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "This is a basic Evolutionary Programming scheme.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -199,36 +215,45 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
         return "EP";
     }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Edit the properties of the population used.";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
-    /** Choose a method for selecting the reduced population.
+
+    /**
+     * Choose a method for selecting the reduced population.
+     *
      * @param selection
      */
     public void setEnvironmentSelection(InterfaceSelection selection) {
         this.m_EnvironmentSelection = selection;
     }
+
     public InterfaceSelection getEnvironmentSelection() {
         return this.m_EnvironmentSelection;
     }
+
     public String environmentSelectionTipText() {
         return "Choose a method for selecting the reduced population.";
     }

src/eva2/server/go/strategies/FloodAlgorithm.java

@@ -9,33 +9,31 @@ import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-/** The flood algorithm, and alternative to the threshold algorithms. No really
- * good but commonly known and sometimes even used. Here the problem is to choose
- * the initial flood peak and the drain rate such that it fits the current optimization
- * problem. But again this is a greedy local search strategy. Similar to the
- * evolutionary programming strategy this strategy sets the mutation rate temporarily
- * to 1.0.
- * The algorithm regards only one-dimensional fitness.
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 01.10.2004
- * Time: 13:46:02
- * To change this template use File | Settings | File Templates.
+/**
+ * The flood algorithm, and alternative to the threshold algorithms. No really
+ * good but commonly known and sometimes even used. Here the problem is to
+ * choose the initial flood peak and the drain rate such that it fits the
+ * current optimization problem. But again this is a greedy local search
+ * strategy. Similar to the evolutionary programming strategy this strategy sets
+ * the mutation rate temporarily to 1.0. The algorithm regards only
+ * one-dimensional fitness. Created by IntelliJ IDEA. User: streiche Date:
+ * 01.10.2004 Time: 13:46:02 To change this template use File | Settings | File
+ * Templates.
  */
 public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable {
     // These variables are necessary for the simple testcase
-    private InterfaceOptimizationProblem            m_Problem           = new B1Problem();
-    private int             m_MultiRuns             = 100;
-    private int             m_FitnessCalls          = 100;
-    private int             m_FitnessCallsNeeded    = 0;
-    GAIndividualBinaryData  m_Best, m_Test;
-    public double           m_InitialFloodPeak      = 2000.0, m_CurrentFloodPeak;
-    public double           m_DrainRate             = 1.0;
 
+    private InterfaceOptimizationProblem m_Problem = new B1Problem();
+    private int m_MultiRuns = 100;
+    private int m_FitnessCalls = 100;
+    private int m_FitnessCallsNeeded = 0;
+    GAIndividualBinaryData m_Best, m_Test;
+    public double m_InitialFloodPeak = 2000.0, m_CurrentFloodPeak;
+    public double m_DrainRate = 1.0;
     // These variables are necessary for the more complex LectureGUI enviroment
-    transient private String                m_Identifier = "";
+    transient private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
-    private Population      m_Population;
+    private Population m_Population;
 
     public FloodAlgorithm() {
         this.m_Population = new Population();
@@ -43,10 +41,10 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
     }
 
     public FloodAlgorithm(FloodAlgorithm a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_InitialFloodPeak             = a.m_InitialFloodPeak;
-        this.m_DrainRate                    = a.m_DrainRate;
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_InitialFloodPeak = a.m_InitialFloodPeak;
+        this.m_DrainRate = a.m_DrainRate;
     }
 
     @Override
@@ -54,7 +52,8 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         return (Object) new FloodAlgorithm(this);
     }
 
-    /** This method will init the HillClimber
+    /**
+     * This method will init the HillClimber
      */
     @Override
     public void init() {
@@ -64,27 +63,30 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will init the optimizer with a given population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
-        this.m_Population = (Population)pop.clone();
+        this.m_Population = (Population) pop.clone();
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
         this.m_CurrentFloodPeak = this.m_InitialFloodPeak;
     }
 
-    /** This method will optimize
+    /**
+     * This method will optimize
      */
     @Override
     public void optimize() {
-        AbstractEAIndividual    indy;
-        Population              original = (Population)this.m_Population.clone();
-        double[]                fitness;
+        AbstractEAIndividual indy;
+        Population original = (Population) this.m_Population.clone();
+        double[] fitness;
 
         for (int i = 0; i < this.m_Population.size(); i++) {
             indy = ((AbstractEAIndividual) this.m_Population.get(i));
@@ -95,7 +97,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         }
         this.m_Problem.evaluate(this.m_Population);
         for (int i = 0; i < this.m_Population.size(); i++) {
-            fitness = ((AbstractEAIndividual)this.m_Population.get(i)).getFitness();
+            fitness = ((AbstractEAIndividual) this.m_Population.get(i)).getFitness();
             if (fitness[0] > this.m_CurrentFloodPeak) {
                 this.m_Population.remove(i);
                 this.m_Population.add(i, original.get(i));
@@ -106,13 +108,15 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method calculates the difference between the fitness values
-     * @param org     The original
-     * @param mut     The mutant
+    /**
+     * This method calculates the difference between the fitness values
+     *
+     * @param org The original
+     * @param mut The mutant
      */
     private double calculateDelta(AbstractEAIndividual org, AbstractEAIndividual mut) {
-        double      result = 0;
-        double[]    fitOrg, fitMut;
+        double result = 0;
+        double[] fitOrg, fitMut;
         fitOrg = org.getFitness();
         fitMut = mut.getFitness();
         for (int i = 0; i < fitOrg.length; i++) {
@@ -121,19 +125,23 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         return result;
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will init the HillClimber
+    /**
+     * This method will init the HillClimber
      */
     public void defaultInit() {
         this.m_FitnessCallsNeeded = 0;
@@ -141,24 +149,26 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         this.m_Best.defaultInit(m_Problem);
     }
 
-    /** This method will optimize
+    /**
+     * This method will optimize
      */
     public void defaultOptimize() {
         for (int i = 0; i < m_FitnessCalls; i++) {
-            this.m_Test = (GAIndividualBinaryData)((this.m_Best).clone());
+            this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone());
             this.m_Test.defaultMutate();
             if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) {
                 this.m_Best = this.m_Test;
             }
             this.m_FitnessCallsNeeded = i;
             if (this.m_Best.defaultEvaulateAsMiniBits() == 0) {
-                i = this.m_FitnessCalls +1;
+                i = this.m_FitnessCalls + 1;
             }
         }
     }
 
-    /** This main method will start a simple hillclimber.
-     * No arguments necessary.
+    /**
+     * This main method will start a simple hillclimber. No arguments necessary.
+     *
      * @param args
      */
     public static void main(String[] args) {
@@ -172,36 +182,43 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         }
         TmpMeanCalls /= program.m_MultiRuns;
         TmpMeanFitness /= program.m_MultiRuns;
-        System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
+        System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
     }
 
-    /** This method allows you to add the LectureGUI as listener to the Optimizer
+    /**
+     * This method allows you to add the LectureGUI as listener to the Optimizer
+     *
      * @param ea
      */
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    /** Something has changed
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -209,44 +226,46 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         String result = "";
         if (this.m_Population.size() > 1) {
             result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
-        }
-        else {
+        } else {
             result += "Simulated Annealing:\n";
         }
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "The flood algorithm uses an declining flood peak to accpect new solutions (*shudder* check inital flood peak and drain very carefully!).";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -254,55 +273,67 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         return "MS-FA";
     }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Change the number of best individuals stored (MS-FA).";
     }
 
-    
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
-    /** This methods allow you to set/get the temperatur of the flood
-     * algorithm procedure
+
+    /**
+     * This methods allow you to set/get the temperatur of the flood algorithm
+     * procedure
+     *
      * @return The initial flood level.
      */
     public double getInitialFloodPeak() {
         return this.m_InitialFloodPeak;
     }
-    public void setInitialFloodPeak(double pop){
+
+    public void setInitialFloodPeak(double pop) {
         this.m_InitialFloodPeak = pop;
     }
+
     public String initialFloodPeakTipText() {
         return "Set the initial flood peak.";
     }
 
-    /** This methods allow you to set/get the drain rate of the flood
-     * algorithm procedure
+    /**
+     * This methods allow you to set/get the drain rate of the flood algorithm
+     * procedure
+     *
      * @return The drain rate.
      */
     public double getDrainRate() {
         return this.m_DrainRate;
     }
-    public void setDrainRate(double a){
+
+    public void setDrainRate(double a) {
         this.m_DrainRate = a;
         if (this.m_DrainRate < 0) {
             this.m_DrainRate = 0.0;
         }
     }
+
     public String drainRateTipText() {
         return "Set the drain rate that reduces the current flood level each generation.";
     }

src/eva2/server/go/strategies/GeneticAlgorithm.java

@@ -241,14 +241,6 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
         return this.identifier;
     }
 
-    /**
-     * This method is required to free the memory on a RMIServer, but there is
-     * nothing to implement.
-     */
-    @Override
-    public void freeWilly() {
-    }
-
     /**
      * ********************************************************************************************************************
      * These are for GUI

src/eva2/server/go/strategies/HillClimbing.java

@@ -9,31 +9,29 @@ import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-
-/** This is a Multi-Start Hill-Climber, here the population size gives the number of
- * multi-starts. Similar to the evolutionary programming strategy this strategy sets the 
- * mutation rate temporarily to 1.0.
- * Copyright:       Copyright (c) 2003
- * Company:         University of Tuebingen, Computer Architecture
- * @author          Felix Streichert
- * @version:  $Revision: 307 $
- *            $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
- *            $Author: mkron $
+/**
+ * This is a Multi-Start Hill-Climber, here the population size gives the number
+ * of multi-starts. Similar to the evolutionary programming strategy this
+ * strategy sets the mutation rate temporarily to 1.0. Copyright: Copyright (c)
+ * 2003 Company: University of Tuebingen, Computer Architecture
+ *
+ * @author Felix Streichert
+ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
+ * 2007) $ $Author: mkron $
  */
-
 public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
     // These variables are necessary for the simple testcase
-    private InterfaceOptimizationProblem            m_Problem           = new B1Problem();
-    private InterfaceMutation 						mutator = null;
+
+    private InterfaceOptimizationProblem m_Problem = new B1Problem();
+    private InterfaceMutation mutator = null;
 //    private int             m_MultiRuns             = 100;
 //    private int             m_FitnessCalls          = 100;
 //    private int             m_FitnessCallsNeeded    = 0;
 //    GAIndividualBinaryData  m_Best, m_Test;
-
     // These variables are necessary for the more complex LectureGUI enviroment
-    transient private String                m_Identifier = "";
-    transient private InterfacePopulationChangedEventListener   m_Listener;
-    private Population                                          m_Population;
+    transient private String m_Identifier = "";
+    transient private InterfacePopulationChangedEventListener m_Listener;
+    private Population m_Population;
 
     public HillClimbing() {
         this.m_Population = new Population();
@@ -41,8 +39,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
     }
 
     public HillClimbing(HillClimbing a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
     }
 
     @Override
@@ -50,7 +48,8 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
         return (Object) new HillClimbing(this);
     }
 
-    /** This method will init the HillClimber
+    /**
+     * This method will init the HillClimber
      */
     @Override
     public void init() {
@@ -61,20 +60,21 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
 
     @Override
     public void initByPopulation(Population pop, boolean reset) {
-        this.m_Population = (Population)pop.clone();
+        this.m_Population = (Population) pop.clone();
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
-    /** This method will optimize
+    /**
+     * This method will optimize
      */
     @Override
     public void optimize() {
-        AbstractEAIndividual   indy;
-        Population original = (Population)this.m_Population.clone();
+        AbstractEAIndividual indy;
+        Population original = (Population) this.m_Population.clone();
         double tmpD;
         InterfaceMutation tmpMut;
 
@@ -84,20 +84,19 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
             indy.setMutationProbability(1.0);
             if (mutator == null) {
                 indy.mutate();
-            }
-            else {
+            } else {
                 mutator.mutate(indy);
             }
             indy.setMutationProbability(tmpD);
         }
         this.m_Problem.evaluate(this.m_Population);
         for (int i = 0; i < this.m_Population.size(); i++) {
-            if (((AbstractEAIndividual)original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual)this.m_Population.get(i)))) {
+            if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.m_Population.get(i)))) {
 //                this.m_Population.remove(i); 
                 // throw away mutated one and replace by old one 
                 this.m_Population.set(i, original.get(i));
             } else {
-            	// else: mutation improved the individual, so leave the new one
+                // else: mutation improved the individual, so leave the new one
             }
         }
         this.m_Population.incrGeneration();
@@ -119,28 +118,32 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
     }
 
     public InterfaceMutation getMutationOperator() {
-    	return mutator;
+        return mutator;
     }
 
     /**
-     * Allows to set a desired mutator by hand, which is used instead of the one in the individuals.
-     * Set it to null to use the one in the individuals, which is the default.
+     * Allows to set a desired mutator by hand, which is used instead of the one
+     * in the individuals. Set it to null to use the one in the individuals,
+     * which is the default.
      *
      * @param mute
      */
     public void SetMutationOperator(InterfaceMutation mute) {
-    	mutator = mute;
+        mutator = mute;
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
@@ -163,7 +166,6 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
 //            if (this.m_Best.defaultEvaulateAsMiniBits() == 0) i = this.m_FitnessCalls +1;
 //        }
 //    }
-
 //    /** This main method will start a simple hillclimber.
 //     * No arguments necessary.
 //     * @param args
@@ -181,34 +183,40 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
 //        TmpMeanFitness = TmpMeanFitness/program.m_MultiRuns;
 //        System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
 //    }
-
-    /** This method allows you to add the LectureGUI as listener to the Optimizer
+    /**
+     * This method allows you to add the LectureGUI as listener to the Optimizer
+     *
      * @param ea
      */
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    /** Something has changed
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -216,63 +224,68 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
         String result = "";
         if (this.m_Population.size() > 1) {
             result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
-        }
-        else {
+        } else {
             result += "Hill Climbing:\n";
         }
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "The Hill Climber uses the default EA mutation and initializing operators. If the population size is bigger than one a multi-start Hill Climber is performed.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
     public String getName() {
-        return "MS-HC"+getIdentifier();
+        return "MS-HC" + getIdentifier();
     }
+
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
+
     public String populationTipText() {
         return "Change the number of best individuals stored (MS-HC).";
     }

src/eva2/server/go/strategies/InterfaceOptimizer.java

@@ -22,29 +22,29 @@ public interface InterfaceOptimizer {
     /** This method will return deep clone of the optimizer
      * @return The clone
      */
-    public Object clone();
+    Object clone();
 
     /** This method will return a naming String
      * @return The name of the algorithm
      */
-    public String getName();
+    String getName();
 
     /** 
      * This method allows you to add a listener to the Optimizer.
      * @param ea
      */
-    public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
+    void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
     
     /** 
      * This method removes a listener from the Optimizer. It returns true on success,
      * false if the listener could not be found.
      * @param ea
      */
-    public boolean removePopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
+    boolean removePopulationChangedEventListener(InterfacePopulationChangedEventListener ea);
     
     /** This method will init the optimizer
      */
-    public void init();
+    void init();
 
     /** 
      * This method will init the optimizer with a given population.
@@ -52,22 +52,22 @@ public interface InterfaceOptimizer {
      * @param pop       The initial population
      * @param reset     If true the population is reinitialized and reevaluated.
      */
-    public void initByPopulation(Population pop, boolean reset);
+    void initByPopulation(Population pop, boolean reset);
 
     /** This method will optimize for a single iteration, after this step
      * the population should be as big as possible (ie. the size of lambda
      * and not mu) and all individual should be evaluated. This allows more
      * usefull statistics on the population.
      */
-    public void optimize();
+    void optimize();
 
     /** Assuming that all optimizer will store their data in a population
      * we will allow access to this population to query to current state
      * of the optimizer.
      * @return The population of current solutions to a given problem.
      */
-    public Population getPopulation();
-    public void setPopulation(Population pop);
+    Population getPopulation();
+    void setPopulation(Population pop);
     
     /**
      * Return all found solutions (local optima) if they are not contained in the current population. Be
@@ -78,14 +78,14 @@ public interface InterfaceOptimizer {
      * 
      * @return A solution set of the current population and possibly earlier solutions.
      */
-    public InterfaceSolutionSet getAllSolutions();
+    InterfaceSolutionSet getAllSolutions();
     
     /** 
      * This method allows you to set an identifier for the algorithm
      * @param name      The identifier
      */
-     public void setIdentifier(String name);
-     public String getIdentifier();
+    void setIdentifier(String name);
+    String getIdentifier();
 
     /**
      * This method will set the problem that is to be optimized. The problem
@@ -93,17 +93,12 @@ public interface InterfaceOptimizer {
      *
      * @param problem
      */
-    public void setProblem (InterfaceOptimizationProblem problem);
-    public InterfaceOptimizationProblem getProblem ();
+    void setProblem (InterfaceOptimizationProblem problem);
+    InterfaceOptimizationProblem getProblem ();
 
     /** This method will return a string describing all properties of the optimizer
      * and the applied methods.
      * @return A descriptive string
      */
-    public String getStringRepresentation();
-
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
-    public void freeWilly();
+    String getStringRepresentation();
 }

src/eva2/server/go/strategies/IslandModelEA.java

@@ -17,64 +17,59 @@ import eva2.server.go.problems.F8Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.server.go.problems.TF1Problem;
 
-/** The one and only island model for parallelization. Since parallelization based
- * on the RMIProxyRemoteThread is on the one hand much slower than benchmark function
- * evaluation and on the other hand the GUI based distribution scheme is rather prone
- * to config errors (the correct ssh version is required, the jar needs to be in
- * the working dir and possible problem data must be on the servers to) an implicit
- * island-model has been implemented too to allow fast and reliable computation.
- * This is still usefull, since it is less prone to premature convergence and also
- * an heterogenuous island model can be used.
+/**
+ * The one and only island model for parallelization. Since parallelization
+ * based on the RMIProxyRemoteThread is on the one hand much slower than
+ * benchmark function evaluation and on the other hand the GUI based
+ * distribution scheme is rather prone to config errors (the correct ssh version
+ * is required, the jar needs to be in the working dir and possible problem data
+ * must be on the servers to) an implicit island-model has been implemented too
+ * to allow fast and reliable computation. This is still usefull, since it is
+ * less prone to premature convergence and also an heterogenuous island model
+ * can be used.
  *
- * A population of the same size is sent to all nodes and evaluated there independently 
- * for a cycle (more precisely: for MigrationRate generations) after which a communication
- * step is performed according to the migration model. Only after migration is a main
- * cycle complete, the statistics updated etc.
+ * A population of the same size is sent to all nodes and evaluated there
+ * independently for a cycle (more precisely: for MigrationRate generations)
+ * after which a communication step is performed according to the migration
+ * model. Only after migration is a main cycle complete, the statistics updated
+ * etc.
  *
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 12.09.2004
- * Time: 14:48:20
- * To change this template use File | Settings | File Templates.
+ * Created by IntelliJ IDEA. User: streiche Date: 12.09.2004 Time: 14:48:20 To
+ * change this template use File | Settings | File Templates.
  */
-
 public class IslandModelEA implements InterfacePopulationChangedEventListener, InterfaceOptimizer, java.io.Serializable {
 
-    private Population                              m_Population        = new Population();
-    private InterfaceOptimizer                      m_Optimizer         = new GeneticAlgorithm();
-    private InterfaceMigration                      m_Migration         = new SOBestMigration();
-    private InterfaceOptimizationProblem            m_Problem           = new F8Problem();
+    private Population m_Population = new Population();
+    private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm();
+    private InterfaceMigration m_Migration = new SOBestMigration();
+    private InterfaceOptimizationProblem m_Problem = new F8Problem();
 //    private String[]                                m_NodesList;
-    private int                                     m_MigrationRate     = 10;
-    private boolean                                 m_HeterogenuousProblems = false;
-
+    private int m_MigrationRate = 10;
+    private boolean m_HeterogenuousProblems = false;
     // These are the processor to run on
-    private int                                                 m_numLocalCPUs         = 1;
-    private boolean                                             m_localOnly       = false;
-    transient private InterfaceOptimizer[]                      m_Islands;
- 
+    private int m_numLocalCPUs = 1;
+    private boolean m_localOnly = false;
+    transient private InterfaceOptimizer[] m_Islands;
     // This is for debugging
-    private boolean                                             m_LogLocalChanges   = true;
-    private boolean                                             m_Show              = false;
-    transient private Plot                                      m_Plot              = null;
-
-    transient private String                                    m_Identifier        = "";
-    transient private InterfacePopulationChangedEventListener   m_Listener;
-	transient private final boolean TRACE = false;
-
+    private boolean m_LogLocalChanges = true;
+    private boolean m_Show = false;
+    transient private Plot m_Plot = null;
+    transient private String m_Identifier = "";
+    transient private InterfacePopulationChangedEventListener m_Listener;
+    transient private final boolean TRACE = false;
 
     public IslandModelEA() {
     }
 
     public IslandModelEA(IslandModelEA a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_Optimizer                    = (InterfaceOptimizer)a.m_Optimizer.clone();
-        this.m_Migration                    = (InterfaceMigration)a.m_Migration.clone();
-        this.m_MigrationRate				= a.m_MigrationRate;
-        this.m_HeterogenuousProblems		= a.m_HeterogenuousProblems;
-        this.m_numLocalCPUs                    = a.m_numLocalCPUs;
-        this.m_localOnly                  = a.m_localOnly;
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_Optimizer = (InterfaceOptimizer) a.m_Optimizer.clone();
+        this.m_Migration = (InterfaceMigration) a.m_Migration.clone();
+        this.m_MigrationRate = a.m_MigrationRate;
+        this.m_HeterogenuousProblems = a.m_HeterogenuousProblems;
+        this.m_numLocalCPUs = a.m_numLocalCPUs;
+        this.m_localOnly = a.m_localOnly;
     }
 
     @Override
@@ -98,14 +93,14 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         this.m_Population.init();
         this.m_Optimizer.init();
         this.m_Optimizer.setProblem(this.m_Problem);
-        this.m_Optimizer.setPopulation((Population)m_Population.clone());
+        this.m_Optimizer.setPopulation((Population) m_Population.clone());
         InterfacePopulationChangedEventListener myLocal = null;
         if (this.m_localOnly) {
             // this is running on the local machine
             this.m_Islands = new InterfaceOptimizer[this.m_numLocalCPUs];
             for (int i = 0; i < this.m_numLocalCPUs; i++) {
                 this.m_Islands[i] = (InterfaceOptimizer) this.m_Optimizer.clone();
-                this.m_Islands[i].setIdentifier(""+i);
+                this.m_Islands[i].setIdentifier("" + i);
                 this.m_Islands[i].init();
                 if (this.m_LogLocalChanges) {
                     this.m_Islands[i].addPopulationChangedEventListener(this);
@@ -115,49 +110,51 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
             // this is running on remote machines
             // ToDo: Parallelize with Threads?!?
             /*if (this.m_LocalServer == null) {
-                this.m_LocalServer = RMIServer.getInstance();
-            }
-            try {
-                myLocal = (InterfacePopulationChangedEventListener) RMIProxyLocal.newInstance(this);
-            } catch(Exception e) {
-                System.err.println("Island Model EA warning on local RMIServer... but i'll start anyway!");
-            }
-            String[] nodesList = this.m_Servers.getCheckedServerNodes();
-            if ((nodesList == null) || (nodesList.length == 0)) {
-            	throw new RuntimeException("Error, no active remote servers available! Activate servers or use localOnly mode.");
-            }
-            this.m_Islands = new InterfaceOptimizer[nodesList.length];
-            for (int i = 0; i < nodesList.length; i++) {
-                this.m_Islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.m_Optimizer, nodesList[i]);
-                this.m_Islands[i].setIdentifier(""+i);
-                this.m_Islands[i].init();
-                if (this.m_LogLocalChanges) {
-                    this.m_Islands[i].addPopulationChangedEventListener(myLocal);
-                }
-            }*/
+             this.m_LocalServer = RMIServer.getInstance();
+             }
+             try {
+             myLocal = (InterfacePopulationChangedEventListener) RMIProxyLocal.newInstance(this);
+             } catch(Exception e) {
+             System.err.println("Island Model EA warning on local RMIServer... but i'll start anyway!");
+             }
+             String[] nodesList = this.m_Servers.getCheckedServerNodes();
+             if ((nodesList == null) || (nodesList.length == 0)) {
+             throw new RuntimeException("Error, no active remote servers available! Activate servers or use localOnly mode.");
+             }
+             this.m_Islands = new InterfaceOptimizer[nodesList.length];
+             for (int i = 0; i < nodesList.length; i++) {
+             this.m_Islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.m_Optimizer, nodesList[i]);
+             this.m_Islands[i].setIdentifier(""+i);
+             this.m_Islands[i].init();
+             if (this.m_LogLocalChanges) {
+             this.m_Islands[i].addPopulationChangedEventListener(myLocal);
+             }
+             }*/
         }
 
         this.m_Migration.initMigration(this.m_Islands);
         Population pop;
-   		this.m_Population.incrGeneration(); // the island-initialization has increased the island-pop generations. 
+        this.m_Population.incrGeneration(); // the island-initialization has increased the island-pop generations. 
 
         for (int i = 0; i < this.m_Islands.length; i++) {
-            pop = (Population)this.m_Islands[i].getPopulation().clone();
+            pop = (Population) this.m_Islands[i].getPopulation().clone();
             this.m_Population.addPopulation(pop);
             this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
-            if (m_Islands[i].getPopulation().getGeneration()!=m_Population.getGeneration()) {
-            	System.err.println("Error, inconsistent generations!");
+            if (m_Islands[i].getPopulation().getGeneration() != m_Population.getGeneration()) {
+                System.err.println("Error, inconsistent generations!");
             }
         }
         this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
     }
 
-    /** This method will init the optimizer with a given population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population tpop, boolean reset) {
-    	// TODO this is again evil copy&paste style
+        // TODO this is again evil copy&paste style
         if (this.m_Show) {
             if (this.m_Plot == null) {
                 double[] tmpD = new double[2];
@@ -167,9 +164,9 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
             }
         }
 
-        this.m_Population = (Population)tpop.clone();
+        this.m_Population = (Population) tpop.clone();
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.m_Population.incrGeneration();
         }
         this.m_Optimizer.init();
@@ -180,7 +177,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
             this.m_Islands = new InterfaceOptimizer[this.m_numLocalCPUs];
             for (int i = 0; i < this.m_numLocalCPUs; i++) {
                 this.m_Islands[i] = (InterfaceOptimizer) this.m_Optimizer.clone();
-                this.m_Islands[i].setIdentifier(""+i);
+                this.m_Islands[i].setIdentifier("" + i);
                 this.m_Islands[i].init();
                 if (this.m_LogLocalChanges) {
                     this.m_Islands[i].addPopulationChangedEventListener(this);
@@ -190,47 +187,48 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
             // this is running on remote machines
             // ToDo: Parallellize with threads?!?
             /*
-            if (this.m_LocalServer == null) {
-                this.m_LocalServer = RMIServer.getInstance();
-            }
-            try {
-                myLocal = (InterfacePopulationChangedEventListener) RMIProxyLocal.newInstance(this);
-            } catch(Exception e) {
-                System.err.println("Island Model EA warning on local RMIServer... but i'll try to start anyway!");
-            }
-            String[] nodesList = this.m_Servers.getCheckedServerNodes();
-            if ((nodesList == null) || (nodesList.length == 0)) {
-                return;
-            }
-            this.m_Islands = new InterfaceOptimizer[nodesList.length];
-            for (int i = 0; i < nodesList.length; i++) {
-                this.m_Islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.m_Optimizer, nodesList[i]);
-                this.m_Islands[i].setIdentifier(""+i);
-                this.m_Islands[i].init();
-                if (this.m_LogLocalChanges) {
-                    this.m_Islands[i].addPopulationChangedEventListener(myLocal);
-                }
-            }*/
+             if (this.m_LocalServer == null) {
+             this.m_LocalServer = RMIServer.getInstance();
+             }
+             try {
+             myLocal = (InterfacePopulationChangedEventListener) RMIProxyLocal.newInstance(this);
+             } catch(Exception e) {
+             System.err.println("Island Model EA warning on local RMIServer... but i'll try to start anyway!");
+             }
+             String[] nodesList = this.m_Servers.getCheckedServerNodes();
+             if ((nodesList == null) || (nodesList.length == 0)) {
+             return;
+             }
+             this.m_Islands = new InterfaceOptimizer[nodesList.length];
+             for (int i = 0; i < nodesList.length; i++) {
+             this.m_Islands[i] = (InterfaceOptimizer) RMIProxyRemoteThread.newInstance(this.m_Optimizer, nodesList[i]);
+             this.m_Islands[i].setIdentifier(""+i);
+             this.m_Islands[i].init();
+             if (this.m_LogLocalChanges) {
+             this.m_Islands[i].addPopulationChangedEventListener(myLocal);
+             }
+             }*/
         }
 
         this.m_Migration.initMigration(this.m_Islands);
         Population pop;
         for (int i = 0; i < this.m_Islands.length; i++) {
-            pop = (Population)this.m_Islands[i].getPopulation().clone();
+            pop = (Population) this.m_Islands[i].getPopulation().clone();
             this.m_Population.addPopulation(pop);
             this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
         this.firePropertyChangedEvent(Population.nextGenerationPerformed, this.m_Optimizer.getPopulation());
     }
 
-    /** The optimize method will compute an 'improved' and evaluated population
+    /**
+     * The optimize method will compute an 'improved' and evaluated population
      */
     @Override
     public void optimize() {
         for (int i = 0; i < this.m_Islands.length; i++) {
             if (this.m_Islands[i].getPopulation().size() > 0) {
-            	this.m_Islands[i].optimize();
-            	if (TRACE ) {
+                this.m_Islands[i].optimize();
+                if (TRACE) {
                     System.out.println(BeanInspector.toString(m_Islands[i].getPopulation()));
                 }
             } else {
@@ -253,28 +251,28 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         System.gc();
     }
 
-    /** This method will manage communication between the
-     * islands
+    /**
+     * This method will manage communication between the islands
      */
     private void communicate() {
         // Here i'll have to wait until all islands are finished
         boolean allReachedG = false;
-        int     G = this.m_Population.getGeneration();
+        int G = this.m_Population.getGeneration();
         while (!allReachedG) {
             allReachedG = true;
             String gen = "[";
             for (int i = 0; i < this.m_Islands.length; i++) {
-                gen += this.m_Islands[i].getPopulation().getGeneration()+"; ";
+                gen += this.m_Islands[i].getPopulation().getGeneration() + "; ";
                 if (this.m_Islands[i].getPopulation().getGeneration() != G) {
                     allReachedG = false;
                 }
             }
             if (!allReachedG) {
-                System.out.println("Waiting...."+gen+"] ?= " + G);
+                System.out.println("Waiting...." + gen + "] ?= " + G);
                 try {
-                  Thread.sleep(1000);
+                    Thread.sleep(1000);
                 } catch (Exception e) {
-                  System.err.println("Error in sleep of XThread");
+                    System.err.println("Error in sleep of XThread");
                 }
             }
         }
@@ -282,7 +280,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         this.m_Population.setFunctionCalls(0);
         Population pop;
         for (int i = 0; i < this.m_Islands.length; i++) {
-            pop = (Population)this.m_Islands[i].getPopulation().clone();
+            pop = (Population) this.m_Islands[i].getPopulation().clone();
             this.m_Population.addPopulation(pop);
             this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
@@ -296,46 +294,56 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         this.m_Migration.migrate(this.m_Islands);
     }
 
-    /** This method allows you to add the LectureGUI as listener to the Optimizer
+    /**
+     * This method allows you to add the LectureGUI as listener to the Optimizer
+     *
      * @param ea
      */
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    /** Something has changed
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name, Population population) {     
+    protected void firePropertyChangedEvent(String name, Population population) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
         this.m_Optimizer.setProblem(problem);
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -350,11 +358,11 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         if (this.m_HeterogenuousProblems) {
             result += " Heterogenuous Optimizers: \n";
             for (int i = 0; i < this.m_Islands.length; i++) {
-                result += this.m_Islands[i].getStringRepresentation() +"\n";
+                result += this.m_Islands[i].getStringRepresentation() + "\n";
             }
         } else {
             result += " Homogeneous Optimizer = " + this.m_Optimizer.getClass().toString() + "\n";
-            result += this.m_Optimizer.getStringRepresentation() +"\n";
+            result += this.m_Optimizer.getStringRepresentation() + "\n";
         }
         //result += "=> The Optimization Problem: ";
         //result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
@@ -362,22 +370,23 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         return result;
     }
 
-    /** This method is to test the parallelization scheme
+    /**
+     * This method is to test the parallelization scheme
      *
      * @param args
      */
     public static void main(String[] args) {
         // @todo die ServerStarter muss ich noch hin kriegen
         // @todo Wichtig ich brauche den eva2.tools.jproxy.RMIServer!
-        IslandModelEA imea  = new IslandModelEA();
+        IslandModelEA imea = new IslandModelEA();
         imea.m_Show = true;
         imea.m_localOnly = false;
         if (false) {
-            imea.m_Optimizer    = new MultiObjectiveEA();
-            ((MultiObjectiveEA)imea.m_Optimizer).setArchiveSize(25);
-            ((MultiObjectiveEA)imea.m_Optimizer).getPopulation().setTargetSize(50);
-            imea.m_Problem      = new TF1Problem();
-            ((TF1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
+            imea.m_Optimizer = new MultiObjectiveEA();
+            ((MultiObjectiveEA) imea.m_Optimizer).setArchiveSize(25);
+            ((MultiObjectiveEA) imea.m_Optimizer).getPopulation().setTargetSize(50);
+            imea.m_Problem = new TF1Problem();
+            ((TF1Problem) imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
 //            ((TF1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
 //            imea.m_Problem      = new TFPortfolioSelectionProblem();
 //            ((TFPortfolioSelectionProblem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
@@ -386,7 +395,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
                 c.getKMeans().setUseSearchSpace(false);
                 c.setUseConstraints(true);
                 c.m_Debug = true;
-                imea.m_Migration    = c;
+                imea.m_Migration = c;
             }
             if (false) {
                 MOConeSeparation c = new MOConeSeparation();
@@ -395,11 +404,11 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
                 imea.m_Migration = c;
             }
             if (true) {
-                imea.m_Migration    = new MOBestMigration();
+                imea.m_Migration = new MOBestMigration();
             }
         } else {
-            imea.m_Problem      = new F8Problem();
-            ((F1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
+            imea.m_Problem = new F8Problem();
+            ((F1Problem) imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
         }
         imea.m_MigrationRate = 15;
         imea.init();
@@ -410,75 +419,82 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         //System.exit(0);
     }
 
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method will return the Optimizers
+    @Override
+    public String getIdentifier() {
+        return this.m_Identifier;
+    }
+
+    /**
+     * This method will return the Optimizers
+     *
      * @return An array of optimizers
      */
     public InterfaceOptimizer[] getOptimizers() {
         return this.m_Islands;
     }
 
-    /** This method will allow you to toggel between homogenuous and heterogenuous problems.
-     * In case of heterogenuous problems the individuals need to be reevaluated after migration.
+    /**
+     * This method will allow you to toggel between homogenuous and
+     * heterogenuous problems. In case of heterogenuous problems the individuals
+     * need to be reevaluated after migration.
+     *
      * @param t
      */
     public void setHeterogenuousProblems(boolean t) {
         this.m_HeterogenuousProblems = t;
     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
+    /**
+     * ********************************************************************************************************************
+     * These are for InterfacePopulationChangedEventListener
      */
-    @Override
-    public void freeWilly() {
-        for (int i = 0; i < this.m_Islands.length; i++) {
-            this.m_Islands[i].freeWilly();
-        }
-    }
-
-/**********************************************************************************************************************
- * These are for InterfacePopulationChangedEventListener
- */
-    /** This method allows an optimizer to register a change in the EA-lecture
-     * @param source        The source of the event.
-     * @param name          Could be used to indicate the nature of the event.
+    /**
+     * This method allows an optimizer to register a change in the EA-lecture
+     *
+     * @param source The source of the event.
+     * @param name Could be used to indicate the nature of the event.
      */
     @Override
     public void registerPopulationStateChanged(Object source, String name) {
-        InterfaceOptimizer  opt = (InterfaceOptimizer)source;
-        int                 sourceID = new Integer(opt.getIdentifier()).intValue();
-        double              cFCOpt = opt.getPopulation().getFunctionCalls();
-        double              plotValue = (this.m_Problem.getDoublePlotValue(opt.getPopulation())).doubleValue();
+        InterfaceOptimizer opt = (InterfaceOptimizer) source;
+        int sourceID = new Integer(opt.getIdentifier()).intValue();
+        double cFCOpt = opt.getPopulation().getFunctionCalls();
+        double plotValue = (this.m_Problem.getDoublePlotValue(opt.getPopulation())).doubleValue();
 
         if (this.m_Show) {
-            this.m_Plot.setConnectedPoint(cFCOpt, plotValue, (sourceID+1));
+            this.m_Plot.setConnectedPoint(cFCOpt, plotValue, (sourceID + 1));
         }
 
         //System.out.println("Someone has finished, ("+this.m_Generation+"/"+this.m_Performed+")");
         //System.out.println(sourceID + " is at generation "+ opt.getPopulation().getGeneration() +" i'm at " +this.m_Generation);
     }
 
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "This is an island model EA distributing the individuals across several (remote) CPUs for optimization.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -486,8 +502,10 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         return "IslandEA";
     }
 
-    /** This method allows you to toggle between a truly parallel
-     * and a serial implementation.
+    /**
+     * This method allows you to toggle between a truly parallel and a serial
+     * implementation.
+     *
      * @return The current optimization mode
      */
     // TODO Deactivated from GUI because the current implementation does not really parallelize on a multicore. 
@@ -495,62 +513,79 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
 //    public boolean isLocalOnly() {
 //        return this.m_localOnly;
 //    }
-    public void setLocalOnly(boolean b){
+    public void setLocalOnly(boolean b) {
         this.m_localOnly = b;
     }
+
     public String localOnlyTipText() {
         return "Toggle between usage of local CPUs and remote servers.";
     }
 
-    /** This will show the local performance
+    /**
+     * This will show the local performance
+     *
      * @return The current optimzation mode
      */
     public boolean getShow() {
         return this.m_Show;
     }
-    public void setShow(boolean b){
-        this.m_Show             = b;
-        this.m_LogLocalChanges  = b;
+
+    public void setShow(boolean b) {
+        this.m_Show = b;
+        this.m_LogLocalChanges = b;
     }
+
     public String showTipText() {
         return "This will show the local performance.";
     }
 
-    /** This method allows you to set/get the optimizing technique to use.
+    /**
+     * This method allows you to set/get the optimizing technique to use.
+     *
      * @return The current optimizing method
      */
     public InterfaceOptimizer getOptimizer() {
         return this.m_Optimizer;
     }
-    public void setOptimizer(InterfaceOptimizer b){
+
+    public void setOptimizer(InterfaceOptimizer b) {
         this.m_Optimizer = b;
     }
+
     public String optimizerTipText() {
         return "Choose a population based optimizing technique to use.";
     }
 
-    /** This method allows you to set/get the migration strategy to use.
+    /**
+     * This method allows you to set/get the migration strategy to use.
+     *
      * @return The current migration strategy
      */
     public InterfaceMigration getMigrationStrategy() {
         return this.m_Migration;
     }
-    public void setMigrationStrategy(InterfaceMigration b){
+
+    public void setMigrationStrategy(InterfaceMigration b) {
         this.m_Migration = b;
     }
+
     public String migrationStrategyTipText() {
         return "Choose a migration strategy to use.";
     }
 
-    /** This method allows you to set/get the migration rate to use.
+    /**
+     * This method allows you to set/get the migration rate to use.
+     *
      * @return The current migration rate
      */
     public int getMigrationRate() {
         return this.m_MigrationRate;
     }
-    public void setMigrationRate(int b){
+
+    public void setMigrationRate(int b) {
         this.m_MigrationRate = b;
     }
+
     public String migrationRateTipText() {
         return "Set the migration rate for communication between islands.";
     }
@@ -559,36 +594,42 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         return "Choose and manage the servers (only active in parallelized mode).";
     }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
     public void setPopulation(Population pop) {
         // @todo Jetzt m<>sste ich die pop auch auf die Rechner verteilen...
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "(Defunct)";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
-    /** This method allows you to set the number of processors in local mode
-     * @param n     Number of processors.
+
+    /**
+     * This method allows you to set the number of processors in local mode
+     *
+     * @param n Number of processors.
      */
     public void setNumberLocalCPUs(int n) {
-    	if (n>=1) {
+        if (n >= 1) {
             this.m_numLocalCPUs = n;
-        }
-    	else {
+        } else {
             System.err.println("Number of CPUs must be at least 1!");
         }
     }
@@ -597,6 +638,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
 //    public int getNumberLocalCPUs() {
 //        return this.m_LocalCPUs;
 //    }
+
     public String numberLocalCPUsTipText() {
         return "Set the number of local CPUS (>=1, only used in local mode).";
     }

src/eva2/server/go/strategies/LTGA.java

@@ -364,10 +364,6 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
         return "Linkage Tree GA";
     }
 
-    @Override
-    public void freeWilly() {
-    }
-
     @Override
     public void registerPopulationStateChanged(Object source, String name) {
         // The events of the interim hill climbing population will be caught here 

src/eva2/server/go/strategies/MLTGA.java

@@ -246,13 +246,13 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
         this.problem.evaluatePopulationStart(this.population);
         Stack<Set<Integer>> linkageTree = buildLinkageTree();
         Population newPop = new Population(this.popSize);
-        if(elitism){
+        if (elitism) {
             AbstractEAIndividual firstIndy = this.population.getBestEAIndividual();
             AbstractEAIndividual firstNewIndy = buildNewIndy(firstIndy, linkageTree);
             newPop.add(firstNewIndy);
         }
         for (int i = 0; i < this.popSize; i++) {
-            if(this.elitism && i==0){
+            if (this.elitism && i == 0) {
                 continue;
             }
             Population indies = this.population.getRandNIndividuals(1);
@@ -265,21 +265,21 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
     }
 
     private AbstractEAIndividual buildNewIndy(AbstractEAIndividual indy,
-    		Stack<Set<Integer>> linkageTree) {
-    	for (Set<Integer> mask : linkageTree) {
-    		BitSet gen = getBinaryData(indy);
-    		BitSet newGene = (BitSet) gen.clone();
-    		for (Integer flipID : mask) {
-    			newGene.flip(flipID);
-    		}
-    		AbstractEAIndividual newIndy = (AbstractEAIndividual) this.template.clone();
-    		((InterfaceDataTypeBinary) newIndy).SetBinaryGenotype(newGene);
-    		evaluate(newIndy);
-    		if (newIndy.getFitness(0) < indy.getFitness(0)) {
-    			indy = newIndy;
-    		}
-    	}
-    	return indy;
+            Stack<Set<Integer>> linkageTree) {
+        for (Set<Integer> mask : linkageTree) {
+            BitSet gen = getBinaryData(indy);
+            BitSet newGene = (BitSet) gen.clone();
+            for (Integer flipID : mask) {
+                newGene.flip(flipID);
+            }
+            AbstractEAIndividual newIndy = (AbstractEAIndividual) this.template.clone();
+            ((InterfaceDataTypeBinary) newIndy).SetBinaryGenotype(newGene);
+            evaluate(newIndy);
+            if (newIndy.getFitness(0) < indy.getFitness(0)) {
+                indy = newIndy;
+            }
+        }
+        return indy;
     }
 
     /**
@@ -321,15 +321,15 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
         this.problem = (AbstractOptimizationProblem) problem;
     }
 
-    public boolean getElitism(){
+    public boolean getElitism() {
         return this.elitism;
     }
 
-    public void setElitism(boolean b){
+    public void setElitism(boolean b) {
         this.elitism = b;
     }
 
-    public String elitismTipText(){
+    public String elitismTipText() {
         return "use elitism?";
     }
 
@@ -343,10 +343,6 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
         return "Linkage Tree GA";
     }
 
-    @Override
-    public void freeWilly() {
-    }
-
     @Override
     public void registerPopulationStateChanged(Object source, String name) {
         // The events of the interim hill climbing population will be caught here 

src/eva2/server/go/strategies/MemeticAlgorithm.java

@@ -12,421 +12,400 @@ import eva2.server.go.problems.InterfaceLocalSearchable;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import java.util.Hashtable;
 
-
 /**
  * A memetic algorithm by hannes planatscher. The local search strategy can only
  * be applied to problems which implement the InterfaceLocalSearchable else the
- * local search will not be activated at all.
- * <p>
- * Title: EvA2
- * </p>
- * <p>
- * Description:
- * </p>
- * <p>
- * Copyright: Copyright (c) 2003
- * </p>
- * <p>
- * Company:
- * </p>
+ * local search will not be activated at all. <p> Title: EvA2 </p> <p>
+ * Description: </p> <p> Copyright: Copyright (c) 2003 </p> <p> Company: </p>
  *
  * @author not attributable
  * @version 1.0
  */
-
 public class MemeticAlgorithm implements InterfaceOptimizer,
-    java.io.Serializable {
+        java.io.Serializable {
 
-	/**
-   * serial version uid.
-   */
-  private static final long serialVersionUID = -1730086430763348568L;
+    /**
+     * serial version uid.
+     */
+    private static final long serialVersionUID = -1730086430763348568L;
+    private int localSearchSteps = 1;
+    private int subsetsize = 5;
+    private int globalSearchIterations = 1;
+    private boolean lamarckism = true;
+    // int counter = 0; !?
+    // int maxfunctioncalls = 1000; !?
+    private boolean TRACE = false;
+    private String m_Identifier = "";
+    private InterfaceOptimizationProblem m_Problem = new F1Problem();
+    private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm();
+    private InterfaceSelection selectorPlug = new SelectBestIndividuals();
+    transient private InterfacePopulationChangedEventListener m_Listener;
 
-	private int	                                              localSearchSteps	= 1;
+    public MemeticAlgorithm() {
+    }
 
-	private int	                                              subsetsize	      = 5;
-
-	private int	                                              globalSearchIterations	= 1;
-
-	private boolean	                                          lamarckism	      = true;
-
-	// int counter = 0; !?
-	// int maxfunctioncalls = 1000; !?
-
-	private boolean	                                          TRACE	            = false;
-
-	private String	                                          m_Identifier	    = "";
-
-	private InterfaceOptimizationProblem	                    m_Problem	        = new F1Problem();
-
-	private InterfaceOptimizer	                              m_GlobalOptimizer	= new GeneticAlgorithm();
-
-	private InterfaceSelection	                              selectorPlug	    = new SelectBestIndividuals();
-
-	transient private InterfacePopulationChangedEventListener	m_Listener;
-
-	public MemeticAlgorithm() {
-
-	}
-
-	public MemeticAlgorithm(MemeticAlgorithm a) {
-		// this.m_Population = (Population)a.m_Population.clone();
-		this.m_Problem = (InterfaceLocalSearchable) a.m_Problem.clone();
-		this.m_GlobalOptimizer = (InterfaceOptimizer) a.m_GlobalOptimizer;
-		this.selectorPlug = (InterfaceSelection) a.selectorPlug;
-		this.m_Identifier = a.m_Identifier;
-		this.localSearchSteps = a.localSearchSteps;
-		this.subsetsize = a.subsetsize;
-		this.globalSearchIterations = a.globalSearchIterations;
-		this.lamarckism = a.lamarckism;
-	}
-
-	@Override
-	public Object clone() {
-		return new MemeticAlgorithm(this);
-	}
+    public MemeticAlgorithm(MemeticAlgorithm a) {
+        // this.m_Population = (Population)a.m_Population.clone();
+        this.m_Problem = (InterfaceLocalSearchable) a.m_Problem.clone();
+        this.m_GlobalOptimizer = (InterfaceOptimizer) a.m_GlobalOptimizer;
+        this.selectorPlug = (InterfaceSelection) a.selectorPlug;
+        this.m_Identifier = a.m_Identifier;
+        this.localSearchSteps = a.localSearchSteps;
+        this.subsetsize = a.subsetsize;
+        this.globalSearchIterations = a.globalSearchIterations;
+        this.lamarckism = a.lamarckism;
+    }
 
     @Override
-	public void initByPopulation(Population pop, boolean reset) {
-		this.setPopulation((Population) pop.clone());
-		if (reset) {
-			this.getPopulation().init();
-			this.m_Problem.evaluate(this.getPopulation());
-			this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-		}
-	}
+    public Object clone() {
+        return new MemeticAlgorithm(this);
+    }
 
     @Override
-	public void init() {
-		// counter = 0;
-		this.m_GlobalOptimizer.setProblem(this.m_Problem);
-		this.m_GlobalOptimizer.init();
-		this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation());
-		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-	}
-
-	/**
-	 * This method will evaluate the current population using the given problem.
-	 *
-	 * @param population
-	 *          The population that is to be evaluated
-	 */
-	private void evaluatePopulation(Population population) {
-		this.m_Problem.evaluate(population);
-		population.incrGeneration();
-	}
+    public void initByPopulation(Population pop, boolean reset) {
+        this.setPopulation((Population) pop.clone());
+        if (reset) {
+            this.getPopulation().init();
+            this.m_Problem.evaluate(this.getPopulation());
+            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+        }
+    }
 
     @Override
-	public void optimize() {
+    public void init() {
+        // counter = 0;
+        this.m_GlobalOptimizer.setProblem(this.m_Problem);
+        this.m_GlobalOptimizer.init();
+        this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation());
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+    }
 
-		if (TRACE) {
-                System.out.println("global search");
+    /**
+     * This method will evaluate the current population using the given problem.
+     *
+     * @param population The population that is to be evaluated
+     */
+    private void evaluatePopulation(Population population) {
+        this.m_Problem.evaluate(population);
+        population.incrGeneration();
+    }
+
+    @Override
+    public void optimize() {
+
+        if (TRACE) {
+            System.out.println("global search");
+        }
+        this.m_GlobalOptimizer.optimize();
+
+        if ((globalSearchIterations > 0) && (((this.m_GlobalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0))
+                && (this.localSearchSteps > 0)
+                && (this.m_Problem instanceof InterfaceLocalSearchable)) {
+            // here the local search is performed
+            if (TRACE) {
+                System.out.println("Performing local search on " + subsetsize
+                        + " individuals.");
             }
-		this.m_GlobalOptimizer.optimize();
-
-		if ((globalSearchIterations>0) && (((this.m_GlobalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0))
-		    && (this.localSearchSteps > 0)
-		    && (this.m_Problem instanceof InterfaceLocalSearchable)) {
-			// here the local search is performed
-			if (TRACE) {
-                            System.out.println("Performing local search on " + subsetsize
-                                + " individuals.");
-                        }
-			Population gop = this.m_GlobalOptimizer.getPopulation();
-			Population subset = selectorPlug.selectFrom(gop, subsetsize);
-			Population subsetclone = new Population();
-			for (int i = 0; i < subset.size(); i++) {
-				subsetclone.add(((AbstractEAIndividual) subset.get(i)).clone());
-			}
-			if (subset.size() != subsetsize) {
-                            System.err.println("ALERT! identical individual instances in subset");
-                        }
-			Hashtable antilamarckismcache = new Hashtable();
-			if (!this.lamarckism) {
-				for (int i = 0; i < subset.size(); i++) {
-					AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i);
-					AbstractEAIndividual indyclone = (AbstractEAIndividual) subsetclone
-					    .get(i);
-					antilamarckismcache.put(indy, indyclone);
-				}
-			}
-
-			// int dosearchsteps = this.localSearchSteps;
-			double cost = ((InterfaceLocalSearchable) this.m_Problem)
-			    .getLocalSearchStepFunctionCallEquivalent();
-			// int calls = gop.getFunctionCalls() + (int) Math.round(localSearchSteps
-			// * cost * subset.size());
-			// nett aber total unn<6E>tig-falsch man kann nicht davon ausgehen, dass man
-			// einen Fitnesscall Terminator hat..
-			// if (calls > this.maxfunctioncalls) {
-			// int remainingfunctioncalls = this.maxfunctioncalls -
-			// gop.getFunctionCalls();
-			// dosearchsteps = (int)Math.floor(((double) remainingfunctioncalls) /
-			// (cost * subsetsize));
-			// stopit = true;
-			// }
-			for (int i = 0; i < localSearchSteps; i++) {
-				((InterfaceLocalSearchable) this.m_Problem).doLocalSearch(subsetclone);
-			}
-			this.m_Problem.evaluate(subsetclone);
-			if (this.lamarckism) {
-				gop.removeAll(subset);
-				gop.addPopulation(subsetclone);
-			} else {
-				for (int i = 0; i < subset.size(); i++) {
-					AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i);
-					try {
-						AbstractEAIndividual newindy = (AbstractEAIndividual) antilamarckismcache
-						    .get(indy);
-						indy.setFitness(newindy.getFitness());
-					} catch (Exception ex) {
-						System.err.println("individual not found in antilamarckismcache");
-					}
-				}
-			}
-			// eigentlich muss hier noch subsetsize drauf, aber lassen wir das
-			gop.SetFunctionCalls(gop.getFunctionCalls()
-			    + (int) Math.round(localSearchSteps * cost * subset.size()));
-
-			if (TRACE) {
-                            System.out.println("Population size after local search:" + gop.size());
-                        }
-
-			this.setPopulation(gop);
-		}
-
-		if (TRACE) {
-                System.out.println("function calls"
-                    + this.m_GlobalOptimizer.getPopulation().getFunctionCalls());
+            Population gop = this.m_GlobalOptimizer.getPopulation();
+            Population subset = selectorPlug.selectFrom(gop, subsetsize);
+            Population subsetclone = new Population();
+            for (int i = 0; i < subset.size(); i++) {
+                subsetclone.add(((AbstractEAIndividual) subset.get(i)).clone());
+            }
+            if (subset.size() != subsetsize) {
+                System.err.println("ALERT! identical individual instances in subset");
+            }
+            Hashtable antilamarckismcache = new Hashtable();
+            if (!this.lamarckism) {
+                for (int i = 0; i < subset.size(); i++) {
+                    AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i);
+                    AbstractEAIndividual indyclone = (AbstractEAIndividual) subsetclone
+                            .get(i);
+                    antilamarckismcache.put(indy, indyclone);
+                }
             }
-		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-	}
 
-	/**
-	 * This method allows you to add the LectureGUI as listener to the Optimizer
-	 *
-	 * @param ea
-	 */
-    @Override
-	public void addPopulationChangedEventListener(
-	    InterfacePopulationChangedEventListener ea) {
-		this.m_Listener = ea;
-	}
-    @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-	/**
-	 * Something has changed
-	 */
-	protected void firePropertyChangedEvent(String name) {
-		if (this.m_Listener != null) {
-			if (TRACE) {
-                        System.out.println("firePropertyChangedEvent MA");
+            // int dosearchsteps = this.localSearchSteps;
+            double cost = ((InterfaceLocalSearchable) this.m_Problem)
+                    .getLocalSearchStepFunctionCallEquivalent();
+            // int calls = gop.getFunctionCalls() + (int) Math.round(localSearchSteps
+            // * cost * subset.size());
+            // nett aber total unn<6E>tig-falsch man kann nicht davon ausgehen, dass man
+            // einen Fitnesscall Terminator hat..
+            // if (calls > this.maxfunctioncalls) {
+            // int remainingfunctioncalls = this.maxfunctioncalls -
+            // gop.getFunctionCalls();
+            // dosearchsteps = (int)Math.floor(((double) remainingfunctioncalls) /
+            // (cost * subsetsize));
+            // stopit = true;
+            // }
+            for (int i = 0; i < localSearchSteps; i++) {
+                ((InterfaceLocalSearchable) this.m_Problem).doLocalSearch(subsetclone);
+            }
+            this.m_Problem.evaluate(subsetclone);
+            if (this.lamarckism) {
+                gop.removeAll(subset);
+                gop.addPopulation(subsetclone);
+            } else {
+                for (int i = 0; i < subset.size(); i++) {
+                    AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i);
+                    try {
+                        AbstractEAIndividual newindy = (AbstractEAIndividual) antilamarckismcache
+                                .get(indy);
+                        indy.setFitness(newindy.getFitness());
+                    } catch (Exception ex) {
+                        System.err.println("individual not found in antilamarckismcache");
                     }
-			this.m_Listener.registerPopulationStateChanged(this, name);
-		}
-	}
+                }
+            }
+            // eigentlich muss hier noch subsetsize drauf, aber lassen wir das
+            gop.SetFunctionCalls(gop.getFunctionCalls()
+                    + (int) Math.round(localSearchSteps * cost * subset.size()));
 
-	/**
-	 * This method will set the problem that is to be optimized
-	 *
-	 * @param problem
-	 */
+            if (TRACE) {
+                System.out.println("Population size after local search:" + gop.size());
+            }
+
+            this.setPopulation(gop);
+        }
+
+        if (TRACE) {
+            System.out.println("function calls"
+                    + this.m_GlobalOptimizer.getPopulation().getFunctionCalls());
+        }
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+    }
+
+    /**
+     * This method allows you to add the LectureGUI as listener to the Optimizer
+     *
+     * @param ea
+     */
     @Override
-	public void setProblem(InterfaceOptimizationProblem problem) {
-		this.m_Problem = problem;
-		this.m_GlobalOptimizer.setProblem(this.m_Problem);
-	}
+    public void addPopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        this.m_Listener = ea;
+    }
 
     @Override
-	public InterfaceOptimizationProblem getProblem() {
-		return this.m_Problem;
-	}
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
 
-	/**
-	 * This method will return a string describing all properties of the optimizer
-	 * and the applied methods.
-	 *
-	 * @return A descriptive string
-	 */
-    @Override
-	public String getStringRepresentation() {
-		String result = "";
-		result += "Memetic Algorithm:\n";
-		result += "Optimization Problem: ";
-		result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
-		result += this.m_GlobalOptimizer.getStringRepresentation();
-		return result;
-	}
+    /**
+     * Something has changed
+     */
+    protected void firePropertyChangedEvent(String name) {
+        if (this.m_Listener != null) {
+            if (TRACE) {
+                System.out.println("firePropertyChangedEvent MA");
+            }
+            this.m_Listener.registerPopulationStateChanged(this, name);
+        }
+    }
 
-	/**
-	 * This method allows you to set an identifier for the algorithm
-	 *
-	 * @param name
-	 *          The indenifier
-	 */
+    /**
+     * This method will set the problem that is to be optimized
+     *
+     * @param problem
+     */
     @Override
-	public void setIdentifier(String name) {
-		this.m_Identifier = name;
-	}
+    public void setProblem(InterfaceOptimizationProblem problem) {
+        this.m_Problem = problem;
+        this.m_GlobalOptimizer.setProblem(this.m_Problem);
+    }
 
     @Override
-	public String getIdentifier() {
-		return this.m_Identifier;
-	}
+    public InterfaceOptimizationProblem getProblem() {
+        return this.m_Problem;
+    }
 
-	/**
-	 * This method is required to free the memory on a RMIServer, but there is
-	 * nothing to implement.
-	 */
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
+     * @return A descriptive string
+     */
     @Override
-	public void freeWilly() {
+    public String getStringRepresentation() {
+        String result = "";
+        result += "Memetic Algorithm:\n";
+        result += "Optimization Problem: ";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
+        result += this.m_GlobalOptimizer.getStringRepresentation();
+        return result;
+    }
 
-	}
-
-	/*
-	 * ========================================================================================
-	 * These are for GUI
-	 */
-	/**
-	 * This method returns a global info string
-	 *
-	 * @return description
-	 */
-	public static String globalInfo() {
-		return "This is a basic generational Memetic Algorithm. Local search steps are performed on a selected subset " +
-				"of individuals after certain numbers of global search iterations. Note " +
-				"that the problem class must implement InterfaceLocalSearchable.";
-	}
-
-	/**
-	 * This method will return a naming String
-	 *
-	 * @return The name of the algorithm
-	 */
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
+     */
     @Override
-	public String getName() {
-		return "MemeticAlgorithm";
-	}
-
-	/**
-	 * Assuming that all optimizer will store thier data in a population we will
-	 * allow acess to this population to query to current state of the optimizer.
-	 *
-	 * @return The population of current solutions to a given problem.
-	 */
-    @Override
-	public Population getPopulation() {
-		return this.m_GlobalOptimizer.getPopulation();
-	}
+    public void setIdentifier(String name) {
+        this.m_Identifier = name;
+    }
 
     @Override
-	public void setPopulation(Population pop) {
-		this.m_GlobalOptimizer.setPopulation(pop);
-	}
+    public String getIdentifier() {
+        return this.m_Identifier;
+    }
 
-	public String populationTipText() {
-		return "Edit the properties of the population used.";
-	}
 
-	/**
-	 * Choose the global optimization strategy to use
-	 *
-	 * @param m_GlobalOptimizer
-	 */
-	public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) {
-		this.m_GlobalOptimizer = m_GlobalOptimizer;
-		this.m_GlobalOptimizer.setProblem(this.getProblem());
-		this.init();
-	}
+    /*
+     * ========================================================================================
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
+     * @return description
+     */
+    public static String globalInfo() {
+        return "This is a basic generational Memetic Algorithm. Local search steps are performed on a selected subset "
+                + "of individuals after certain numbers of global search iterations. Note "
+                + "that the problem class must implement InterfaceLocalSearchable.";
+    }
 
-	public InterfaceOptimizer getGlobalOptimizer() {
-		return m_GlobalOptimizer;
-	}
+    /**
+     * This method will return a naming String
+     *
+     * @return The name of the algorithm
+     */
+    @Override
+    public String getName() {
+        return "MemeticAlgorithm";
+    }
 
-	public String globalOptimizerTipText() {
-		return "Choose the global optimization strategy to use.";
-	}
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
+     * @return The population of current solutions to a given problem.
+     */
+    @Override
+    public Population getPopulation() {
+        return this.m_GlobalOptimizer.getPopulation();
+    }
 
-	/**
-	 * Choose the number of local search steps to perform per selected individual.
-	 *
-	 * @param localSearchSteps
-	 */
-	public void setLocalSearchSteps(int localSearchSteps) {
-		this.localSearchSteps = localSearchSteps;
-	}
-	public int getLocalSearchSteps() {
-		return localSearchSteps;
-	}
-	public String localSearchStepsTipText() {
-		return "Choose the number of local search steps to perform per selected individual.";
-	}
+    @Override
+    public void setPopulation(Population pop) {
+        this.m_GlobalOptimizer.setPopulation(pop);
+    }
 
-	/**
-	 * Choose the interval between the application of the local search
-	 *
-	 * @param globalSearchSteps
-	 */
-	public void setGlobalSearchIterations(int globalSearchSteps) {
-		this.globalSearchIterations = globalSearchSteps;
-	}
-	public int getGlobalSearchIterations() {
-		return globalSearchIterations;
-	}
-	public String globalSearchIterationsTipText() {
-		return "Choose the interval between the application of the local search.";
-	}
+    public String populationTipText() {
+        return "Edit the properties of the population used.";
+    }
+
+    /**
+     * Choose the global optimization strategy to use
+     *
+     * @param m_GlobalOptimizer
+     */
+    public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) {
+        this.m_GlobalOptimizer = m_GlobalOptimizer;
+        this.m_GlobalOptimizer.setProblem(this.getProblem());
+        this.init();
+    }
+
+    public InterfaceOptimizer getGlobalOptimizer() {
+        return m_GlobalOptimizer;
+    }
+
+    public String globalOptimizerTipText() {
+        return "Choose the global optimization strategy to use.";
+    }
+
+    /**
+     * Choose the number of local search steps to perform per selected
+     * individual.
+     *
+     * @param localSearchSteps
+     */
+    public void setLocalSearchSteps(int localSearchSteps) {
+        this.localSearchSteps = localSearchSteps;
+    }
+
+    public int getLocalSearchSteps() {
+        return localSearchSteps;
+    }
+
+    public String localSearchStepsTipText() {
+        return "Choose the number of local search steps to perform per selected individual.";
+    }
+
+    /**
+     * Choose the interval between the application of the local search
+     *
+     * @param globalSearchSteps
+     */
+    public void setGlobalSearchIterations(int globalSearchSteps) {
+        this.globalSearchIterations = globalSearchSteps;
+    }
+
+    public int getGlobalSearchIterations() {
+        return globalSearchIterations;
+    }
+
+    public String globalSearchIterationsTipText() {
+        return "Choose the interval between the application of the local search.";
+    }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
+    }
+
+    /**
+     * Choose the number of individual to be locally optimized
+     *
+     * @param subsetsize
+     */
+    public void setSubsetsize(int subsetsize) {
+        this.subsetsize = subsetsize;
     }
 
-	/**
-	 * Choose the number of individual to be locally optimized
-	 *
-	 * @param subsetsize
-	 */
-	public void setSubsetsize(int subsetsize) {
-		this.subsetsize = subsetsize;
-	}
     public int getSubsetsize() {
-		return subsetsize;
-	}
-	public String subsetsizeTipText() {
-		return "Choose the number of individuals to be locally optimized.";
-	}
+        return subsetsize;
+    }
 
-	/**
-	 * Toggle between Lamarckism and the Baldwin Effect
-	 *
-	 * @param lamarckism
-	 */
-	public void setLamarckism(boolean lamarckism) {
-		this.lamarckism = lamarckism;
-	}
-	public String lamarckismTipText() {
-		return "Toggle between Lamarckism and the Baldwin Effect.";
-	}
-	public boolean isLamarckism() {
-		return lamarckism;
-	}
+    public String subsetsizeTipText() {
+        return "Choose the number of individuals to be locally optimized.";
+    }
 
-	public InterfaceSelection getSubSetSelector() {
-		return selectorPlug;
-	}
-	public void setSubSetSelector(InterfaceSelection selectorPlug) {
-		this.selectorPlug = selectorPlug;
-	}
-	public String subSetSelectorTipText() {
-		return "Selection method to select the subset on which local search is to be performed.";
-	}
+    /**
+     * Toggle between Lamarckism and the Baldwin Effect
+     *
+     * @param lamarckism
+     */
+    public void setLamarckism(boolean lamarckism) {
+        this.lamarckism = lamarckism;
+    }
+
+    public String lamarckismTipText() {
+        return "Toggle between Lamarckism and the Baldwin Effect.";
+    }
+
+    public boolean isLamarckism() {
+        return lamarckism;
+    }
+
+    public InterfaceSelection getSubSetSelector() {
+        return selectorPlug;
+    }
+
+    public void setSubSetSelector(InterfaceSelection selectorPlug) {
+        this.selectorPlug = selectorPlug;
+    }
+
+    public String subSetSelectorTipText() {
+        return "Selection method to select the subset on which local search is to be performed.";
+    }
 }

src/eva2/server/go/strategies/MonteCarloSearch.java

@@ -10,34 +10,33 @@ import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
 /**
- * The simple random or Monte-Carlo search, simple but useful
- * to evaluate the complexity of the search space.
- * This implements a Random Walk Search using the initialization
- * method of the problem instance, meaning that the random characteristics
- * may be problem dependent.
+ * The simple random or Monte-Carlo search, simple but useful to evaluate the
+ * complexity of the search space. This implements a Random Walk Search using
+ * the initialization method of the problem instance, meaning that the random
+ * characteristics may be problem dependent.
  *
- * Copyright:       Copyright (c) 2003
- * Company:         University of Tuebingen, Computer Architecture
- * @author          Felix Streichert
- * @version:  $Revision: 307 $
- *            $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
- *            $Author: mkron $
+ * Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
+ * Architecture
+ *
+ * @author Felix Streichert
+ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
+ * 2007) $ $Author: mkron $
  */
 public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializable {
-    /**
-	 * Generated serial version id.
-	 */
-	private static final long serialVersionUID = -751760624411490405L;
-	// These variables are necessary for the simple testcase
-    private InterfaceOptimizationProblem    m_Problem               = new B1Problem();
-    private int                             m_MultiRuns             = 100;
-    private int                             m_FitnessCalls          = 100;
-    private int                             m_FitnessCallsNeeded    = 0;
-    private Population                      m_Population;
-    private GAIndividualBinaryData          m_Best, m_Test;
 
+    /**
+     * Generated serial version id.
+     */
+    private static final long serialVersionUID = -751760624411490405L;
+    // These variables are necessary for the simple testcase
+    private InterfaceOptimizationProblem m_Problem = new B1Problem();
+    private int m_MultiRuns = 100;
+    private int m_FitnessCalls = 100;
+    private int m_FitnessCallsNeeded = 0;
+    private Population m_Population;
+    private GAIndividualBinaryData m_Best, m_Test;
     // These variables are necessary for the more complex LectureGUI enviroment
-    transient private String                m_Identifier = "";
+    transient private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
 
     public MonteCarloSearch() {
@@ -46,8 +45,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
     }
 
     public MonteCarloSearch(MonteCarloSearch a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
     }
 
     @Override
@@ -55,7 +54,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
         return (Object) new MonteCarloSearch(this);
     }
 
-    /** This method will init the MonteCarloSearch
+    /**
+     * This method will init the MonteCarloSearch
      */
     @Override
     public void init() {
@@ -64,37 +64,39 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
-        this.m_Population = (Population)pop.clone();
+        this.m_Population = (Population) pop.clone();
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
     /**
-     * This method will optimize without specific operators, by just calling the individual method
-     * for initialization.
+     * This method will optimize without specific operators, by just calling the
+     * individual method for initialization.
      */
     @Override
     public void optimize() {
-        Population original = (Population)this.m_Population.clone();
+        Population original = (Population) this.m_Population.clone();
 
 //        this.m_Problem.initPopulation(this.m_Population);
-        for (int i=0; i<m_Population.size(); i++) {
-        	m_Population.getEAIndividual(i).defaultInit(null);
+        for (int i = 0; i < m_Population.size(); i++) {
+            m_Population.getEAIndividual(i).defaultInit(null);
         }
 
         this.m_Population.SetFunctionCalls(original.getFunctionCalls());
         this.m_Problem.evaluate(this.m_Population);
         for (int i = 0; i < this.m_Population.size(); i++) {
-            if (((AbstractEAIndividual)original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual)this.m_Population.get(i)))) {
+            if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.m_Population.get(i)))) {
                 this.m_Population.remove(i);
                 this.m_Population.add(i, original.get(i));
             }
@@ -103,20 +105,23 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will init the HillClimber
+    /**
+     * This method will init the HillClimber
      */
     public void defaultInit() {
         this.m_FitnessCallsNeeded = 0;
@@ -124,7 +129,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
         this.m_Best.defaultInit(m_Problem);
     }
 
-    /** This method will optimize
+    /**
+     * This method will optimize
      */
     public void defaultOptimize() {
         for (int i = 0; i < m_FitnessCalls; i++) {
@@ -135,13 +141,14 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
             }
             this.m_FitnessCallsNeeded = i;
             if (this.m_Best.defaultEvaulateAsMiniBits() == 0) {
-                i = this.m_FitnessCalls +1;
+                i = this.m_FitnessCalls + 1;
             }
         }
     }
 
-    /** This main method will start a simple hillclimber.
-     * No arguments necessary.
+    /**
+     * This main method will start a simple hillclimber. No arguments necessary.
+     *
      * @param args
      */
     public static void main(String[] args) {
@@ -155,36 +162,43 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
         }
         TmpMeanCalls /= program.m_MultiRuns;
         TmpMeanFitness /= program.m_MultiRuns;
-        System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
+        System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
     }
 
-    /** This method allows you to add the LectureGUI as listener to the Optimizer
+    /**
+     * This method allows you to add the LectureGUI as listener to the Optimizer
+     *
      * @param ea
      */
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    /** Something has changed
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -192,64 +206,72 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
         String result = "";
         result += "Monte-Carlo Search:\n";
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "The Monte Carlo Search repeatively creates random individuals and stores the best individuals found.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
     public String getName() {
         return "MCS";
     }
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Change the number of best individuals stored.";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
 }

src/eva2/server/go/strategies/MultiObjectiveCMAES.java

@@ -21,401 +21,386 @@ import java.util.HashMap;
  */
 public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
 
-	/**
-	 * Generated serial version identifier
-	 */
-	private static final long serialVersionUID = 1L;
+    /**
+     * Generated serial version identifier
+     */
+    private static final long serialVersionUID = 1L;
 
-	/**
-	 * 
-	 * @author mkron
-	 *
-	 */
-	class CounterClass {
-		public CounterClass(int i) {
-			value = i;
-		}
+    /**
+     *
+     * @author mkron
+     *
+     */
+    class CounterClass {
 
-		public int value;
-		public boolean seen = false;
-	}
+        public CounterClass(int i) {
+            value = i;
+        }
+        public int value;
+        public boolean seen = false;
+    }
+    private String m_Identifier = "MOCMAES";
+    private Population m_Population;
+    private AbstractOptimizationProblem m_Problem;
+    transient private InterfacePopulationChangedEventListener m_Listener;
+    private int m_lambda = 1;
+    private int m_lambdamo = 1;
 
-	private String m_Identifier = "MOCMAES";
+    public MultiObjectiveCMAES() {
+        m_Population = new Population(m_lambdamo);
+    }
 
-	private Population m_Population;
-	private AbstractOptimizationProblem m_Problem;
-
-	transient private InterfacePopulationChangedEventListener m_Listener;
-
-	private int m_lambda = 1;
-	private int m_lambdamo = 1;
-
-	public MultiObjectiveCMAES() {
-		m_Population = new Population(m_lambdamo);
-	}
-
-	public MultiObjectiveCMAES(MultiObjectiveCMAES a) {
-		m_Problem = (AbstractOptimizationProblem) a.m_Problem.clone();
-		setPopulation((Population) a.m_Population.clone());
-		m_lambda = a.m_lambda;
-	}
+    public MultiObjectiveCMAES(MultiObjectiveCMAES a) {
+        m_Problem = (AbstractOptimizationProblem) a.m_Problem.clone();
+        setPopulation((Population) a.m_Population.clone());
+        m_lambda = a.m_lambda;
+    }
 
     @Override
-	public MultiObjectiveCMAES clone() {
-		return new MultiObjectiveCMAES(this);
-	}
+    public MultiObjectiveCMAES clone() {
+        return new MultiObjectiveCMAES(this);
+    }
 
-	public void hideHideable() {
-		GenericObjectEditor
-				.setHideProperty(this.getClass(), "population", true);
-	}
+    public void hideHideable() {
+        GenericObjectEditor
+                .setHideProperty(this.getClass(), "population", true);
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see
-	 * eva2.server.go.strategies.InterfaceOptimizer#setIdentifier(java.lang.
-	 * String)
-	 */
+    /*
+     * (non-Javadoc)
+     * 
+     * @see
+     * eva2.server.go.strategies.InterfaceOptimizer#setIdentifier(java.lang.
+     * String)
+     */
     @Override
-	public void setIdentifier(String name) {
-		m_Identifier = name;
-	}
+    public void setIdentifier(String name) {
+        m_Identifier = name;
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see
-	 * eva2.server.go.strategies.InterfaceOptimizer#SetProblem(eva2.server.go
-	 * .problems.InterfaceOptimizationProblem)
-	 */
+    /*
+     * (non-Javadoc)
+     * 
+     * @see
+     * eva2.server.go.strategies.InterfaceOptimizer#SetProblem(eva2.server.go
+     * .problems.InterfaceOptimizationProblem)
+     */
     @Override
-	public void setProblem(InterfaceOptimizationProblem problem) {
-		m_Problem = (AbstractOptimizationProblem) problem;
-	}
+    public void setProblem(InterfaceOptimizationProblem problem) {
+        m_Problem = (AbstractOptimizationProblem) problem;
+    }
 
-	/**
-	 * This method allows you to add the LectureGUI as listener to the Optimizer
-	 * 
-	 * @param ea
-	 */
+    /**
+     * This method allows you to add the LectureGUI as listener to the Optimizer
+     *
+     * @param ea
+     */
     @Override
-	public void addPopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		this.m_Listener = ea;
-	}
+    public void addPopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        this.m_Listener = ea;
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see eva2.server.go.strategies.InterfaceOptimizer#freeWilly()
-	 */
+    /*
+     * (non-Javadoc)
+     * 
+     * @see eva2.server.go.strategies.InterfaceOptimizer#getAllSolutions()
+     */
     @Override
-	public void freeWilly() {
-	}
+    public InterfaceSolutionSet getAllSolutions() {
+        Population pop = getPopulation();
+        return new SolutionSet(pop, pop);
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see eva2.server.go.strategies.InterfaceOptimizer#getAllSolutions()
-	 */
+    /*
+     * (non-Javadoc)
+     * 
+     * @see eva2.server.go.strategies.InterfaceOptimizer#getIdentifier()
+     */
     @Override
-	public InterfaceSolutionSet getAllSolutions() {
-		Population pop = getPopulation();
-		return new SolutionSet(pop, pop);
-	}
+    public String getIdentifier() {
+        return m_Identifier;
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see eva2.server.go.strategies.InterfaceOptimizer#getIdentifier()
-	 */
+    /*
+     * (non-Javadoc)
+     * 
+     * @see eva2.server.go.strategies.InterfaceOptimizer#getName()
+     */
     @Override
-	public String getIdentifier() {
-		return m_Identifier;
-	}
+    public String getName() {
+        return "(1+" + m_lambda + ") MO-CMA-ES";
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see eva2.server.go.strategies.InterfaceOptimizer#getName()
-	 */
+    public static String globalInfo() {
+        return "A multi-objective CMA-ES variant after Igel, Hansen and Roth 2007 (EC 15(1),1-28).";
+    }
+
+    /*
+     * (non-Javadoc)
+     * 
+     * @see eva2.server.go.strategies.InterfaceOptimizer#getPopulation()
+     */
     @Override
-	public String getName() {
-		return "(1+" + m_lambda + ") MO-CMA-ES";
-	}
+    public Population getPopulation() {
+        return m_Population;
+    }
 
-	public static String globalInfo() {
-		return "A multi-objective CMA-ES variant after Igel, Hansen and Roth 2007 (EC 15(1),1-28).";
-	}
-
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see eva2.server.go.strategies.InterfaceOptimizer#getPopulation()
-	 */
+    /*
+     * (non-Javadoc)
+     * 
+     * @see eva2.server.go.strategies.InterfaceOptimizer#getProblem()
+     */
     @Override
-	public Population getPopulation() {
-		return m_Population;
-	}
+    public InterfaceOptimizationProblem getProblem() {
+        return m_Problem;
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see eva2.server.go.strategies.InterfaceOptimizer#getProblem()
-	 */
+    /*
+     * (non-Javadoc)
+     * 
+     * @see
+     * eva2.server.go.strategies.InterfaceOptimizer#getStringRepresentation()
+     */
     @Override
-	public InterfaceOptimizationProblem getProblem() {
-		return m_Problem;
-	}
+    public String getStringRepresentation() {
+        StringBuilder strB = new StringBuilder(200);
+        strB.append("(1+" + m_lambda + ") MO-CMA-ES:\nOptimization Problem: ");
+        strB.append(this.m_Problem.getStringRepresentationForProblem(this));
+        strB.append("\n");
+        strB.append(this.m_Population.getStringRepresentation());
+        return strB.toString();
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see
-	 * eva2.server.go.strategies.InterfaceOptimizer#getStringRepresentation()
-	 */
+    /*
+     * (non-Javadoc)
+     * 
+     * @see eva2.server.go.strategies.InterfaceOptimizer#init()
+     */
     @Override
-	public String getStringRepresentation() {
-		StringBuilder strB = new StringBuilder(200);
-		strB.append("(1+" + m_lambda + ") MO-CMA-ES:\nOptimization Problem: ");
-		strB.append(this.m_Problem.getStringRepresentationForProblem(this));
-		strB.append("\n");
-		strB.append(this.m_Population.getStringRepresentation());
-		return strB.toString();
-	}
+    public void init() {
+        // initByPopulation(m_Population, true);
+        this.m_Population.setTargetSize(m_lambdamo);
+        this.m_Problem.initPopulation(this.m_Population);
+        // children = new Population(m_Population.size());
+        this.evaluatePopulation(this.m_Population);
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see eva2.server.go.strategies.InterfaceOptimizer#init()
-	 */
+    }
+
+    /*
+     * (non-Javadoc)
+     * 
+     * @see
+     * eva2.server.go.strategies.InterfaceOptimizer#initByPopulation(eva2.server
+     * .go.populations.Population, boolean)
+     */
     @Override
-	public void init() {
-		// initByPopulation(m_Population, true);
-		this.m_Population.setTargetSize(m_lambdamo);
-		this.m_Problem.initPopulation(this.m_Population);
-		// children = new Population(m_Population.size());
-		this.evaluatePopulation(this.m_Population);
-		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+    public void initByPopulation(Population pop, boolean reset) {
+        setPopulation(pop);
+        if (reset) {
+            m_Problem.initPopulation(m_Population);
+            m_Problem.evaluate(m_Population);
 
-	}
+        }
+    }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see
-	 * eva2.server.go.strategies.InterfaceOptimizer#initByPopulation(eva2.server
-	 * .go.populations.Population, boolean)
-	 */
+    /**
+     * This method will evaluate the current population using the given problem.
+     *
+     * @param population The population that is to be evaluated
+     */
+    private void evaluatePopulation(Population population) {
+        this.m_Problem.evaluate(population);
+    }
+
+    /*
+     * (non-Javadoc)
+     * 
+     * @see eva2.server.go.strategies.InterfaceOptimizer#optimize()
+     */
     @Override
-	public void initByPopulation(Population pop, boolean reset) {
-		setPopulation(pop);
-		if (reset) {
-			m_Problem.initPopulation(m_Population);
-			m_Problem.evaluate(m_Population);
+    public void optimize() {
 
-		}
-	}
+        HashMap<Long, CounterClass> SuccessCounterMap = new HashMap<Long, CounterClass>();
 
-	/**
-	 * This method will evaluate the current population using the given problem.
-	 * 
-	 * @param population
-	 *            The population that is to be evaluated
-	 */
-	private void evaluatePopulation(Population population) {
-		this.m_Problem.evaluate(population);
-	}
+        // Eltern markieren und f<>r die Z<>hlung vorbereiten
+        for (int j = 0; j < m_lambdamo && j < m_Population.size(); j++) {
+            m_Population.getEAIndividual(j).putData("Parent",
+                    m_Population.getEAIndividual(j));
+            SuccessCounterMap.put(m_Population.getEAIndividual(j).getIndyID(),
+                    new CounterClass(0));
+        }
 
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see eva2.server.go.strategies.InterfaceOptimizer#optimize()
-	 */
-    @Override
-	public void optimize() {
+        // Kinder erzeugen
+        Population children = new Population(m_lambdamo * m_lambda);
+        children.setGenerationTo(m_Population.getGeneration());
 
-		HashMap<Long, CounterClass> SuccessCounterMap = new HashMap<Long, CounterClass>();
+        for (int j = 0; j < children.getTargetSize(); j++) {
+            AbstractEAIndividual parent = m_Population.getEAIndividual(j
+                    % m_lambdamo);
+            AbstractEAIndividual indy = (AbstractEAIndividual) parent.clone();
+            indy.mutate();
+            indy.putData("Parent", parent);
+            children.add(indy);
+        }
+        evaluatePopulation(children);
 
-		// Eltern markieren und f<>r die Z<>hlung vorbereiten
-		for (int j = 0; j < m_lambdamo && j < m_Population.size(); j++) {
-			m_Population.getEAIndividual(j).putData("Parent",
-					m_Population.getEAIndividual(j));
-			SuccessCounterMap.put(m_Population.getEAIndividual(j).getIndyID(),
-					new CounterClass(0));
-		}
+        m_Population.addPopulation(children);
+        // Ranking
+        ArchivingNSGAII dummyArchive = new ArchivingNSGAIISMeasure();
+        Population[] store = dummyArchive
+                .getNonDominatedSortedFronts(m_Population);
+        store = dummyArchive.getNonDominatedSortedFronts(m_Population);
+        dummyArchive.calculateCrowdingDistance(store);
 
-		// Kinder erzeugen
-		Population children = new Population(m_lambdamo * m_lambda);
-		children.setGenerationTo(m_Population.getGeneration());
+        // Vergleichen und den Successcounter hochz<68>hlen wenn wir besser als
+        // unser Elter sind
+        for (int j = 0; j < m_Population.size(); j++) {
+            AbstractEAIndividual parent = (AbstractEAIndividual) m_Population
+                    .getEAIndividual(j).getData("Parent");
+            if (m_Population.getEAIndividual(j) != parent) { // Eltern nicht mit
+                // sich selber
+                // vergleichen
+                int parentParetoLevel = ((Integer) parent
+                        .getData("ParetoLevel")).intValue();
+                double parentSMeasure = ((Double) parent.getData("HyperCube"))
+                        .doubleValue();
+                int childParetoLevel = ((Integer) m_Population.getEAIndividual(
+                        j).getData("ParetoLevel")).intValue();
+                double childSMeasure = ((Double) m_Population
+                        .getEAIndividual(j).getData("HyperCube")).doubleValue();
+                if (childParetoLevel < parentParetoLevel
+                        || ((childParetoLevel == parentParetoLevel) && childSMeasure > parentSMeasure)) {
+                    SuccessCounterMap.get(parent.getIndyID()).value++;
+                }
+            } else { // Debug
 
-		for (int j = 0; j < children.getTargetSize(); j++) {
-			AbstractEAIndividual parent = m_Population.getEAIndividual(j
-					% m_lambdamo);
-			AbstractEAIndividual indy = (AbstractEAIndividual) parent.clone();
-			indy.mutate();
-			indy.putData("Parent", parent);
-			children.add(indy);
-		}
-		evaluatePopulation(children);
-
-		m_Population.addPopulation(children);
-		// Ranking
-		ArchivingNSGAII dummyArchive = new ArchivingNSGAIISMeasure();
-		Population[] store = dummyArchive
-				.getNonDominatedSortedFronts(m_Population);
-		store = dummyArchive.getNonDominatedSortedFronts(m_Population);
-		dummyArchive.calculateCrowdingDistance(store);
-
-		// Vergleichen und den Successcounter hochz<68>hlen wenn wir besser als
-		// unser Elter sind
-		for (int j = 0; j < m_Population.size(); j++) {
-			AbstractEAIndividual parent = (AbstractEAIndividual) m_Population
-					.getEAIndividual(j).getData("Parent");
-			if (m_Population.getEAIndividual(j) != parent) { // Eltern nicht mit
-				// sich selber
-				// vergleichen
-				int parentParetoLevel = ((Integer) parent
-						.getData("ParetoLevel")).intValue();
-				double parentSMeasure = ((Double) parent.getData("HyperCube"))
-						.doubleValue();
-				int childParetoLevel = ((Integer) m_Population.getEAIndividual(
-						j).getData("ParetoLevel")).intValue();
-				double childSMeasure = ((Double) m_Population
-						.getEAIndividual(j).getData("HyperCube")).doubleValue();
-				if (childParetoLevel < parentParetoLevel
-						|| ((childParetoLevel == parentParetoLevel) && childSMeasure > parentSMeasure)) {
-					SuccessCounterMap.get(parent.getIndyID()).value++;
-				}
-			} else { // Debug
-
-				SuccessCounterMap.get(parent.getIndyID()).seen = true;
-			}
-		}
-
-		// Selection
-		m_Population.clear();
-		for (int i = 0; i < store.length; i++) {
-			if (m_Population.size() + store[i].size() <= m_lambdamo) { // Die
-				// Front
-				// passt
-				// noch
-				// komplett
-				m_Population.addPopulation(store[i]);
-
-			} else { // die besten aus der aktuellen Front heraussuchen bis voll
-				while (store[i].size() > 0 && m_Population.size() < m_lambdamo) {
-					AbstractEAIndividual indy = store[i].getEAIndividual(0);
-					double bestMeasure = ((Double) indy.getData("HyperCube"))
-							.doubleValue(); // TODO mal noch effizient machen
-					// (sortieren und die besten n
-					// herausholen)
-					for (int j = 1; j < store[i].size(); j++) {
-						if (bestMeasure < ((Double) store[i].getEAIndividual(j)
-								.getData("HyperCube")).doubleValue()) {
-							bestMeasure = ((Double) store[i].getEAIndividual(j)
-									.getData("HyperCube")).doubleValue();
-							indy = store[i].getEAIndividual(j);
-						}
-					}
-					m_Population.add(indy);
-					store[i].removeMember(indy);
-				}
-			}
-
-		}
-
-		// Strategieparemeter updaten
-		for (int j = 0; j < m_Population.size(); j++) {
-
-			AbstractEAIndividual indy = m_Population.getEAIndividual(j);
-			if (indy.getMutationOperator() instanceof MutateESCovarianceMatrixAdaptionPlus) { // Das
-				// geht
-				// nur
-				// wenn
-				// wir
-				// auch
-				// die
-				// richtige
-				// Mutation
-				// haben
-				AbstractEAIndividual parent = (AbstractEAIndividual) indy
-						.getData("Parent");
-				MutateESCovarianceMatrixAdaptionPlus muta = (MutateESCovarianceMatrixAdaptionPlus) indy
-						.getMutationOperator();
-				double rate = ((double) SuccessCounterMap.get(parent
-						.getIndyID()).value)
-						/ ((double) m_lambda);
-
-				if (indy != parent) {
-					muta.updateCovariance();
-				}
-				muta.updateStepSize(rate);
-			}
-		}
-
-		for (int j = 0; j < children.size(); j++) {
-			children.getEAIndividual(j).putData("Parent", null);
-		}
-
-		m_Population.incrFunctionCallsBy(children.size());
-		m_Population.incrGeneration();
-		this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-
-	}
-
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @seeeva2.server.go.strategies.InterfaceOptimizer#
-	 * removePopulationChangedEventListener
-	 * (eva2.server.go.InterfacePopulationChangedEventListener)
-	 */
-    @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		return false;
-	}
-
-	/*
-	 * (non-Javadoc)
-	 * 
-	 * @see
-	 * eva2.server.go.strategies.InterfaceOptimizer#setPopulation(eva2.server
-	 * .go.populations.Population)
-	 */
-    @Override
-	public void setPopulation(Population pop) {
-		m_Population = pop;
-		m_Population.setNotifyEvalInterval(1);
-		m_lambdamo = m_Population.getTargetSize();
-
-	}
-
-	/**
-	 * Something has changed
-	 * 
-	 * @param name
-	 */
-	protected void firePropertyChangedEvent(String name) {
-		if (this.m_Listener != null) {
-                this.m_Listener.registerPopulationStateChanged(this, name);
+                SuccessCounterMap.get(parent.getIndyID()).seen = true;
             }
-	}
+        }
 
-	public int getLambda() {
-		return m_lambda;
-	}
+        // Selection
+        m_Population.clear();
+        for (int i = 0; i < store.length; i++) {
+            if (m_Population.size() + store[i].size() <= m_lambdamo) { // Die
+                // Front
+                // passt
+                // noch
+                // komplett
+                m_Population.addPopulation(store[i]);
 
-	public void setLambda(int mLambda) {
-		m_lambda = mLambda;
-	}
+            } else { // die besten aus der aktuellen Front heraussuchen bis voll
+                while (store[i].size() > 0 && m_Population.size() < m_lambdamo) {
+                    AbstractEAIndividual indy = store[i].getEAIndividual(0);
+                    double bestMeasure = ((Double) indy.getData("HyperCube"))
+                            .doubleValue(); // TODO mal noch effizient machen
+                    // (sortieren und die besten n
+                    // herausholen)
+                    for (int j = 1; j < store[i].size(); j++) {
+                        if (bestMeasure < ((Double) store[i].getEAIndividual(j)
+                                .getData("HyperCube")).doubleValue()) {
+                            bestMeasure = ((Double) store[i].getEAIndividual(j)
+                                    .getData("HyperCube")).doubleValue();
+                            indy = store[i].getEAIndividual(j);
+                        }
+                    }
+                    m_Population.add(indy);
+                    store[i].removeMember(indy);
+                }
+            }
 
-	/*
-	 * public int getLambdaMo() { return m_lambdamo; }
-	 * 
-	 * public void setLambdaMo(int mLambda) { m_lambdamo = mLambda; }
-	 */
+        }
 
+        // Strategieparemeter updaten
+        for (int j = 0; j < m_Population.size(); j++) {
+
+            AbstractEAIndividual indy = m_Population.getEAIndividual(j);
+            if (indy.getMutationOperator() instanceof MutateESCovarianceMatrixAdaptionPlus) { // Das
+                // geht
+                // nur
+                // wenn
+                // wir
+                // auch
+                // die
+                // richtige
+                // Mutation
+                // haben
+                AbstractEAIndividual parent = (AbstractEAIndividual) indy
+                        .getData("Parent");
+                MutateESCovarianceMatrixAdaptionPlus muta = (MutateESCovarianceMatrixAdaptionPlus) indy
+                        .getMutationOperator();
+                double rate = ((double) SuccessCounterMap.get(parent
+                        .getIndyID()).value)
+                        / ((double) m_lambda);
+
+                if (indy != parent) {
+                    muta.updateCovariance();
+                }
+                muta.updateStepSize(rate);
+            }
+        }
+
+        for (int j = 0; j < children.size(); j++) {
+            children.getEAIndividual(j).putData("Parent", null);
+        }
+
+        m_Population.incrFunctionCallsBy(children.size());
+        m_Population.incrGeneration();
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+
+    }
+
+    /*
+     * (non-Javadoc)
+     * 
+     * @seeeva2.server.go.strategies.InterfaceOptimizer#
+     * removePopulationChangedEventListener
+     * (eva2.server.go.InterfacePopulationChangedEventListener)
+     */
+    @Override
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        return false;
+    }
+
+    /*
+     * (non-Javadoc)
+     * 
+     * @see
+     * eva2.server.go.strategies.InterfaceOptimizer#setPopulation(eva2.server
+     * .go.populations.Population)
+     */
+    @Override
+    public void setPopulation(Population pop) {
+        m_Population = pop;
+        m_Population.setNotifyEvalInterval(1);
+        m_lambdamo = m_Population.getTargetSize();
+
+    }
+
+    /**
+     * Something has changed
+     *
+     * @param name
+     */
+    protected void firePropertyChangedEvent(String name) {
+        if (this.m_Listener != null) {
+            this.m_Listener.registerPopulationStateChanged(this, name);
+        }
+    }
+
+    public int getLambda() {
+        return m_lambda;
+    }
+
+    public void setLambda(int mLambda) {
+        m_lambda = mLambda;
+    }
+
+    /*
+     * public int getLambdaMo() { return m_lambdamo; }
+     * 
+     * public void setLambdaMo(int mLambda) { m_lambdamo = mLambda; }
+     */
 }

src/eva2/server/go/strategies/MultiObjectiveEA.java

@@ -16,53 +16,42 @@ import eva2.server.go.problems.FM0Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
 /**
- * A generic framework for multi-objecitve optimization, you need
- * to specify an optimization strategy (like GA), an archiver and
- * an information retrival strategy. With this scheme you can realized:
- *	Vector Evaluated GA
- *	Random Weight GA
- *	Multiple Objective GA
- *	NSGA
- *	NSGA-II
- *	SPEA
- *	SPEA 2
- *	PESA
- *	PESA-II
- * In case you address a multi-objective optimization problem with a single-
- * objective optimizer instead of this MOEA, such an optimizer would randomly
- * toggle between the objective for each selection and thus explore at least
- * the extreme points of the objective space, but simpler methods like
- * random search or hill-climbing might even fail on that. 
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 05.06.2003
- * Time: 11:03:50
- * To change this template use Options | File Templates.
+ * A generic framework for multi-objecitve optimization, you need to specify an
+ * optimization strategy (like GA), an archiver and an information retrival
+ * strategy. With this scheme you can realized: Vector Evaluated GA Random
+ * Weight GA Multiple Objective GA NSGA NSGA-II SPEA SPEA 2 PESA PESA-II In case
+ * you address a multi-objective optimization problem with a single- objective
+ * optimizer instead of this MOEA, such an optimizer would randomly toggle
+ * between the objective for each selection and thus explore at least the
+ * extreme points of the objective space, but simpler methods like random search
+ * or hill-climbing might even fail on that. Created by IntelliJ IDEA. User:
+ * streiche Date: 05.06.2003 Time: 11:03:50 To change this template use Options
+ * | File Templates.
  */
 public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
 
-    private InterfaceOptimizer              m_Optimizer         = new GeneticAlgorithm();
-    private InterfaceArchiving              m_Archiver          = new ArchivingNSGAII();
-    private InterfaceInformationRetrieval   m_InformationRetrieval = new InformationRetrievalInserting();
-    private InterfaceOptimizationProblem    m_Problem           = new FM0Problem();
-    private String                          m_Identifier        = "";
+    private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm();
+    private InterfaceArchiving m_Archiver = new ArchivingNSGAII();
+    private InterfaceInformationRetrieval m_InformationRetrieval = new InformationRetrievalInserting();
+    private InterfaceOptimizationProblem m_Problem = new FM0Problem();
+    private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
 
     public MultiObjectiveEA() {
         this.m_Optimizer.getPopulation().setTargetSize(100);
-        ((GeneticAlgorithm)this.m_Optimizer).setParentSelection(new SelectMONonDominated());
-        ((GeneticAlgorithm)this.m_Optimizer).setPartnerSelection(new SelectMONonDominated());
+        ((GeneticAlgorithm) this.m_Optimizer).setParentSelection(new SelectMONonDominated());
+        ((GeneticAlgorithm) this.m_Optimizer).setPartnerSelection(new SelectMONonDominated());
     }
 
     public MultiObjectiveEA(MultiObjectiveEA a) {
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_Optimizer                    = (InterfaceOptimizer)a.m_Optimizer.clone();
-        this.m_Archiver                     = (InterfaceArchiving)a.m_Archiver.clone();
-        this.m_InformationRetrieval         = (InterfaceInformationRetrieval)a.m_InformationRetrieval.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_Optimizer = (InterfaceOptimizer) a.m_Optimizer.clone();
+        this.m_Archiver = (InterfaceArchiving) a.m_Archiver.clone();
+        this.m_InformationRetrieval = (InterfaceInformationRetrieval) a.m_InformationRetrieval.clone();
     }
 
     public MultiObjectiveEA(InterfaceOptimizer subOpt, InterfaceArchiving archiving, int archiveSize,
-    	    InterfaceInformationRetrieval infoRetrieval, AbstractOptimizationProblem problem) {
+            InterfaceInformationRetrieval infoRetrieval, AbstractOptimizationProblem problem) {
         setOptimizer(subOpt);
         setArchivingStrategy(archiving);
         setArchiveSize(archiveSize);
@@ -82,10 +71,11 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
@@ -94,7 +84,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** The optimize method will compute a 'improved' and evaluated population
+    /**
+     * The optimize method will compute a 'improved' and evaluated population
      */
     @Override
     public void optimize() {
@@ -144,7 +135,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
 
         double[][] fitness = new double[tmp.size()][];
         for (int i = 0; i < tmp.size(); i++) {
-            fitness[i] = ((AbstractEAIndividual)tmp.get(i)).getFitness();
+            fitness[i] = ((AbstractEAIndividual) tmp.get(i)).getFitness();
         }
         double[] minY, maxY;
         minY = fitness[0];
@@ -170,37 +161,42 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
     }
 
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
 
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
         this.m_Optimizer.setProblem(problem);
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -212,42 +208,44 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
         result += " Information Retrieval = " + this.m_InformationRetrieval.getClass().toString() + "\n";
         result += " Information Retrieval = " + this.getClass().toString() + "\n";
         result += " Optimizer             = " + this.m_Optimizer.getClass().toString() + "\n";
-        result += this.m_Optimizer.getStringRepresentation() +"\n";
+        result += this.m_Optimizer.getStringRepresentation() + "\n";
         //result += "=> The Optimization Problem: ";
         //result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
         //result += this.m_Population.getStringRepresentation();
         return result;
     }
 
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "This is a general Multi-objective Evolutionary Optimization Framework.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -255,69 +253,87 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
         return "MOEA";
     }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Optimizer.getPopulation();
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Optimizer.setPopulation(pop);
     }
+
     public String populationTipText() {
         return "Edit the properties of the Population used.";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation(), ArchivingNSGAII.getNonDominatedSortedFront(getPopulation().getArchive()).getSortedPop(new AbstractEAIndividualComparator(0)));
+        return new SolutionSet(getPopulation(), ArchivingNSGAII.getNonDominatedSortedFront(getPopulation().getArchive()).getSortedPop(new AbstractEAIndividualComparator(0)));
     }
 
-    /** This method allows you to set/get the optimizing technique to use.
+    /**
+     * This method allows you to set/get the optimizing technique to use.
+     *
      * @return The current optimizing method
      */
     public InterfaceOptimizer getOptimizer() {
         return this.m_Optimizer;
     }
-    public void setOptimizer(InterfaceOptimizer b){
+
+    public void setOptimizer(InterfaceOptimizer b) {
         this.m_Optimizer = b;
     }
+
     public String optimizerTipText() {
         return "Choose a population based optimizing technique to use.";
     }
 
-    /** This method allows you to set/get the archiving strategy to use.
+    /**
+     * This method allows you to set/get the archiving strategy to use.
+     *
      * @return The current optimizing method
      */
     public InterfaceArchiving getArchivingStrategy() {
         return this.m_Archiver;
     }
-    public void setArchivingStrategy(InterfaceArchiving b){
+
+    public void setArchivingStrategy(InterfaceArchiving b) {
         this.m_Archiver = b;
     }
+
     public String archivingStrategyTipText() {
         return "Choose the archiving strategy.";
     }
 
-
-    /** This method allows you to set/get the Information Retrieval strategy to use.
+    /**
+     * This method allows you to set/get the Information Retrieval strategy to
+     * use.
+     *
      * @return The current optimizing method
      */
     public InterfaceInformationRetrieval getInformationRetrieval() {
         return this.m_InformationRetrieval;
     }
-    public void setInformationRetrieval(InterfaceInformationRetrieval b){
+
+    public void setInformationRetrieval(InterfaceInformationRetrieval b) {
         this.m_InformationRetrieval = b;
     }
+
     public String informationRetrievalTipText() {
         return "Choose the Information Retrieval strategy.";
     }
 
-    /** This method allows you to set/get the size of the archive.
+    /**
+     * This method allows you to set/get the size of the archive.
+     *
      * @return The current optimizing method
      */
     public int getArchiveSize() {
@@ -328,7 +344,8 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
         }
         return archive.getTargetSize();
     }
-    public void setArchiveSize(int b){
+
+    public void setArchiveSize(int b) {
         Population archive = this.m_Optimizer.getPopulation().getArchive();
         if (archive == null) {
             archive = new Population();
@@ -336,6 +353,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
         }
         archive.setTargetSize(b);
     }
+
     public String archiveSizeTipText() {
         return "Choose the size of the archive.";
     }

src/eva2/server/go/strategies/NelderMeadSimplex.java

@@ -15,505 +15,497 @@ import java.io.Serializable;
 import java.util.Vector;
 
 /**
- * Nelder-Mead-Simplex does not guarantee an equal number of evaluations within each optimize call
- * because of the different step types.
- * Range check is now available by projection at the bounds.
+ * Nelder-Mead-Simplex does not guarantee an equal number of evaluations within
+ * each optimize call because of the different step types. Range check is now
+ * available by projection at the bounds.
  *
  * @author mkron
  *
  */
 public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, InterfacePopulationChangedEventListener {
 
-	private int populationSize = 100;
-	// simulating the generational cycle. Set rather small (eg 5) for use as local search, higher for global search (eg 50)
-	private int	generationCycle = 50;
+    private int populationSize = 100;
+    // simulating the generational cycle. Set rather small (eg 5) for use as local search, higher for global search (eg 50)
+    private int generationCycle = 50;
+    private int fitIndex = 0; // choose criterion for multi objective functions
+    private Population m_Population;
+    private AbstractOptimizationProblem m_Problem;
+    private transient Vector<InterfacePopulationChangedEventListener> m_Listener;
+    private String m_Identifier = "NelderMeadSimplex";
+    private boolean checkConstraints = true;
 
-	private int fitIndex = 0; // choose criterion for multi objective functions
+    public NelderMeadSimplex() {
+        setPopulation(new Population(populationSize));
+    }
 
-	private Population m_Population;
-	private AbstractOptimizationProblem m_Problem;
-	private transient Vector<InterfacePopulationChangedEventListener> m_Listener;
-	private String m_Identifier = "NelderMeadSimplex";
-	private boolean checkConstraints = true;
+    public NelderMeadSimplex(int popSize) {
+        populationSize = popSize;
+        setPopulation(new Population(populationSize));
+    }
 
-	public NelderMeadSimplex() {
-		setPopulation(new Population(populationSize));
-	}
-	
-	public NelderMeadSimplex(int popSize) {
-		populationSize=popSize;
-		setPopulation(new Population(populationSize));
-	}
-	
-	public NelderMeadSimplex(NelderMeadSimplex a) {
-		m_Problem = (AbstractOptimizationProblem)a.m_Problem.clone();
-		setPopulation((Population)a.m_Population.clone());
-		populationSize = a.populationSize;
-		generationCycle = a.generationCycle;
-		m_Identifier = a.m_Identifier;
-	}
+    public NelderMeadSimplex(NelderMeadSimplex a) {
+        m_Problem = (AbstractOptimizationProblem) a.m_Problem.clone();
+        setPopulation((Population) a.m_Population.clone());
+        populationSize = a.populationSize;
+        generationCycle = a.generationCycle;
+        m_Identifier = a.m_Identifier;
+    }
 
     @Override
-	public NelderMeadSimplex clone() {
-		return new NelderMeadSimplex(this);
-	}
+    public NelderMeadSimplex clone() {
+        return new NelderMeadSimplex(this);
+    }
 
     @Override
-	public void setIdentifier(String name) {
-		m_Identifier = name;
-	}
+    public void setIdentifier(String name) {
+        m_Identifier = name;
+    }
 
     @Override
-	public void setProblem(InterfaceOptimizationProblem problem) {
-		m_Problem = (AbstractOptimizationProblem)problem;
-	}
+    public void setProblem(InterfaceOptimizationProblem problem) {
+        m_Problem = (AbstractOptimizationProblem) problem;
+    }
 
-	public boolean setProblemAndPopSize(InterfaceOptimizationProblem problem) {
-		setProblem(problem);
-		if (m_Problem instanceof AbstractProblemDouble) {
-			setPopulationSize(((AbstractProblemDouble)problem).getProblemDimension()+1);
-			return true;
-		} else {
-			Object ret=BeanInspector.callIfAvailable(problem, "getProblemDimension", null);
-			if (ret!=null) {
-				setPopulationSize(((Integer)ret)+1);
-				return true;
-			}
-		}
-		return false;
-	}
+    public boolean setProblemAndPopSize(InterfaceOptimizationProblem problem) {
+        setProblem(problem);
+        if (m_Problem instanceof AbstractProblemDouble) {
+            setPopulationSize(((AbstractProblemDouble) problem).getProblemDimension() + 1);
+            return true;
+        } else {
+            Object ret = BeanInspector.callIfAvailable(problem, "getProblemDimension", null);
+            if (ret != null) {
+                setPopulationSize(((Integer) ret) + 1);
+                return true;
+            }
+        }
+        return false;
+    }
 
     @Override
-	public void addPopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (ea!=null) {
-			if (m_Listener == null) {
-                        m_Listener = new Vector<InterfacePopulationChangedEventListener>();
-                    }
-			if (!m_Listener.contains(ea)) {
-                        m_Listener.add(ea);
-                    }
-		}
-	}
+    public void addPopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (ea != null) {
+            if (m_Listener == null) {
+                m_Listener = new Vector<InterfacePopulationChangedEventListener>();
+            }
+            if (!m_Listener.contains(ea)) {
+                m_Listener.add(ea);
+            }
+        }
+    }
 
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==null) {
-                                return false;
-                            }
-		else {
-                                return m_Listener.remove(ea);
-                            }
-	}
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == null) {
+            return false;
+        } else {
+            return m_Listener.remove(ea);
+        }
+    }
 
-    @Override
-	public void freeWilly() {}
-
-	protected double[] calcChallengeVect(double[] centroid, double[] refX) {
-		double[] r = new double[centroid.length];
-		for (int i=0; i<r.length; i++) {
-            r[i] = 2*centroid[i] - refX[i];
+    protected double[] calcChallengeVect(double[] centroid, double[] refX) {
+        double[] r = new double[centroid.length];
+        for (int i = 0; i < r.length; i++) {
+            r[i] = 2 * centroid[i] - refX[i];
         }
 //		double alpha = 1.3;
 //		for (int i=0; i<r.length; i++) r[i] = centroid[i] + alpha*(centroid[i] - refX[i]);
-		return r;
-	}
+        return r;
+    }
 
-	protected boolean firstIsBetter(double[] first, double[] second) {
-		return first[fitIndex]<second[fitIndex];
-	}
+    protected boolean firstIsBetter(double[] first, double[] second) {
+        return first[fitIndex] < second[fitIndex];
+    }
 
-	protected boolean firstIsBetterEqual(double[] first, double[] second) {
-		return first[fitIndex]<=second[fitIndex];
-	}
+    protected boolean firstIsBetterEqual(double[] first, double[] second) {
+        return first[fitIndex] <= second[fitIndex];
+    }
 
-	protected boolean firstIsBetter(AbstractEAIndividual first, AbstractEAIndividual second) {
-		return firstIsBetter(first.getFitness(), second.getFitness());
-	}
+    protected boolean firstIsBetter(AbstractEAIndividual first, AbstractEAIndividual second) {
+        return firstIsBetter(first.getFitness(), second.getFitness());
+    }
 
-	protected boolean firstIsBetterEqual(AbstractEAIndividual first, AbstractEAIndividual second) {
-		return firstIsBetterEqual(first.getFitness(), second.getFitness());
-	}
+    protected boolean firstIsBetterEqual(AbstractEAIndividual first, AbstractEAIndividual second) {
+        return firstIsBetterEqual(first.getFitness(), second.getFitness());
+    }
 
-	public AbstractEAIndividual simplexStep(Population subpop) {
-		// parameter
+    public AbstractEAIndividual simplexStep(Population subpop) {
+        // parameter
 
-		// hole die n-1 besten individuen der fitness dimension fitIndex
-		subpop.setSortingFitnessCriterion(fitIndex);
-		Population bestpop = subpop.getBestNIndividuals(subpop.size()-1, fitIndex);
-		// und das schlechteste
-		AbstractEAIndividual worst = subpop.getWorstEAIndividual(fitIndex);
-		AbstractEAIndividual best=subpop.getBestEAIndividual(fitIndex);
+        // hole die n-1 besten individuen der fitness dimension fitIndex
+        subpop.setSortingFitnessCriterion(fitIndex);
+        Population bestpop = subpop.getBestNIndividuals(subpop.size() - 1, fitIndex);
+        // und das schlechteste
+        AbstractEAIndividual worst = subpop.getWorstEAIndividual(fitIndex);
+        AbstractEAIndividual best = subpop.getBestEAIndividual(fitIndex);
 
-		double[][] range = ((InterfaceDataTypeDouble) worst).getDoubleRange();
-		double[] x_worst = ((InterfaceDataTypeDouble) worst).getDoubleData();
-		int dim = x_worst.length;
+        double[][] range = ((InterfaceDataTypeDouble) worst).getDoubleRange();
+        double[] x_worst = ((InterfaceDataTypeDouble) worst).getDoubleData();
+        int dim = x_worst.length;
 
-		// Centroid berechnen
-		double[] centroid = new double[dim];
-		for (int i=0; i<bestpop.size(); i++) {
-			for (int j=0; j<dim; j++) {
-				AbstractEAIndividual bestIndi= (AbstractEAIndividual) bestpop.getIndividual(i);
-				double[] bestIndyPos = ((InterfaceDataTypeDouble)bestIndi).getDoubleDataWithoutUpdate();
-				centroid[j] +=bestIndyPos[j]/bestpop.size(); // bug?
-			}
-		}
+        // Centroid berechnen
+        double[] centroid = new double[dim];
+        for (int i = 0; i < bestpop.size(); i++) {
+            for (int j = 0; j < dim; j++) {
+                AbstractEAIndividual bestIndi = (AbstractEAIndividual) bestpop.getIndividual(i);
+                double[] bestIndyPos = ((InterfaceDataTypeDouble) bestIndi).getDoubleDataWithoutUpdate();
+                centroid[j] += bestIndyPos[j] / bestpop.size(); // bug?
+            }
+        }
 
-		// Reflection
-		double[] r = calcChallengeVect(centroid, x_worst);
+        // Reflection
+        double[] r = calcChallengeVect(centroid, x_worst);
 
-		if (checkConstraints) {
-			if (!Mathematics.isInRange(r, range)) {
-				Mathematics.reflectBounds(r, range);
-			}
-		}
+        if (checkConstraints) {
+            if (!Mathematics.isInRange(r, range)) {
+                Mathematics.reflectBounds(r, range);
+            }
+        }
 //		AbstractEAIndividual reflectedInd = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone(); 
 //		((InterfaceDataTypeDouble)reflectedInd).SetDoubleGenotype(r);
 //
 //		m_Problem.evaluate(reflectedInd);
-		AbstractEAIndividual reflectedInd = createEvalIndy(bestpop, r);
-		this.m_Population.incrFunctionCalls();
+        AbstractEAIndividual reflectedInd = createEvalIndy(bestpop, r);
+        this.m_Population.incrFunctionCalls();
 
-		if (firstIsBetter(best, reflectedInd) && firstIsBetter(reflectedInd, bestpop.getWorstEAIndividual(fitIndex))) {
-			return reflectedInd;
-		} else if (firstIsBetter(reflectedInd, best)) { //neues besser als bisher bestes => Expansion
-			double[] e = new double[dim];
-			for (int i=0; i<dim; i++) {
-                e[i] = 3*centroid[i] - 2*x_worst[i];
+        if (firstIsBetter(best, reflectedInd) && firstIsBetter(reflectedInd, bestpop.getWorstEAIndividual(fitIndex))) {
+            return reflectedInd;
+        } else if (firstIsBetter(reflectedInd, best)) { //neues besser als bisher bestes => Expansion
+            double[] e = new double[dim];
+            for (int i = 0; i < dim; i++) {
+                e[i] = 3 * centroid[i] - 2 * x_worst[i];
             }
-			if (checkConstraints && !Mathematics.isInRange(e, range)) {
-                        Mathematics.projectToRange(e, range);
-                    }
-			
-			AbstractEAIndividual e_ind = createEvalIndy(bestpop, e);
-			this.m_Population.incrFunctionCalls();
-			
-			if (firstIsBetter(e_ind, reflectedInd)) { //expandiertes ist besser als reflektiertes
-				return e_ind;
-			} else {
-				return reflectedInd;
-			}
-		} else if (firstIsBetterEqual(bestpop.getWorstEAIndividual(fitIndex), reflectedInd)) {
-			//kontrahiere da neues indi keine verbesserung brachte
-			double[] c = new double[dim];
-			for (int i=0; i<dim; i++) {
-                c[i] = 0.5*centroid[i] + 0.5*x_worst[i];
+            if (checkConstraints && !Mathematics.isInRange(e, range)) {
+                Mathematics.projectToRange(e, range);
+            }
+
+            AbstractEAIndividual e_ind = createEvalIndy(bestpop, e);
+            this.m_Population.incrFunctionCalls();
+
+            if (firstIsBetter(e_ind, reflectedInd)) { //expandiertes ist besser als reflektiertes
+                return e_ind;
+            } else {
+                return reflectedInd;
+            }
+        } else if (firstIsBetterEqual(bestpop.getWorstEAIndividual(fitIndex), reflectedInd)) {
+            //kontrahiere da neues indi keine verbesserung brachte
+            double[] c = new double[dim];
+            for (int i = 0; i < dim; i++) {
+                c[i] = 0.5 * centroid[i] + 0.5 * x_worst[i];
+            }
+            if (checkConstraints && !Mathematics.isInRange(c, range)) {
+                Mathematics.projectToRange(c, range);
             }
-			if (checkConstraints && !Mathematics.isInRange(c, range)) {
-                        Mathematics.projectToRange(c, range);
-                    }
 
 //			AbstractEAIndividual c_ind = (AbstractEAIndividual)((AbstractEAIndividual)bestpop.getIndividual(1)).clone(); 
 //			((InterfaceDataTypeDouble)c_ind).SetDoubleGenotype(c);
 //			m_Problem.evaluate(c_ind);
-			AbstractEAIndividual c_ind = createEvalIndy(bestpop, c);
-			this.m_Population.incrFunctionCalls();
-			if(firstIsBetterEqual(c_ind, worst)) {
-				return c_ind;
-			}
-		}
-		return null;
-	}
+            AbstractEAIndividual c_ind = createEvalIndy(bestpop, c);
+            this.m_Population.incrFunctionCalls();
+            if (firstIsBetterEqual(c_ind, worst)) {
+                return c_ind;
+            }
+        }
+        return null;
+    }
 
-	private AbstractEAIndividual createEvalIndy(Population pop, double[] newGenotype) {
-		AbstractEAIndividual e_ind = (AbstractEAIndividual)((AbstractEAIndividual)pop.getIndividual(1)).clone(); 
-		((InterfaceDataTypeDouble)e_ind).SetDoubleGenotype(newGenotype);
-		e_ind.resetConstraintViolation();
-		m_Problem.evaluate(e_ind);
-		if (e_ind.getFitness(0)<6000) {
-			m_Problem.evaluate(e_ind);
-		}
-		return e_ind;
-	}
+    private AbstractEAIndividual createEvalIndy(Population pop, double[] newGenotype) {
+        AbstractEAIndividual e_ind = (AbstractEAIndividual) ((AbstractEAIndividual) pop.getIndividual(1)).clone();
+        ((InterfaceDataTypeDouble) e_ind).SetDoubleGenotype(newGenotype);
+        e_ind.resetConstraintViolation();
+        m_Problem.evaluate(e_ind);
+        if (e_ind.getFitness(0) < 6000) {
+            m_Problem.evaluate(e_ind);
+        }
+        return e_ind;
+    }
 
     @Override
-	public String getIdentifier() {
-		return m_Identifier;
-	}
+    public String getIdentifier() {
+        return m_Identifier;
+    }
 
     @Override
-	public String getName() {
-		return m_Identifier;
-	}
+    public String getName() {
+        return m_Identifier;
+    }
 
-	public static String globalInfo() {
-		return "The Nelder-Mead simplex search algorithm for local search. Reflection on bounds may be used for constraint handling.";
-	}
+    public static String globalInfo() {
+        return "The Nelder-Mead simplex search algorithm for local search. Reflection on bounds may be used for constraint handling.";
+    }
 
     @Override
-	public Population getPopulation() {
-		return m_Population;
-	}
+    public Population getPopulation() {
+        return m_Population;
+    }
 
     @Override
-	public InterfaceOptimizationProblem getProblem() {
-		return m_Problem;
-	}
+    public InterfaceOptimizationProblem getProblem() {
+        return m_Problem;
+    }
 
     @Override
-	public String getStringRepresentation() {
-    	StringBuilder strB = new StringBuilder(200);
-    	strB.append("Nelder-Mead-Simplex Strategy:\nOptimization Problem: ");
+    public String getStringRepresentation() {
+        StringBuilder strB = new StringBuilder(200);
+        strB.append("Nelder-Mead-Simplex Strategy:\nOptimization Problem: ");
         strB.append(this.m_Problem.getStringRepresentationForProblem(this));
         strB.append("\n");
         strB.append(this.m_Population.getStringRepresentation());
         return strB.toString();
-	}
+    }
 
     @Override
-	public void init() {
-		initByPopulation(m_Population, true);
-	}
+    public void init() {
+        initByPopulation(m_Population, true);
+    }
 
     @Override
-	public void initByPopulation(Population pop, boolean reset) {
-		setPopulation(pop);
-		pop.addPopulationChangedEventListener(this);
-		if (reset) {
-			m_Problem.initPopulation(m_Population);
-			m_Problem.evaluate(m_Population);
-		}
+    public void initByPopulation(Population pop, boolean reset) {
+        setPopulation(pop);
+        pop.addPopulationChangedEventListener(this);
+        if (reset) {
+            m_Problem.initPopulation(m_Population);
+            m_Problem.evaluate(m_Population);
+        }
 //		fireNextGenerationPerformed();
-	}
+    }
 
-	private void fireNextGenerationPerformed() {
-		if (m_Listener != null) {
-			for (int i=0; i<m_Listener.size(); i++) {
+    private void fireNextGenerationPerformed() {
+        if (m_Listener != null) {
+            for (int i = 0; i < m_Listener.size(); i++) {
                 m_Listener.elementAt(i).registerPopulationStateChanged(this, Population.nextGenerationPerformed);
             }
-		}
-	}
+        }
+    }
 
     @Override
-	public void optimize() {
-		// make at least as many calls as there are individuals within the population.
-		// this simulates the generational loop expected by some other modules
-		int evalCntStart = m_Population.getFunctionCalls();
-		int evalsDone = 0;
-		m_Problem.evaluatePopulationStart(m_Population);
-		do {
-			AbstractEAIndividual ind = simplexStep(m_Population);
-			if(ind!=null){ //Verbesserung gefunden
-				double[] x=((InterfaceDataTypeDouble)ind).getDoubleData();
-				double[][] range = ((InterfaceDataTypeDouble)ind).getDoubleRange();
-				if (!Mathematics.isInRange(x, range)) {
-					System.err.println("WARNING: nelder mead step produced indy out of range!");
+    public void optimize() {
+        // make at least as many calls as there are individuals within the population.
+        // this simulates the generational loop expected by some other modules
+        int evalCntStart = m_Population.getFunctionCalls();
+        int evalsDone = 0;
+        m_Problem.evaluatePopulationStart(m_Population);
+        do {
+            AbstractEAIndividual ind = simplexStep(m_Population);
+            if (ind != null) { //Verbesserung gefunden
+                double[] x = ((InterfaceDataTypeDouble) ind).getDoubleData();
+                double[][] range = ((InterfaceDataTypeDouble) ind).getDoubleRange();
+                if (!Mathematics.isInRange(x, range)) {
+                    System.err.println("WARNING: nelder mead step produced indy out of range!");
 //					Mathematics.projectToRange(x, range);
 //					((InterfaceDataTypeDouble)ind).SetDoubleGenotype(x);
 //					m_Problem.evaluate(ind);
 //					this.m_Population.incrFunctionCalls();
-				}
-				m_Population.set(m_Population.getIndexOfWorstIndividualNoConstr(fitIndex), ind, fitIndex);
-			}else{//keine Verbesserung gefunden shrink!!
+                }
+                m_Population.set(m_Population.getIndexOfWorstIndividualNoConstr(fitIndex), ind, fitIndex);
+            } else {//keine Verbesserung gefunden shrink!!
 
-				double[] u_1 = ((InterfaceDataTypeDouble) m_Population.getBestEAIndividual(fitIndex)).getDoubleData();
+                double[] u_1 = ((InterfaceDataTypeDouble) m_Population.getBestEAIndividual(fitIndex)).getDoubleData();
 
-				for(int j=0;j<m_Population.size();j++){
-					double [] c= ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).getDoubleData();
-					for (int i=0; i<c.length; i++) {
-                        c[i] = 0.5*c[i] + 0.5*u_1[i];
+                for (int j = 0; j < m_Population.size(); j++) {
+                    double[] c = ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).getDoubleData();
+                    for (int i = 0; i < c.length; i++) {
+                        c[i] = 0.5 * c[i] + 0.5 * u_1[i];
                     }
-					((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).SetDoubleGenotype(c);
+                    ((InterfaceDataTypeDouble) m_Population.getEAIndividual(j)).SetDoubleGenotype(c);
 //					m_Population.getEAIndividual(j).resetConstraintViolation(); // not a good idea because during evaluation, a stats update may be performed which mustnt see indies which are evaluated, but possible constraints have been reset.
-				}
-				m_Problem.evaluate(m_Population);
-			}
-			evalsDone =  m_Population.getFunctionCalls() - evalCntStart;
-		} while (evalsDone < generationCycle);
-		m_Problem.evaluatePopulationEnd(m_Population);
-		this.m_Population.incrGeneration();
-	}
-
-    @Override
-	public void setPopulation(Population pop) {
-		m_Population = pop;
-		m_Population.addPopulationChangedEventListener(this);
-		m_Population.setNotifyEvalInterval(populationSize);
-	}
-
-    @Override
-	public InterfaceSolutionSet getAllSolutions() {
-		Population pop = getPopulation();
-		return new SolutionSet(pop, pop);
-	}
-
-	/**
-	 * @return the populationSize
-	 */
-	public int getPopulationSize() {
-		return populationSize;
-	}
-
-	/**
-	 * @param populationSize the populationSize to set
-	 */
-	public void setPopulationSize(int populationSize) {
-		this.populationSize = populationSize;
-		if (m_Population!=null) {
-			m_Population.setTargetSize(populationSize);
-			m_Population.setNotifyEvalInterval(m_Population.getTargetSize());
-		}
-	}
-	
-	public String populationSizeTipText() {
-		return "The population size should be adapted to the dimensions of the problem (e.g. n+1)";
-	}
-
-    @Override
-	public void registerPopulationStateChanged(Object source, String name) {
-		if (name.compareTo(Population.funCallIntervalReached) == 0) {
-			fireNextGenerationPerformed();
-		}// else System.err.println("unknown event!");
-	}
-	
-	/**
-	 * This method creates a Nelder-Mead instance.
-	 *
-	 * @param pop
-	 *          The size of the population
-	 * @param problem
-	 *          The problem to be optimized
-	 * @param listener
-	 * @return An optimization procedure that performs nelder mead optimization.
-	 */
-	public static final NelderMeadSimplex createNelderMeadSimplex(AbstractOptimizationProblem problem,
-	    InterfacePopulationChangedEventListener listener) {
-
-		problem.initProblem();
-		NelderMeadSimplex nms = new NelderMeadSimplex();
-		nms.setProblemAndPopSize(problem);
-		
-		if (listener!=null) {
-                    nms.addPopulationChangedEventListener(listener);
                 }
-		nms.init();
-
-		if (listener!=null) {
-                    listener.registerPopulationStateChanged(nms.getPopulation(), "");
-                }
-
-		return nms;
-	}
-
-	/**
-	 * This method creates a Nelder-Mead instance with an initial population 
-	 * around a given candidate solution. The population is created as a simplex with given 
-	 * perturbation ratio or randomly across the search range if the perturbation ratio is 
-	 * zero or below zero.
-	 * 
-	 *
-	 * @param problem
-	 *          The problem to be optimized
-	 * @param candidate starting point of the search
-	 * @param perturbationRatio perturbation ratio relative to the problem range for the initial simplex creation
-	 * @param listener
-	 * @return An optimization procedure that performs nelder mead optimization.
-	 */
-	public static final NelderMeadSimplex createNelderMeadSimplexLocal(AbstractOptimizationProblem problem,
-			AbstractEAIndividual candidate, double perturbationRatio,
-	    InterfacePopulationChangedEventListener listener) {
-
-		// TODO this method might be superfluous when using PostProcess
-		problem.initProblem();
-		NelderMeadSimplex nms = new NelderMeadSimplex();
-		nms.setProblemAndPopSize(problem);
-		
-		Population initialPop;
-		if (perturbationRatio <= 0) { // random case
-			initialPop = new Population(nms.getPopulationSize());
-			problem.initPopulation(initialPop);
-			initialPop.set(0, candidate);
-		} else { 
-			double[][] range = ((InterfaceDataTypeDouble)candidate).getDoubleRange();
-			if (range.length != nms.getPopulationSize()-1) {
-                        System.err.println("Unexpected population size for nelder mead!");
-                    }
-			initialPop = createNMSPopulation(candidate, perturbationRatio, range, true);
-		}
-		if (listener != null) {
-                    nms.addPopulationChangedEventListener(listener);
-                }
-		nms.initByPopulation(initialPop, false);
-		//nms.setPopulation(initialPop);
-
-		return nms;
-	}
-	
-	/**
-	 * From a given candidate solution, create n solutions around the candidate, where every i-th
-	 * new candidate differs in i dimensions by a distance of perturbRatio relative to the range in
-	 * that dimension (respecting the range). 
-	 * The new solutions are returned as a population, which, if includeCand is true,
-	 * also contains the initial candidate. However, the new candidates have not been evaluated.
-	 * 
-	 * @param candidate
-	 * @param perturbRelative
-	 * @param range
-	 * @param includeCand
-	 * @return
-	 */
-	public static Population createNMSPopulation(AbstractEAIndividual candidate, double perturbRelative, double[][] range, boolean includeCand) {
-		Population initPop = new Population();
-		if (includeCand) {
-                initPop.add(candidate);
+                m_Problem.evaluate(m_Population);
             }
-		if (perturbRelative >= 1. || (perturbRelative <= 0.)) {
-			System.err.println("Warning: perturbation ratio should lie between 0 and 1! (NelderMeadSimplex:createNMSPopulation)");
-		}
-		addPerturbedPopulation(perturbRelative, initPop, range, candidate);
-		return initPop;
-	}
+            evalsDone = m_Population.getFunctionCalls() - evalCntStart;
+        } while (evalsDone < generationCycle);
+        m_Problem.evaluatePopulationEnd(m_Population);
+        this.m_Population.incrGeneration();
+    }
 
-	private static void addPerturbedPopulation(double perturbationRatio,
-			Population initialPop, double[][] range, AbstractEAIndividual candidate) {
-		AbstractEAIndividual indy = (AbstractEAIndividual)candidate.clone();
-		// span by perturbation, every new individual i is modified in dimension i by
-		// a value of perturbRatio*range_i such that a simplex of relative side length perturbRatio is created. 
-		for (int i=0; i<range.length; i+=1) {
-			double curPerturb = ((range[i][1]-range[i][0])*perturbationRatio);
-			double[] dat = ((InterfaceDataTypeDouble)indy).getDoubleData();
-			if (dat[i]==range[i][1]) { // in this case the bound is said to be too close 
-				dat[i]=Math.max(dat[i]-curPerturb, range[i][0]);
-			} else {
-                        dat[i] = Math.min(dat[i]+curPerturb, range[i][1]);
-                    }
-			((InterfaceDataTypeDouble)indy).SetDoubleGenotype(dat);
-			indy.resetConstraintViolation();
-			initialPop.add((AbstractEAIndividual)indy.clone());
-		}
-		initialPop.synchSize();
-	}
+    @Override
+    public void setPopulation(Population pop) {
+        m_Population = pop;
+        m_Population.addPopulationChangedEventListener(this);
+        m_Population.setNotifyEvalInterval(populationSize);
+    }
 
-	/**
-	 * @param generationCycle the generationCycle to set
-	 */
-	public void setGenerationCycle(int generationCycle) {
-		this.generationCycle = generationCycle;
-	}
+    @Override
+    public InterfaceSolutionSet getAllSolutions() {
+        Population pop = getPopulation();
+        return new SolutionSet(pop, pop);
+    }
 
-	public boolean isCheckRange() {
-		return checkConstraints;
-	}
+    /**
+     * @return the populationSize
+     */
+    public int getPopulationSize() {
+        return populationSize;
+    }
 
-	public void setCheckRange(boolean checkRange) {
-		this.checkConstraints = checkRange;
-	}
+    /**
+     * @param populationSize the populationSize to set
+     */
+    public void setPopulationSize(int populationSize) {
+        this.populationSize = populationSize;
+        if (m_Population != null) {
+            m_Population.setTargetSize(populationSize);
+            m_Population.setNotifyEvalInterval(m_Population.getTargetSize());
+        }
+    }
 
-	public String checkRangeTipText() {
-		return "Mark to check range constraints by reflection/projection";
-	}
+    public String populationSizeTipText() {
+        return "The population size should be adapted to the dimensions of the problem (e.g. n+1)";
+    }
 
-	public int getCritIndex() {
-		return fitIndex;
-	}
+    @Override
+    public void registerPopulationStateChanged(Object source, String name) {
+        if (name.compareTo(Population.funCallIntervalReached) == 0) {
+            fireNextGenerationPerformed();
+        }// else System.err.println("unknown event!");
+    }
 
-	public void setCritIndex(int fitIndex) {
-		this.fitIndex = fitIndex;
-	}
+    /**
+     * This method creates a Nelder-Mead instance.
+     *
+     * @param pop The size of the population
+     * @param problem The problem to be optimized
+     * @param listener
+     * @return An optimization procedure that performs nelder mead optimization.
+     */
+    public static final NelderMeadSimplex createNelderMeadSimplex(AbstractOptimizationProblem problem,
+            InterfacePopulationChangedEventListener listener) {
 
-	public String critIndexTipText() {
-		return "For multi-criterial problems, set the index of the fitness to be used in 0..n-1. Default is 0";
-	}
+        problem.initProblem();
+        NelderMeadSimplex nms = new NelderMeadSimplex();
+        nms.setProblemAndPopSize(problem);
 
+        if (listener != null) {
+            nms.addPopulationChangedEventListener(listener);
+        }
+        nms.init();
+
+        if (listener != null) {
+            listener.registerPopulationStateChanged(nms.getPopulation(), "");
+        }
+
+        return nms;
+    }
+
+    /**
+     * This method creates a Nelder-Mead instance with an initial population
+     * around a given candidate solution. The population is created as a simplex
+     * with given perturbation ratio or randomly across the search range if the
+     * perturbation ratio is zero or below zero.
+     *
+     *
+     * @param problem The problem to be optimized
+     * @param candidate starting point of the search
+     * @param perturbationRatio perturbation ratio relative to the problem range
+     * for the initial simplex creation
+     * @param listener
+     * @return An optimization procedure that performs nelder mead optimization.
+     */
+    public static final NelderMeadSimplex createNelderMeadSimplexLocal(AbstractOptimizationProblem problem,
+            AbstractEAIndividual candidate, double perturbationRatio,
+            InterfacePopulationChangedEventListener listener) {
+
+        // TODO this method might be superfluous when using PostProcess
+        problem.initProblem();
+        NelderMeadSimplex nms = new NelderMeadSimplex();
+        nms.setProblemAndPopSize(problem);
+
+        Population initialPop;
+        if (perturbationRatio <= 0) { // random case
+            initialPop = new Population(nms.getPopulationSize());
+            problem.initPopulation(initialPop);
+            initialPop.set(0, candidate);
+        } else {
+            double[][] range = ((InterfaceDataTypeDouble) candidate).getDoubleRange();
+            if (range.length != nms.getPopulationSize() - 1) {
+                System.err.println("Unexpected population size for nelder mead!");
+            }
+            initialPop = createNMSPopulation(candidate, perturbationRatio, range, true);
+        }
+        if (listener != null) {
+            nms.addPopulationChangedEventListener(listener);
+        }
+        nms.initByPopulation(initialPop, false);
+        //nms.setPopulation(initialPop);
+
+        return nms;
+    }
+
+    /**
+     * From a given candidate solution, create n solutions around the candidate,
+     * where every i-th new candidate differs in i dimensions by a distance of
+     * perturbRatio relative to the range in that dimension (respecting the
+     * range). The new solutions are returned as a population, which, if
+     * includeCand is true, also contains the initial candidate. However, the
+     * new candidates have not been evaluated.
+     *
+     * @param candidate
+     * @param perturbRelative
+     * @param range
+     * @param includeCand
+     * @return
+     */
+    public static Population createNMSPopulation(AbstractEAIndividual candidate, double perturbRelative, double[][] range, boolean includeCand) {
+        Population initPop = new Population();
+        if (includeCand) {
+            initPop.add(candidate);
+        }
+        if (perturbRelative >= 1. || (perturbRelative <= 0.)) {
+            System.err.println("Warning: perturbation ratio should lie between 0 and 1! (NelderMeadSimplex:createNMSPopulation)");
+        }
+        addPerturbedPopulation(perturbRelative, initPop, range, candidate);
+        return initPop;
+    }
+
+    private static void addPerturbedPopulation(double perturbationRatio,
+            Population initialPop, double[][] range, AbstractEAIndividual candidate) {
+        AbstractEAIndividual indy = (AbstractEAIndividual) candidate.clone();
+        // span by perturbation, every new individual i is modified in dimension i by
+        // a value of perturbRatio*range_i such that a simplex of relative side length perturbRatio is created. 
+        for (int i = 0; i < range.length; i += 1) {
+            double curPerturb = ((range[i][1] - range[i][0]) * perturbationRatio);
+            double[] dat = ((InterfaceDataTypeDouble) indy).getDoubleData();
+            if (dat[i] == range[i][1]) { // in this case the bound is said to be too close 
+                dat[i] = Math.max(dat[i] - curPerturb, range[i][0]);
+            } else {
+                dat[i] = Math.min(dat[i] + curPerturb, range[i][1]);
+            }
+            ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(dat);
+            indy.resetConstraintViolation();
+            initialPop.add((AbstractEAIndividual) indy.clone());
+        }
+        initialPop.synchSize();
+    }
+
+    /**
+     * @param generationCycle the generationCycle to set
+     */
+    public void setGenerationCycle(int generationCycle) {
+        this.generationCycle = generationCycle;
+    }
+
+    public boolean isCheckRange() {
+        return checkConstraints;
+    }
+
+    public void setCheckRange(boolean checkRange) {
+        this.checkConstraints = checkRange;
+    }
+
+    public String checkRangeTipText() {
+        return "Mark to check range constraints by reflection/projection";
+    }
+
+    public int getCritIndex() {
+        return fitIndex;
+    }
+
+    public void setCritIndex(int fitIndex) {
+        this.fitIndex = fitIndex;
+    }
+
+    public String critIndexTipText() {
+        return "For multi-criterial problems, set the index of the fitness to be used in 0..n-1. Default is 0";
+    }
 }

src/eva2/server/go/strategies/ParticleFilterOptimization.java

@@ -1,6 +1,5 @@
 package eva2.server.go.strategies;
 
-
 import eva2.gui.BeanInspector;
 import eva2.gui.GenericObjectEditor;
 import eva2.gui.Plot;
@@ -22,38 +21,35 @@ import eva2.server.go.problems.InterfaceOptimizationProblem;
 /**
  * This is a Particle Filter implemented by Frank Senke, only some documentation
  * here and not completely checked whether this works on arbitrary problem
- * instances. MK did some adaptations, this should work on real valued problems now.
+ * instances. MK did some adaptations, this should work on real valued problems
+ * now.
  *
- * This is a implementation of Genetic Algorithms.
- * Copyright:       Copyright (c) 2003
- * Company:         University of Tuebingen, Computer Architecture
- * @author          Felix Streichert
- * @version:  $Revision: 307 $
- *            $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
- *            $Author: mkron $
+ * This is a implementation of Genetic Algorithms. Copyright: Copyright (c) 2003
+ * Company: University of Tuebingen, Computer Architecture
+ *
+ * @author Felix Streichert
+ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
+ * 2007) $ $Author: mkron $
  */
-
 public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.Serializable {
 
     /**
-	 * Comment for <code>serialVersionUID</code>
-	 */
-	private static final long serialVersionUID = 1L;
-	private Population                      m_Population        = new Population();
-    private InterfaceOptimizationProblem    m_Problem           = new F1Problem();
-    private InterfaceSelection              m_ParentSelection   = new SelectParticleWheel(0.5);
+     * Comment for
+     * <code>serialVersionUID</code>
+     */
+    private static final long serialVersionUID = 1L;
+    private Population m_Population = new Population();
+    private InterfaceOptimizationProblem m_Problem = new F1Problem();
+    private InterfaceSelection m_ParentSelection = new SelectParticleWheel(0.5);
     //private boolean							m_UseElitism		= true;
-
-    private String					m_Identifier = "";
-    private boolean 				withShow = false;
-    private double					mutationSigma = 0.01;
-    private double					randomImmigrationQuota = 0.05;
-    private double					initialVelocity = 0.02;
-    private double					rotationDeg = 20.;
-    private int						popSize = 300;
-
-    private int 					sleepTime = 0;
-    
+    private String m_Identifier = "";
+    private boolean withShow = false;
+    private double mutationSigma = 0.01;
+    private double randomImmigrationQuota = 0.05;
+    private double initialVelocity = 0.02;
+    private double rotationDeg = 20.;
+    private int popSize = 300;
+    private int sleepTime = 0;
     transient private int indCount = 0;
     transient private InterfacePopulationChangedEventListener m_Listener;
     transient Plot myPlot = null;
@@ -66,31 +62,31 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
     }
 
     public ParticleFilterOptimization(double vInit, double mute, double immiQuote, double rotDeg, double selScaling) {
-    	mutationSigma = mute;
-    	initialVelocity = vInit;
-    	randomImmigrationQuota = immiQuote;
-    	rotationDeg = rotDeg;
-    	m_ParentSelection = new SelectParticleWheel(selScaling);
+        mutationSigma = mute;
+        initialVelocity = vInit;
+        randomImmigrationQuota = immiQuote;
+        rotationDeg = rotDeg;
+        m_ParentSelection = new SelectParticleWheel(selScaling);
         if (withShow) {
             setWithShow(true);
         }
     }
 
     public ParticleFilterOptimization(ParticleFilterOptimization a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_Identifier                   = a.m_Identifier;
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_Identifier = a.m_Identifier;
         //this.m_Plague                       = a.m_Plague;
         //this.m_NumberOfPartners             = a.m_NumberOfPartners;
         //this.m_UseElitism                   = a.m_UseElitism;
-        this.m_ParentSelection              = (InterfaceSelection)a.m_ParentSelection.clone();
+        this.m_ParentSelection = (InterfaceSelection) a.m_ParentSelection.clone();
         if (a.withShow) {
             setWithShow(true);
         }
     }
 
     public void hideHideable() {
-    	GenericObjectEditor.setHideProperty(this.getClass(), "population", true);
+        GenericObjectEditor.setHideProperty(this.getClass(), "population", true);
     }
 
     @Override
@@ -103,12 +99,12 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
         //System.out.println("popsize is   " + m_Population.size());
         //System.out.println("pops targ is " + m_Population.getPopulationSize());
 
-    	if (initialVelocity <= 0.) {
-    		(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESFixedStepSize(mutationSigma));
-    	} else {
-    		(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESCorrVector(mutationSigma, initialVelocity, rotationDeg));
-    	}
-    	m_Population.setTargetSize(popSize);
+        if (initialVelocity <= 0.) {
+            (((AbstractOptimizationProblem) m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESFixedStepSize(mutationSigma));
+        } else {
+            (((AbstractOptimizationProblem) m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESCorrVector(mutationSigma, initialVelocity, rotationDeg));
+        }
+        m_Population.setTargetSize(popSize);
         this.m_Problem.initPopulation(this.m_Population);
 
         setWithShow(withShow);
@@ -117,22 +113,25 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
-        this.m_Population = (Population)pop.clone();
+        this.m_Population = (Population) pop.clone();
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.evaluatePopulation(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
-    /** This method will evaluate the current population using the
-     * given problem.
+    /**
+     * This method will evaluate the current population using the given problem.
+     *
      * @param population The population that is to be evaluated
      */
     private Population evaluatePopulation(Population population) {
@@ -141,45 +140,44 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
         return population;
     }
 
-	/**
-	 * This method will resample the given population using EA parent selection.
-	 * 
+    /**
+     * This method will resample the given population using EA parent selection.
+     *
      */
     protected Population resample(Population pop) {
         Population parents;
-        boolean doImmigr=false;
+        boolean doImmigr = false;
 
         this.m_ParentSelection.prepareSelection(pop);
 
         // Generate a Population of Parents with Parantselectionmethod.
         // DONT forget cloning -> selection does only shallow copies!
         int targetSize = this.m_Population.getTargetSize();
-        if (randomImmigrationQuota>0) {
-        	if (randomImmigrationQuota>1.) {
+        if (randomImmigrationQuota > 0) {
+            if (randomImmigrationQuota > 1.) {
                 System.err.println("Error, invalid immigration quota!");
+            } else {
+                targetSize = (int) (this.m_Population.getTargetSize() * (1. - randomImmigrationQuota));
+                targetSize = Math.max(1, targetSize); // guarantee at least one to be selected 
+                if (targetSize < this.m_Population.getTargetSize()) {
+                    doImmigr = true;
+                }
             }
-        	else {
-        		targetSize = (int)(this.m_Population.getTargetSize() * (1.-randomImmigrationQuota));
-        		targetSize = Math.max(1, targetSize); // guarantee at least one to be selected 
-        		if (targetSize < this.m_Population.getTargetSize()) {
-                        doImmigr=true;
-                    }
-        	}
         }
 
-        parents = (Population)(this.m_ParentSelection.selectFrom(pop, targetSize)).clone();
+        parents = (Population) (this.m_ParentSelection.selectFrom(pop, targetSize)).clone();
 
         if (doImmigr) {
-        	// add immigrants
-    		AbstractEAIndividual immi;
-    		int i;
-        	for (i=0; (i+parents.getTargetSize())<pop.getTargetSize(); i++) {
-        		immi = (AbstractEAIndividual)pop.getEAIndividual(0).clone();
-        		immi.init(getProblem());
-        		parents.add(immi);
-        	}
-        	parents.synchSize();
-        	if (TRACE) {
+            // add immigrants
+            AbstractEAIndividual immi;
+            int i;
+            for (i = 0; (i + parents.getTargetSize()) < pop.getTargetSize(); i++) {
+                immi = (AbstractEAIndividual) pop.getEAIndividual(0).clone();
+                immi.init(getProblem());
+                parents.add(immi);
+            }
+            parents.synchSize();
+            if (TRACE) {
                 System.out.println("Added " + i + " random individuals");
             }
         }
@@ -194,13 +192,13 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
     }
 
     protected void predict(Population pop) {
-    	indCount = 0;
-    	if (withShow) {
-    		drawPop(pop, 0, false);
-    	}
+        indCount = 0;
+        if (withShow) {
+            drawPop(pop, 0, false);
+        }
         for (int i = 0; i < pop.getTargetSize(); i++) {
-        	applyMotionModel((AbstractEAIndividual)((AbstractEAIndividual)pop.get(i)), 0.);
-        	indCount++;
+            applyMotionModel((AbstractEAIndividual) ((AbstractEAIndividual) pop.get(i)), 0.);
+            indCount++;
         }
         if (withShow) {
             drawPop(pop, 1, false);
@@ -208,20 +206,19 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
     }
 
     private void drawPop(Population pop, int graphLabel, boolean useCircles) {
-    	if (myPlot != null) {
-    		if (graphLabel < 0) {
+        if (myPlot != null) {
+            if (graphLabel < 0) {
                 graphLabel = indCount;
             }
-    		for (int i=0; i<pop.size(); i++) {
-    			InterfaceDataTypeDouble endy = (InterfaceDataTypeDouble) pop.getEAIndividual(i);
-    			double[] curPosition    = endy.getDoubleData();
+            for (int i = 0; i < pop.size(); i++) {
+                InterfaceDataTypeDouble endy = (InterfaceDataTypeDouble) pop.getEAIndividual(i);
+                double[] curPosition = endy.getDoubleData();
 
-    			if (useCircles) {
-                        myPlot.getFunctionArea().drawCircle("", curPosition, graphLabel);
-                    }
-    			else {
-                        myPlot.setUnconnectedPoint(curPosition[0], curPosition[1], graphLabel);
-                    }
+                if (useCircles) {
+                    myPlot.getFunctionArea().drawCircle("", curPosition, graphLabel);
+                } else {
+                    myPlot.setUnconnectedPoint(curPosition[0], curPosition[1], graphLabel);
+                }
 //    			myPlot.setConnectedPoint(curPosition[0], curPosition[1], graphLabel);
 //    			if ( !useCircles && (pop.getEAIndividual(i).hasData(MutateESCorrVector.vectorKey))) {
 //    				double[] v=(double[])pop.getEAIndividual(i).getData(MutateESCorrVector.vectorKey);
@@ -229,14 +226,14 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
 //    				curPosition=Mathematics.vvAdd(v, curPosition);
 //    				myPlot.setConnectedPoint(curPosition[0], curPosition[1], graphLabel+5);
 //    			}
-    		}
-    	}
-	}
+            }
+        }
+    }
 
-	protected void applyMotionModel(AbstractEAIndividual indy, double noise) {
-    	// this currently only performs a mutation
-    	indy.mutate();
-    	indy.resetFitness(0);
+    protected void applyMotionModel(AbstractEAIndividual indy, double noise) {
+        // this currently only performs a mutation
+        indy.mutate();
+        indy.resetFitness(0);
     }
 
     /**
@@ -251,8 +248,11 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
         // resample using selection
         nextGeneration = resample(m_Population);
 
-		if (sleepTime > 0 ) {
-            try { Thread.sleep(sleepTime); } catch(Exception e) {}
+        if (sleepTime > 0) {
+            try {
+                Thread.sleep(sleepTime);
+            } catch (Exception e) {
+            }
         }
         if (withShow) {
             clearPlot();
@@ -276,79 +276,89 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    protected void firePropertyChangedEvent (String name) {
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
-    	if (problem instanceof AbstractOptimizationProblem) {
-    		((AbstractOptimizationProblem)problem).informAboutOptimizer(this);
-    	}
+        if (problem instanceof AbstractOptimizationProblem) {
+            ((AbstractOptimizationProblem) problem).informAboutOptimizer(this);
+        }
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
     public String getStringRepresentation() {
-    	StringBuilder strB=new StringBuilder(200);
+        StringBuilder strB = new StringBuilder(200);
         strB.append("Particle Filter:\nOptimization Problem: ");
         strB.append(this.m_Problem.getStringRepresentationForProblem(this));
         strB.append("\n");
         strB.append(this.m_Population.getStringRepresentation());
         return strB.toString();
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "This is a Particle Filter Algorithm.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -356,158 +366,171 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
         return "PF";
     }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Edit the properties of the population used.";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
-    /** This method will set the selection method that is to be used
+
+    /**
+     * This method will set the selection method that is to be used
+     *
      * @param selection
      */
     public void setParentSelection(InterfaceSelection selection) {
         this.m_ParentSelection = selection;
     }
+
     public InterfaceSelection getParentSelection() {
         return this.m_ParentSelection;
     }
+
     public String parentSelectionTipText() {
         return "Choose a parent selection method.";
     }
 
-	/**
-	 * @return the withShow
-	 **/
-	public boolean isWithShow() {
-		return withShow;
-	}
+    /**
+     * @return the withShow
+	 *
+     */
+    public boolean isWithShow() {
+        return withShow;
+    }
 
-	public Plot getPlot() {
-		return myPlot;
-	}
+    public Plot getPlot() {
+        return myPlot;
+    }
 
-	protected void clearPlot() {
-		if (myPlot!=null) {
-			myPlot.clearAll();
-		    double[][] range = null;
-			if ((m_Population != null) && (m_Population.size() > 0)) {
-                        range = ((InterfaceDataTypeDouble)this.m_Population.get(0)).getDoubleRange();
-                    }
-			if (range != null) {
-				myPlot.setCornerPoints(range, 0);
-			}
-		}
-	}
-	
-	/**
-	 * @param withShow the withShow to set
-	 **/
-	public void setWithShow(boolean wShow) {
-		this.withShow = wShow;
-		if (!withShow) {
-                myPlot = null;
+    protected void clearPlot() {
+        if (myPlot != null) {
+            myPlot.clearAll();
+            double[][] range = null;
+            if ((m_Population != null) && (m_Population.size() > 0)) {
+                range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange();
             }
-		else {
-		    double[][] range;
-			if ((m_Population != null) && (m_Population.size() > 0)) {
-                        range = ((InterfaceDataTypeDouble)this.m_Population.get(0)).getDoubleRange();
-                    }
-			else {
-				range = new double[2][];
-				range[0] = new double[2];
-				range[0][0] = 0;
-				range[0][1] = 0;
-				range[1] = range[0]; // this is evil
-			}
-		    myPlot = new eva2.gui.Plot("PF", "x1", "x2", range[0], range[1]);
-		}
-	}
+            if (range != null) {
+                myPlot.setCornerPoints(range, 0);
+            }
+        }
+    }
 
-	/**
-	 * @return the sleepTime
-	 **/
-	public int getSleepTime() {
-		return sleepTime;
-	}
+    /**
+     * @param withShow the withShow to set
+	 *
+     */
+    public void setWithShow(boolean wShow) {
+        this.withShow = wShow;
+        if (!withShow) {
+            myPlot = null;
+        } else {
+            double[][] range;
+            if ((m_Population != null) && (m_Population.size() > 0)) {
+                range = ((InterfaceDataTypeDouble) this.m_Population.get(0)).getDoubleRange();
+            } else {
+                range = new double[2][];
+                range[0] = new double[2];
+                range[0][0] = 0;
+                range[0][1] = 0;
+                range[1] = range[0]; // this is evil
+            }
+            myPlot = new eva2.gui.Plot("PF", "x1", "x2", range[0], range[1]);
+        }
+    }
 
-	/**
-	 * @param sleepTime the sleepTime to set
-	 **/
-	public void setSleepTime(int sleepTime) {
-		this.sleepTime = sleepTime;
-	}
+    /**
+     * @return the sleepTime
+	 *
+     */
+    public int getSleepTime() {
+        return sleepTime;
+    }
 
-	/**
-	 * @return the mutationSigma
-	 **/
-	public double getMutationSigma() {
-		return mutationSigma;
-	}
+    /**
+     * @param sleepTime the sleepTime to set
+	 *
+     */
+    public void setSleepTime(int sleepTime) {
+        this.sleepTime = sleepTime;
+    }
 
-	/**
-	 * @param mutationSigma the mutationSigma to set
-	 **/
-	public void setMutationSigma(double mutationSigma) {
-		this.mutationSigma = mutationSigma;
-	}
+    /**
+     * @return the mutationSigma
+	 *
+     */
+    public double getMutationSigma() {
+        return mutationSigma;
+    }
 
-	public String mutationSigmaTipText() {
-		return "The (fixed) mutation step for the gaussian motion model";
-	}
+    /**
+     * @param mutationSigma the mutationSigma to set
+	 *
+     */
+    public void setMutationSigma(double mutationSigma) {
+        this.mutationSigma = mutationSigma;
+    }
 
-	public double getRndImmigrQuota() {
-		return randomImmigrationQuota;
-	}
+    public String mutationSigmaTipText() {
+        return "The (fixed) mutation step for the gaussian motion model";
+    }
 
-	public void setRndImmigrQuota(double randomImmigrationQuota) {
-		this.randomImmigrationQuota = randomImmigrationQuota;
-	}
+    public double getRndImmigrQuota() {
+        return randomImmigrationQuota;
+    }
 
-	public String rndImmigrQuotaTipText() {
-		return "The give ratio of the population will be reinitialized randomly in every iteration.";
-	}
+    public void setRndImmigrQuota(double randomImmigrationQuota) {
+        this.randomImmigrationQuota = randomImmigrationQuota;
+    }
 
-	public double getInitialVelocity() {
-		return initialVelocity;
-	}
+    public String rndImmigrQuotaTipText() {
+        return "The give ratio of the population will be reinitialized randomly in every iteration.";
+    }
 
-	public void setInitialVelocity(double initialVelocity) {
-		this.initialVelocity = initialVelocity;
-	}
+    public double getInitialVelocity() {
+        return initialVelocity;
+    }
 
-	public String initialVelocityTipText() {
-		return "If > 0, a linear motion model will be applied, otherwise the gaussian model"; 
-	}
+    public void setInitialVelocity(double initialVelocity) {
+        this.initialVelocity = initialVelocity;
+    }
 
-	public double getRotationDeg() {
-		return rotationDeg;
-	}
+    public String initialVelocityTipText() {
+        return "If > 0, a linear motion model will be applied, otherwise the gaussian model";
+    }
 
-	public void setRotationDeg(double rotationDeg) {
-		this.rotationDeg = rotationDeg;
-	}
+    public double getRotationDeg() {
+        return rotationDeg;
+    }
 
-	public int getPopSize() {
-		return popSize;
-	}
+    public void setRotationDeg(double rotationDeg) {
+        this.rotationDeg = rotationDeg;
+    }
 
-	public void setPopSize(int popSize) {
-		this.popSize = popSize;
-		m_Population.setTargetSize(popSize);
-	}
+    public int getPopSize() {
+        return popSize;
+    }
+
+    public void setPopSize(int popSize) {
+        this.popSize = popSize;
+        m_Population.setTargetSize(popSize);
+    }
 }

src/eva2/server/go/strategies/ParticleSwarmOptimization.java

@@ -1731,14 +1731,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
         return this.m_Identifier;
     }
 
-    /**
-     * This method is required to free the memory on a RMIServer, but there is
-     * nothing to implement.
-     */
-    @Override
-    public void freeWilly() {
-    }
-
     /**
      * ********************************************************************************************************************
      * These are for GUI
@@ -2063,7 +2055,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
     /**
      * @return true if swarm visualization is turned on
-	 *
+     *
      */
     public boolean isShow() {
         return m_Show;
@@ -2071,7 +2063,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
     /**
      * @param set swarm visualization (2D)
-	 *
+     *
      */
     public void setShow(boolean show) {
         m_Show = show;
@@ -2086,7 +2078,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
     /**
      * @return the checkSpeedLimit
-	 *
+     *
      */
     public boolean isCheckSpeedLimit() {
         return checkSpeedLimit;
@@ -2094,7 +2086,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
     /**
      * @param checkSpeedLimit the checkSpeedLimit to set
-	 *
+     *
      */
     public void setCheckSpeedLimit(boolean checkSpeedLimit) {
         this.checkSpeedLimit = checkSpeedLimit;
@@ -2113,7 +2105,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 //	}
     /**
      * @return the sleepTime
-	 *
+     *
      */
     public int getSleepTime() {
         return sleepTime;
@@ -2121,7 +2113,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 
     /**
      * @param sleepTime the sleepTime to set
-	 *
+     *
      */
     public void setSleepTime(int sleepTime) {
         this.sleepTime = sleepTime;

src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java

@@ -13,50 +13,50 @@ import eva2.server.go.problems.AbstractOptimizationProblem;
 import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-/** Population based incremental learning in the PSM by Monmarche
- * version with also allows to simulate ant systems due to the flexible
- * update rule of V. But both are limited to binary genotypes.
- * This is a simple implementation of Population Based Incremental Learning.
+/**
+ * Population based incremental learning in the PSM by Monmarche version with
+ * also allows to simulate ant systems due to the flexible update rule of V. But
+ * both are limited to binary genotypes. This is a simple implementation of
+ * Population Based Incremental Learning.
  *
- * Nicolas Monmarché ,  Eric Ramat ,  Guillaume Dromel ,  Mohamed Slimane ,  Gilles Venturini:
- * On the similarities between AS, BSC and PBIL: toward the birth of a new meta-heuristic. 
- * TecReport 215. Univ. de Tours, 1999.
+ * Nicolas Monmarché , Eric Ramat , Guillaume Dromel , Mohamed Slimane , Gilles
+ * Venturini: On the similarities between AS, BSC and PBIL: toward the birth of
+ * a new meta-heuristic. TecReport 215. Univ. de Tours, 1999.
  *
- * Copyright:       Copyright (c) 2003
- * Company:         University of Tuebingen, Computer Architecture
- * @author          Felix Streichert
- * @version:  $Revision: 307 $
- *            $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
- *            $Author: mkron $
+ * Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
+ * Architecture
+ *
+ * @author Felix Streichert
+ * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
+ * 2007) $ $Author: mkron $
  */
-
 public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, java.io.Serializable {
 
     // These variables are necessary for the simple testcase
-    private InterfaceOptimizationProblem            m_Problem           = new B1Problem();
-    private boolean                                 m_UseElitism        = true;
-    private InterfaceSelection                      m_SelectionOperator = new SelectBestIndividuals();
-    transient private String                        m_Identifier = "";
+    private InterfaceOptimizationProblem m_Problem = new B1Problem();
+    private boolean m_UseElitism = true;
+    private InterfaceSelection m_SelectionOperator = new SelectBestIndividuals();
+    transient private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
-    private Population                              m_Population        = new PBILPopulation();
-    private double                                  m_LearningRate      = 0.04;
-    private double                                  m_MutationRate      = 0.5;
-    private double                                  m_MutateSigma       = 0.01;
-    private int                                     m_NumberOfPositiveSamples = 1;
-    private double[]								m_initialProbabilities = ((PBILPopulation)m_Population).getProbabilityVector();
+    private Population m_Population = new PBILPopulation();
+    private double m_LearningRate = 0.04;
+    private double m_MutationRate = 0.5;
+    private double m_MutateSigma = 0.01;
+    private int m_NumberOfPositiveSamples = 1;
+    private double[] m_initialProbabilities = ((PBILPopulation) m_Population).getProbabilityVector();
 
     public PopulationBasedIncrementalLearning() {
     }
 
     public PopulationBasedIncrementalLearning(PopulationBasedIncrementalLearning a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_LearningRate                 = a.m_LearningRate;
-        this.m_MutationRate                 = a.m_MutationRate;
-        this.m_MutateSigma                  = a.m_MutateSigma;
-        this.m_NumberOfPositiveSamples      = a.m_NumberOfPositiveSamples;
-        this.m_UseElitism                   = a.m_UseElitism;
-        this.m_SelectionOperator            = (InterfaceSelection)a.m_SelectionOperator.clone();
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_LearningRate = a.m_LearningRate;
+        this.m_MutationRate = a.m_MutationRate;
+        this.m_MutateSigma = a.m_MutateSigma;
+        this.m_NumberOfPositiveSamples = a.m_NumberOfPositiveSamples;
+        this.m_UseElitism = a.m_UseElitism;
+        this.m_SelectionOperator = (InterfaceSelection) a.m_SelectionOperator.clone();
     }
 
     @Override
@@ -67,10 +67,10 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
     @Override
     public void init() {
         this.m_Problem.initPopulation(this.m_Population);
-        if ((m_initialProbabilities!=null) && (m_initialProbabilities.length==((PBILPopulation)m_Population).getProbabilityVector().length)) {
-        	((PBILPopulation)m_Population).setProbabilityVector(m_initialProbabilities);
+        if ((m_initialProbabilities != null) && (m_initialProbabilities.length == ((PBILPopulation) m_Population).getProbabilityVector().length)) {
+            ((PBILPopulation) m_Population).setProbabilityVector(m_initialProbabilities);
         } else {
-        	if (m_initialProbabilities!=null) {
+            if (m_initialProbabilities != null) {
                 System.err.println("Warning: initial probability vector doesnt match in length!");
             }
         }
@@ -78,27 +78,30 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
         if (!(pop.getEAIndividual(0) instanceof InterfaceGAIndividual)) {
-        	System.err.println("Error: PBIL only works with GAIndividuals!");
+            System.err.println("Error: PBIL only works with GAIndividuals!");
         }
         this.m_Population = new PBILPopulation();
-        this.m_Population.addPopulation((Population)pop.clone());
+        this.m_Population.addPopulation((Population) pop.clone());
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.evaluatePopulation(this.m_Population);
         }
-        ((PBILPopulation)this.m_Population).buildProbabilityVector();
+        ((PBILPopulation) this.m_Population).buildProbabilityVector();
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will evaluate the current population using the
-     * given problem.
+    /**
+     * This method will evaluate the current population using the given problem.
+     *
      * @param population The population that is to be evaluated
      */
     private void evaluatePopulation(Population population) {
@@ -106,17 +109,18 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
         population.incrGeneration();
     }
 
-    /** This method will generate the offspring population from the
-     * given population of evaluated individuals.
+    /**
+     * This method will generate the offspring population from the given
+     * population of evaluated individuals.
      */
     private Population generateChildren() {
-        PBILPopulation                  result = (PBILPopulation)this.m_Population.clone();
-        Population                      examples;
+        PBILPopulation result = (PBILPopulation) this.m_Population.clone();
+        Population examples;
 
 //        this.m_NormationOperator.computeSelectionProbability(this.m_Population, "Fitness");
         //System.out.println("Population:"+this.m_Population.getSolutionRepresentationFor());
         this.m_SelectionOperator.prepareSelection(this.m_Population);
-        examples     = this.m_SelectionOperator.selectFrom(this.m_Population, this.m_NumberOfPositiveSamples);
+        examples = this.m_SelectionOperator.selectFrom(this.m_Population, this.m_NumberOfPositiveSamples);
         //System.out.println("Parents:"+parents.getSolutionRepresentationFor());
         result.learnFrom(examples, this.m_LearningRate);
         result.mutateProbabilityVector(this.m_MutationRate, this.m_MutateSigma);
@@ -127,7 +131,7 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
     @Override
     public void optimize() {
         Population nextGeneration;
-        AbstractEAIndividual   elite;
+        AbstractEAIndividual elite;
 
         nextGeneration = this.generateChildren();
         this.evaluatePopulation(nextGeneration);
@@ -141,46 +145,55 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
         if (m_Problem instanceof AbstractOptimizationProblem) {
-        	if (!(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate() instanceof InterfaceGAIndividual)) { 
-        		System.err.println("Error: PBIL only works with GAIndividuals!");
-        	}
+            if (!(((AbstractOptimizationProblem) m_Problem).getIndividualTemplate() instanceof InterfaceGAIndividual)) {
+                System.err.println("Error: PBIL only works with GAIndividuals!");
+            }
         }
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
+
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    /** Something has changed
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -188,39 +201,42 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
         String result = "";
         result += "Population Based Incremental Learning:\n";
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "The Population based incremental learning is based on a statistical distribution of bit positions. Please note: This optimizer requires a binary genotype!";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -228,26 +244,30 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
         return "PBIL";
     }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Edit the properties of the PBIL population used.";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
 //    /** This method will set the normation method that is to be used.
 //     * @param normation
@@ -262,52 +282,66 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
 //        return "Select the normation method.";
 //    }
 
-    /** This method will set the selection method that is to be used
+    /**
+     * This method will set the selection method that is to be used
+     *
      * @param selection
      */
     public void setSelectionMethod(InterfaceSelection selection) {
         this.m_SelectionOperator = selection;
     }
+
     public InterfaceSelection getSelectionMethod() {
         return this.m_SelectionOperator;
     }
+
     public String selectionMethodTipText() {
         return "Choose a selection method.";
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param elitism
      */
-    public void setElitism (boolean elitism) {
+    public void setElitism(boolean elitism) {
         this.m_UseElitism = elitism;
     }
+
     public boolean getElitism() {
         return this.m_UseElitism;
     }
+
     public String elitismTipText() {
         return "Enable/disable elitism.";
     }
 
-    /** This method will set the learning rate for PBIL
+    /**
+     * This method will set the learning rate for PBIL
+     *
      * @param LearningRate
      */
-    public void setLearningRate (double LearningRate) {
+    public void setLearningRate(double LearningRate) {
         this.m_LearningRate = LearningRate;
         if (this.m_LearningRate < 0) {
             this.m_LearningRate = 0;
         }
     }
+
     public double getLearningRate() {
         return this.m_LearningRate;
     }
+
     public String learningRateTipText() {
         return "The learing rate of PBIL.";
     }
 
-    /** This method will set the mutation rate for PBIL
+    /**
+     * This method will set the mutation rate for PBIL
+     *
      * @param m
      */
-    public void setMutationRate (double m) {
+    public void setMutationRate(double m) {
         this.m_MutationRate = m;
         if (this.m_MutationRate < 0) {
             this.m_MutationRate = 0;
@@ -315,51 +349,61 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
         if (this.m_MutationRate > 1) {
             this.m_MutationRate = 1;
         }
-     }
+    }
+
     public double getMutationRate() {
         return this.m_MutationRate;
     }
+
     public String mutationRateTipText() {
         return "The mutation rate of PBIL.";
     }
 
-    /** This method will set the mutation sigma for PBIL
+    /**
+     * This method will set the mutation sigma for PBIL
+     *
      * @param m
      */
-    public void setMutateSigma (double m) {
+    public void setMutateSigma(double m) {
         this.m_MutateSigma = m;
         if (this.m_MutateSigma < 0) {
             this.m_MutateSigma = 0;
         }
     }
+
     public double getMutateSigma() {
         return this.m_MutateSigma;
     }
+
     public String mutateSigmaTipText() {
         return "Set the sigma for the mutation of the probability vector.";
     }
 
-    /** This method will set the number of positive samples for PBIL
+    /**
+     * This method will set the number of positive samples for PBIL
+     *
      * @param PositiveSamples
      */
-    public void setPositiveSamples (int PositiveSamples) {
+    public void setPositiveSamples(int PositiveSamples) {
         this.m_NumberOfPositiveSamples = PositiveSamples;
         if (this.m_NumberOfPositiveSamples < 1) {
             this.m_NumberOfPositiveSamples = 1;
         }
     }
+
     public int getPositiveSamples() {
         return this.m_NumberOfPositiveSamples;
     }
+
     public String positiveSamplesTipText() {
         return "The number of positive samples that update the PBIL vector.";
     }
 
-	public double[] getInitialProbabilities() {
-		return m_initialProbabilities;
-	}
+    public double[] getInitialProbabilities() {
+        return m_initialProbabilities;
+    }
 
-	public void setInitialProbabilities(double[] probabilities) {
-		m_initialProbabilities = probabilities;
-	}
+    public void setInitialProbabilities(double[] probabilities) {
+        m_initialProbabilities = probabilities;
+    }
 }

src/eva2/server/go/strategies/SimulatedAnnealing.java

@@ -10,30 +10,28 @@ import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
 
-/** Simulated Annealing by Nelder and Mead, a simple yet efficient local search
+/**
+ * Simulated Annealing by Nelder and Mead, a simple yet efficient local search
  * method. But to become less prone to premature convergence the cooling rate
- * has to be tuned to the optimization problem at hand. Again the population size
- * gives the number of multi-starts.
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 13.05.2004
- * Time: 10:30:26
- * To change this template use File | Settings | File Templates.
+ * has to be tuned to the optimization problem at hand. Again the population
+ * size gives the number of multi-starts. Created by IntelliJ IDEA. User:
+ * streiche Date: 13.05.2004 Time: 10:30:26 To change this template use File |
+ * Settings | File Templates.
  */
 public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializable {
     // These variables are necessary for the simple testcase
-    private InterfaceOptimizationProblem            m_Problem           = new B1Problem();
-    private int             m_MultiRuns             = 100;
-    private int             m_FitnessCalls          = 100;
-    private int             m_FitnessCallsNeeded    = 0;
-    GAIndividualBinaryData  m_Best, m_Test;
-    public double           m_InitialTemperature    = 2, m_CurrentTemperature;
-    public double           m_Alpha                 = 0.9;
 
+    private InterfaceOptimizationProblem m_Problem = new B1Problem();
+    private int m_MultiRuns = 100;
+    private int m_FitnessCalls = 100;
+    private int m_FitnessCallsNeeded = 0;
+    GAIndividualBinaryData m_Best, m_Test;
+    public double m_InitialTemperature = 2, m_CurrentTemperature;
+    public double m_Alpha = 0.9;
     // These variables are necessary for the more complex LectureGUI enviroment
-    transient private String                m_Identifier = "";
+    transient private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
-    private Population      m_Population;
+    private Population m_Population;
 
     public SimulatedAnnealing() {
         this.m_Population = new Population();
@@ -41,11 +39,11 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
     }
 
     public SimulatedAnnealing(SimulatedAnnealing a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_InitialTemperature           = a.m_InitialTemperature;
-        this.m_CurrentTemperature           = a.m_CurrentTemperature;
-        this.m_Alpha                        = a.m_Alpha;
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_InitialTemperature = a.m_InitialTemperature;
+        this.m_CurrentTemperature = a.m_CurrentTemperature;
+        this.m_Alpha = a.m_Alpha;
     }
 
     @Override
@@ -53,7 +51,8 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         return (Object) new SimulatedAnnealing(this);
     }
 
-    /** This method will init the HillClimber
+    /**
+     * This method will init the HillClimber
      */
     @Override
     public void init() {
@@ -63,28 +62,31 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
-        this.m_Population = (Population)pop.clone();
+        this.m_Population = (Population) pop.clone();
         this.m_CurrentTemperature = this.m_InitialTemperature;
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
-    /** This method will optimize
+    /**
+     * This method will optimize
      */
     @Override
     public void optimize() {
-        AbstractEAIndividual    indy;
-        Population              original = (Population)this.m_Population.clone();
-        double                  delta;
+        AbstractEAIndividual indy;
+        Population original = (Population) this.m_Population.clone();
+        double delta;
 
         for (int i = 0; i < this.m_Population.size(); i++) {
             indy = ((AbstractEAIndividual) this.m_Population.get(i));
@@ -95,13 +97,13 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         }
         this.m_Problem.evaluate(this.m_Population);
         for (int i = 0; i < this.m_Population.size(); i++) {
-            if (((AbstractEAIndividual)original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual)this.m_Population.get(i)))) {
+            if (((AbstractEAIndividual) original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual) this.m_Population.get(i)))) {
                 this.m_Population.remove(i);
                 this.m_Population.add(i, original.get(i));
             } else {
-                delta = this.calculateDelta(((AbstractEAIndividual)original.get(i)), ((AbstractEAIndividual)this.m_Population.get(i)));
+                delta = this.calculateDelta(((AbstractEAIndividual) original.get(i)), ((AbstractEAIndividual) this.m_Population.get(i)));
                 //System.out.println("delta: " + delta);
-                if (Math.exp(-delta/this.m_CurrentTemperature) > RNG.randomInt(0,1)) {
+                if (Math.exp(-delta / this.m_CurrentTemperature) > RNG.randomInt(0, 1)) {
                     this.m_Population.remove(i);
                     this.m_Population.add(i, original.get(i));
                 }
@@ -112,13 +114,15 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method calculates the difference between the fitness values
-     * @param org     The original
-     * @param mut     The mutant
+    /**
+     * This method calculates the difference between the fitness values
+     *
+     * @param org The original
+     * @param mut The mutant
      */
     private double calculateDelta(AbstractEAIndividual org, AbstractEAIndividual mut) {
-        double      result = 0;
-        double[]    fitOrg, fitMut;
+        double result = 0;
+        double[] fitOrg, fitMut;
         fitOrg = org.getFitness();
         fitMut = mut.getFitness();
         for (int i = 0; i < fitOrg.length; i++) {
@@ -127,19 +131,23 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         return result;
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will init the HillClimber
+    /**
+     * This method will init the HillClimber
      */
     public void defaultInit() {
         this.m_FitnessCallsNeeded = 0;
@@ -147,24 +155,26 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         this.m_Best.defaultInit(m_Problem);
     }
 
-    /** This method will optimize
+    /**
+     * This method will optimize
      */
     public void defaultOptimize() {
         for (int i = 0; i < m_FitnessCalls; i++) {
-            this.m_Test = (GAIndividualBinaryData)((this.m_Best).clone());
+            this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone());
             this.m_Test.defaultMutate();
             if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) {
                 this.m_Best = this.m_Test;
             }
             this.m_FitnessCallsNeeded = i;
             if (this.m_Best.defaultEvaulateAsMiniBits() == 0) {
-                i = this.m_FitnessCalls +1;
+                i = this.m_FitnessCalls + 1;
             }
         }
     }
 
-    /** This main method will start a simple hillclimber.
-     * No arguments necessary.
+    /**
+     * This main method will start a simple hillclimber. No arguments necessary.
+     *
      * @param args
      */
     public static void main(String[] args) {
@@ -178,31 +188,35 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         }
         TmpMeanCalls /= program.m_MultiRuns;
         TmpMeanFitness /= program.m_MultiRuns;
-        System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
+        System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
     }
 
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    protected void firePropertyChangedEvent (String name) {
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -210,96 +224,112 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         String result = "";
         if (this.m_Population.size() > 1) {
             result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
-        }
-        else {
+        } else {
             result += "Simulated Annealing:\n";
         }
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "The simulated annealing uses an additional cooling rate instead of a simple dominate criteria to accpect worse solutions by chance.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
     public String getName() {
         return "MS-SA";
     }
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Change the number of best individuals stored (MS-SA)).";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
-    /** Set the initial temperature
+
+    /**
+     * Set the initial temperature
+     *
      * @return The initial temperature.
      */
     public double getInitialTemperature() {
         return this.m_InitialTemperature;
     }
-    public void setInitialTemperature(double pop){
+
+    public void setInitialTemperature(double pop) {
         this.m_InitialTemperature = pop;
     }
+
     public String initialTemperatureTipText() {
         return "Set the initial temperature.";
     }
 
-    /** Set alpha, which is used to degrade the temperaure
+    /**
+     * Set alpha, which is used to degrade the temperaure
+     *
      * @return The cooling rate.
      */
     public double getAlpha() {
         return this.m_Alpha;
     }
-    public void setAlpha(double a){
+
+    public void setAlpha(double a) {
         this.m_Alpha = a;
         if (this.m_Alpha > 1) {
             this.m_Alpha = 1.0;
         }
     }
+
     public String alphaTipText() {
         return "Set alpha, which is used to degrade the temperaure.";
     }

src/eva2/server/go/strategies/SteadyStateGA.java

@@ -13,186 +13,200 @@ import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-/** A simple implementation of the steady-state GA with variable
- * replacement schemes. To reduce the logging effort population.size()
- * optimization steps are performed each time optimize() is called.
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 19.07.2005
- * Time: 14:30:20
- * To change this template use File | Settings | File Templates.
+/**
+ * A simple implementation of the steady-state GA with variable replacement
+ * schemes. To reduce the logging effort population.size() optimization steps
+ * are performed each time optimize() is called. Created by IntelliJ IDEA. User:
+ * streiche Date: 19.07.2005 Time: 14:30:20 To change this template use File |
+ * Settings | File Templates.
  */
 public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
 
-    private Population                      m_Population        = new Population();
-    private InterfaceOptimizationProblem    m_Problem           = new B1Problem();
-    private InterfaceSelection              m_ParentSelection   = new SelectTournament();
-    private InterfaceSelection              m_PartnerSelection  = new SelectTournament();
-    private InterfaceReplacement            m_ReplacementSelection  = new ReplaceWorst();
-    private int                             m_NumberOfPartners  = 1;
+    private Population m_Population = new Population();
+    private InterfaceOptimizationProblem m_Problem = new B1Problem();
+    private InterfaceSelection m_ParentSelection = new SelectTournament();
+    private InterfaceSelection m_PartnerSelection = new SelectTournament();
+    private InterfaceReplacement m_ReplacementSelection = new ReplaceWorst();
+    private int m_NumberOfPartners = 1;
+    private String m_Identifier = "";
+    transient private InterfacePopulationChangedEventListener m_Listener;
 
-        private String                m_Identifier = "";
-        transient private InterfacePopulationChangedEventListener m_Listener;
+    public SteadyStateGA() {
+    }
 
-        public SteadyStateGA() {
-        }
-
-        public SteadyStateGA(SteadyStateGA a) {
-            this.m_Population                   = (Population)a.m_Population.clone();
-            this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-            this.m_Identifier                   = a.m_Identifier;
-            this.m_NumberOfPartners             = a.m_NumberOfPartners;
-            this.m_ParentSelection              = (InterfaceSelection)a.m_ParentSelection.clone();
-            this.m_PartnerSelection             = (InterfaceSelection)a.m_PartnerSelection.clone();
-            this.m_ReplacementSelection         = (InterfaceReplacement)a.m_ReplacementSelection.clone();
-        }
+    public SteadyStateGA(SteadyStateGA a) {
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_Identifier = a.m_Identifier;
+        this.m_NumberOfPartners = a.m_NumberOfPartners;
+        this.m_ParentSelection = (InterfaceSelection) a.m_ParentSelection.clone();
+        this.m_PartnerSelection = (InterfaceSelection) a.m_PartnerSelection.clone();
+        this.m_ReplacementSelection = (InterfaceReplacement) a.m_ReplacementSelection.clone();
+    }
 
     @Override
-        public Object clone() {
-            return (Object) new SteadyStateGA(this);
-        }
+    public Object clone() {
+        return (Object) new SteadyStateGA(this);
+    }
 
     @Override
-        public void init() {
-            this.m_Problem.initPopulation(this.m_Population);
+    public void init() {
+        this.m_Problem.initPopulation(this.m_Population);
+        this.evaluatePopulation(this.m_Population);
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+    }
+
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param reset If true the population is reset.
+     */
+    @Override
+    public void initByPopulation(Population pop, boolean reset) {
+        this.m_Population = (Population) pop.clone();
+        if (reset) {
+            this.m_Population.init();
             this.evaluatePopulation(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
+    }
 
-        /** This method will init the optimizer with a given population
-         * @param reset     If true the population is reset.
-         */
-    @Override
-        public void initByPopulation(Population pop, boolean reset) {
-            this.m_Population = (Population)pop.clone();
-            if (reset) {
-            	this.m_Population.init();
-                this.evaluatePopulation(this.m_Population);
-                this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-            }
-        }
+    /**
+     * This method will evaluate the current population using the given problem.
+     *
+     * @param population The population that is to be evaluated
+     */
+    private void evaluatePopulation(Population population) {
+        this.m_Problem.evaluate(population);
+        population.incrGeneration();
+    }
 
-        /** This method will evaluate the current population using the
-         * given problem.
-         * @param population The population that is to be evaluated
-         */
-        private void evaluatePopulation(Population population) {
-            this.m_Problem.evaluate(population);
-            population.incrGeneration();
+    /**
+     * This method will assign fitness values to all individual in the current
+     * population.
+     *
+     * @param population The population that is to be evaluated
+     */
+    private void defaultEvaluatePopulation(Population population) {
+        GAIndividualBinaryData tmpIndy;
+        for (int i = 0; i < population.size(); i++) {
+            tmpIndy = (GAIndividualBinaryData) population.get(i);
+            tmpIndy.SetFitness(0, tmpIndy.defaultEvaulateAsMiniBits());
+            population.incrFunctionCalls();
         }
+        population.incrGeneration();
+    }
 
-        /** This method will assign fitness values to all individual in the
-         * current population.
-         * @param population The population that is to be evaluated
-         */
-        private void defaultEvaluatePopulation(Population population) {
-            GAIndividualBinaryData tmpIndy;
-            for (int i = 0; i < population.size(); i++) {
-                tmpIndy = (GAIndividualBinaryData) population.get(i);
-                tmpIndy.SetFitness(0, tmpIndy.defaultEvaulateAsMiniBits());
-                population.incrFunctionCalls();
-            }
-            population.incrGeneration();
-        }
-
-        /** This method will generate the offspring population from the
-         * given population of evaluated individuals.
-         */
-        private void generateChildren() {
-            this.m_ParentSelection.prepareSelection(this.m_Population);
-            this.m_PartnerSelection.prepareSelection(this.m_Population);
-            Population parents                  = this.m_ParentSelection.selectFrom(this.m_Population, 1);
-            AbstractEAIndividual mother         = (AbstractEAIndividual)parents.get(0);
-            parents                             = this.m_PartnerSelection.findPartnerFor(mother, this.m_Population, this.m_NumberOfPartners);
-            AbstractEAIndividual[]  offSprings  = mother.mateWith(parents);
-            offSprings[0].mutate();
-            this.m_Problem.evaluate(offSprings[0]);
-            this.m_ReplacementSelection.insertIndividual(offSprings[0], this.m_Population, parents);
-        }
+    /**
+     * This method will generate the offspring population from the given
+     * population of evaluated individuals.
+     */
+    private void generateChildren() {
+        this.m_ParentSelection.prepareSelection(this.m_Population);
+        this.m_PartnerSelection.prepareSelection(this.m_Population);
+        Population parents = this.m_ParentSelection.selectFrom(this.m_Population, 1);
+        AbstractEAIndividual mother = (AbstractEAIndividual) parents.get(0);
+        parents = this.m_PartnerSelection.findPartnerFor(mother, this.m_Population, this.m_NumberOfPartners);
+        AbstractEAIndividual[] offSprings = mother.mateWith(parents);
+        offSprings[0].mutate();
+        this.m_Problem.evaluate(offSprings[0]);
+        this.m_ReplacementSelection.insertIndividual(offSprings[0], this.m_Population, parents);
+    }
 
     @Override
-        public void optimize() {
-            for (int i = 0; i < this.m_Population.size(); i++) {
-                this.generateChildren();
-            }
-            this.m_Population.incrFunctionCallsBy(this.m_Population.size());
-            this.m_Population.incrGeneration();
-            this.firePropertyChangedEvent(Population.nextGenerationPerformed);
-        }
-    @Override
-        public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
-            this.m_Listener = ea;
-        }
-    @Override
-    	public boolean removePopulationChangedEventListener(
-    			InterfacePopulationChangedEventListener ea) {
-    		if (m_Listener==ea) {
-    			m_Listener=null;
-    			return true;
-    		} else {
-                                return false;
-                            }
-    	}
-        protected void firePropertyChangedEvent (String name) {
-            if (this.m_Listener != null) {
-                this.m_Listener.registerPopulationStateChanged(this, name);
-            }
+    public void optimize() {
+        for (int i = 0; i < this.m_Population.size(); i++) {
+            this.generateChildren();
         }
+        this.m_Population.incrFunctionCallsBy(this.m_Population.size());
+        this.m_Population.incrGeneration();
+        this.firePropertyChangedEvent(Population.nextGenerationPerformed);
+    }
 
-        /** This method will set the problem that is to be optimized
-         * @param problem
-         */
     @Override
-        public void setProblem (InterfaceOptimizationProblem problem) {
-            this.m_Problem = problem;
-        }
-    @Override
-        public InterfaceOptimizationProblem getProblem () {
-            return this.m_Problem;
-        }
+    public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
+        this.m_Listener = ea;
+    }
 
-        /** This method will return a string describing all properties of the optimizer
-         * and the applied methods.
-         * @return A descriptive string
-         */
     @Override
-        public String getStringRepresentation() {
-            String result = "";
-            result += "Genetic Algorithm:\n";
-            result += "Optimization Problem: ";
-            result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
-            result += this.m_Population.getStringRepresentation();
-            return result;
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
         }
-        /** This method allows you to set an identifier for the algorithm
-         * @param name      The indenifier
-         */
-    @Override
-         public void setIdentifier(String name) {
-            this.m_Identifier = name;
-        }
-    @Override
-         public String getIdentifier() {
-             return this.m_Identifier;
-         }
-
-        /** This method is required to free the memory on a RMIServer,
-         * but there is nothing to implement.
-         */
-    @Override
-        public void freeWilly() {
+    }
 
+    protected void firePropertyChangedEvent(String name) {
+        if (this.m_Listener != null) {
+            this.m_Listener.registerPopulationStateChanged(this, name);
         }
-    /**********************************************************************************************************************
+    }
+
+    /**
+     * This method will set the problem that is to be optimized
+     *
+     * @param problem
+     */
+    @Override
+    public void setProblem(InterfaceOptimizationProblem problem) {
+        this.m_Problem = problem;
+    }
+
+    @Override
+    public InterfaceOptimizationProblem getProblem() {
+        return this.m_Problem;
+    }
+
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
+     * @return A descriptive string
+     */
+    @Override
+    public String getStringRepresentation() {
+        String result = "";
+        result += "Genetic Algorithm:\n";
+        result += "Optimization Problem: ";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
+        result += this.m_Population.getStringRepresentation();
+        return result;
+    }
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
+     */
+    @Override
+    public void setIdentifier(String name) {
+        this.m_Identifier = name;
+    }
+
+    @Override
+    public String getIdentifier() {
+        return this.m_Identifier;
+    }
+
+    /**
+     * ********************************************************************************************************************
      * These are for GUI
      */
-    /** This method returns a global info string
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "This is a Steady-State Genetic Algorithm.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -200,42 +214,53 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
         return "SS-GA";
     }
 
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Edit the properties of the population used.";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
-    /** This method will set the parent selection method that is to be used
+
+    /**
+     * This method will set the parent selection method that is to be used
+     *
      * @param selection
      */
     public void setParentSelection(InterfaceSelection selection) {
         this.m_ParentSelection = selection;
     }
+
     public InterfaceSelection getParentSelection() {
         return this.m_ParentSelection;
     }
+
     public String parentSelectionTipText() {
         return "Choose a parent selection method.";
     }
 
-    /** This method will set the number of partners that are needed to create
+    /**
+     * This method will set the number of partners that are needed to create
      * offsprings by mating
+     *
      * @param partners
      */
     public void setNumberOfPartners(int partners) {
@@ -244,35 +269,46 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
         }
         this.m_NumberOfPartners = partners;
     }
+
     public int getNumberOfPartners() {
         return this.m_NumberOfPartners;
     }
+
     public String numberOfPartnersTipText() {
         return "The number of mating partners needed to create offsprings.";
     }
 
-   /** Choose a selection method for selecting recombination partners for given parents
-    * @param selection
-    */
+    /**
+     * Choose a selection method for selecting recombination partners for given
+     * parents
+     *
+     * @param selection
+     */
     public void setPartnerSelection(InterfaceSelection selection) {
         this.m_PartnerSelection = selection;
     }
+
     public InterfaceSelection getPartnerSelection() {
         return this.m_PartnerSelection;
     }
+
     public String partnerSelectionTipText() {
         return "Choose a selection method for selecting recombination partners for given parents.";
     }
 
-    /** Choose a replacement strategy
+    /**
+     * Choose a replacement strategy
+     *
      * @param selection
      */
     public void setReplacementSelection(InterfaceReplacement selection) {
         this.m_ReplacementSelection = selection;
     }
+
     public InterfaceReplacement getReplacementSelection() {
         return this.m_ReplacementSelection;
     }
+
     public String replacementSelectionTipText() {
         return "Choose a replacement strategy.";
     }

src/eva2/server/go/strategies/ThresholdAlgorithm.java

@@ -9,28 +9,25 @@ import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.B1Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-/** Threshold accepting algorithm simliar strategy as the flood
- * algorithm, similar problems.
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 01.10.2004
- * Time: 13:35:49
- * To change this template use File | Settings | File Templates.
+/**
+ * Threshold accepting algorithm simliar strategy as the flood algorithm,
+ * similar problems. Created by IntelliJ IDEA. User: streiche Date: 01.10.2004
+ * Time: 13:35:49 To change this template use File | Settings | File Templates.
  */
 public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializable {
     // These variables are necessary for the simple testcase
-    private InterfaceOptimizationProblem            m_Problem           = new B1Problem();
-    private int             m_MultiRuns             = 100;
-    private int             m_FitnessCalls          = 100;
-    private int             m_FitnessCallsNeeded    = 0;
-    GAIndividualBinaryData  m_Best, m_Test;
-    public double           m_InitialT   = 2, m_CurrentT;
-    public double           m_Alpha         = 0.9;
 
+    private InterfaceOptimizationProblem m_Problem = new B1Problem();
+    private int m_MultiRuns = 100;
+    private int m_FitnessCalls = 100;
+    private int m_FitnessCallsNeeded = 0;
+    GAIndividualBinaryData m_Best, m_Test;
+    public double m_InitialT = 2, m_CurrentT;
+    public double m_Alpha = 0.9;
     // These variables are necessary for the more complex LectureGUI enviroment
-    transient private String                m_Identifier = "";
+    transient private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
-    private Population      m_Population;
+    private Population m_Population;
 
     public ThresholdAlgorithm() {
         this.m_Population = new Population();
@@ -38,11 +35,11 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
     }
 
     public ThresholdAlgorithm(ThresholdAlgorithm a) {
-        this.m_Population                   = (Population)a.m_Population.clone();
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_InitialT                     = a.m_InitialT;
-        this.m_CurrentT                     = a.m_CurrentT;
-        this.m_Alpha                        = a.m_Alpha;
+        this.m_Population = (Population) a.m_Population.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_InitialT = a.m_InitialT;
+        this.m_CurrentT = a.m_CurrentT;
+        this.m_Alpha = a.m_Alpha;
     }
 
     @Override
@@ -50,7 +47,8 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         return (Object) new ThresholdAlgorithm(this);
     }
 
-    /** This method will init the HillClimber
+    /**
+     * This method will init the HillClimber
      */
     @Override
     public void init() {
@@ -60,28 +58,31 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
-        this.m_Population = (Population)pop.clone();
+        this.m_Population = (Population) pop.clone();
         this.m_CurrentT = this.m_InitialT;
         if (reset) {
-        	this.m_Population.init();
+            this.m_Population.init();
             this.m_Problem.evaluate(this.m_Population);
             this.firePropertyChangedEvent(Population.nextGenerationPerformed);
         }
     }
 
-    /** This method will optimize
+    /**
+     * This method will optimize
      */
     @Override
     public void optimize() {
-        AbstractEAIndividual    indy;
-        Population              original = (Population)this.m_Population.clone();
-        double                  delta;
+        AbstractEAIndividual indy;
+        Population original = (Population) this.m_Population.clone();
+        double delta;
 
         for (int i = 0; i < this.m_Population.size(); i++) {
             indy = ((AbstractEAIndividual) this.m_Population.get(i));
@@ -92,7 +93,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         }
         this.m_Problem.evaluate(this.m_Population);
         for (int i = 0; i < this.m_Population.size(); i++) {
-            delta = this.calculateDelta(((AbstractEAIndividual)original.get(i)), ((AbstractEAIndividual)this.m_Population.get(i)));
+            delta = this.calculateDelta(((AbstractEAIndividual) original.get(i)), ((AbstractEAIndividual) this.m_Population.get(i)));
             if (delta < this.m_CurrentT) {
                 this.m_Population.remove(i);
                 this.m_Population.add(i, original.get(i));
@@ -103,13 +104,15 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method calculates the difference between the fitness values
-     * @param org     The original
-     * @param mut     The mutant
+    /**
+     * This method calculates the difference between the fitness values
+     *
+     * @param org The original
+     * @param mut The mutant
      */
     private double calculateDelta(AbstractEAIndividual org, AbstractEAIndividual mut) {
-        double      result = 0;
-        double[]    fitOrg, fitMut;
+        double result = 0;
+        double[] fitOrg, fitMut;
         fitOrg = org.getFitness();
         fitMut = mut.getFitness();
         for (int i = 0; i < fitOrg.length; i++) {
@@ -118,19 +121,23 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         return result;
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will init the HillClimber
+    /**
+     * This method will init the HillClimber
      */
     public void defaultInit() {
         this.m_FitnessCallsNeeded = 0;
@@ -138,24 +145,26 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         this.m_Best.defaultInit(m_Problem);
     }
 
-    /** This method will optimize
+    /**
+     * This method will optimize
      */
     public void defaultOptimize() {
         for (int i = 0; i < m_FitnessCalls; i++) {
-            this.m_Test = (GAIndividualBinaryData)((this.m_Best).clone());
+            this.m_Test = (GAIndividualBinaryData) ((this.m_Best).clone());
             this.m_Test.defaultMutate();
             if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) {
                 this.m_Best = this.m_Test;
             }
             this.m_FitnessCallsNeeded = i;
             if (this.m_Best.defaultEvaulateAsMiniBits() == 0) {
-                i = this.m_FitnessCalls +1;
+                i = this.m_FitnessCalls + 1;
             }
         }
     }
 
-    /** This main method will start a simple hillclimber.
-     * No arguments necessary.
+    /**
+     * This main method will start a simple hillclimber. No arguments necessary.
+     *
      * @param args
      */
     public static void main(String[] args) {
@@ -169,30 +178,35 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         }
         TmpMeanCalls /= program.m_MultiRuns;
         TmpMeanFitness /= program.m_MultiRuns;
-        System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
+        System.out.println("(" + program.m_MultiRuns + "/" + program.m_FitnessCalls + ") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
     }
+
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    protected void firePropertyChangedEvent (String name) {
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -200,96 +214,112 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         String result = "";
         if (this.m_Population.size() > 1) {
             result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
-        }
-        else {
+        } else {
             result += "Threshold Algorithm:\n";
         }
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "The threshold algorithm uses an declining threshold to accpect new solutions.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
     public String getName() {
         return "MS-TA";
     }
-    /** Assuming that all optimizer will store thier data in a population
-     * we will allow acess to this population to query to current state
-     * of the optimizer.
+
+    /**
+     * Assuming that all optimizer will store thier data in a population we will
+     * allow acess to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "Change the number of best individuals stored (MS-TA).";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
-    /** Set the initial threshold
+
+    /**
+     * Set the initial threshold
+     *
      * @return The initial temperature.
      */
     public double getInitialT() {
         return this.m_InitialT;
     }
-    public void setInitialT(double pop){
+
+    public void setInitialT(double pop) {
         this.m_InitialT = pop;
     }
+
     public String initialTTipText() {
         return "Set the initial threshold.";
     }
 
-    /** Set alpha, which is used to degrade the threshold
+    /**
+     * Set alpha, which is used to degrade the threshold
+     *
      * @return The initial temperature.
      */
     public double getAlpha() {
         return this.m_Alpha;
     }
-    public void setAlpha(double a){
+
+    public void setAlpha(double a) {
         this.m_Alpha = a;
         if (this.m_Alpha > 1) {
             this.m_Alpha = 1.0;
         }
     }
+
     public String alphaTipText() {
         return "Set alpha, which is used to degrade the threshold.";
     }

src/eva2/server/go/strategies/WingedMultiObjectiveEA.java

@@ -11,46 +11,43 @@ import eva2.server.go.problems.AbstractMultiObjectiveOptimizationProblem;
 import eva2.server.go.problems.FM0Problem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 
-/** The winged MOEA was a nice idea, which didn't really work out.
- * Here a standard MOEA is assisted by n additional local searchers, each
- * optimizing just one objective. The idea was that these local optimizers
- * would span the search space and would allow the MOEA to converge faster.
- * But in the end the performance of this algorithm strongly depends on the
- * optimization problem. 
- * Created by IntelliJ IDEA.
- * User: streiche
- * Date: 16.02.2005
- * Time: 16:34:22
- * To change this template use File | Settings | File Templates.
+/**
+ * The winged MOEA was a nice idea, which didn't really work out. Here a
+ * standard MOEA is assisted by n additional local searchers, each optimizing
+ * just one objective. The idea was that these local optimizers would span the
+ * search space and would allow the MOEA to converge faster. But in the end the
+ * performance of this algorithm strongly depends on the optimization problem.
+ * Created by IntelliJ IDEA. User: streiche Date: 16.02.2005 Time: 16:34:22 To
+ * change this template use File | Settings | File Templates.
  */
 public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
 
-    private InterfaceOptimizer              m_MOOptimizer       = new MultiObjectiveEA();
-    private InterfaceOptimizer              m_SOOptimizer       = new GeneticAlgorithm();
-    private InterfaceOptimizer[]            m_SOOptimizers;
-    private Population                      m_Population        = new Population();
-    private int                             m_MigrationRate     = 5;
-    private int                             m_OutputDimension   = 2;
-    private int                             m_NumberOfLocalOptimizers = 2;
-    private InterfaceOptimizationProblem    m_Problem           = new FM0Problem();
-    private String                          m_Identifier        = "";
+    private InterfaceOptimizer m_MOOptimizer = new MultiObjectiveEA();
+    private InterfaceOptimizer m_SOOptimizer = new GeneticAlgorithm();
+    private InterfaceOptimizer[] m_SOOptimizers;
+    private Population m_Population = new Population();
+    private int m_MigrationRate = 5;
+    private int m_OutputDimension = 2;
+    private int m_NumberOfLocalOptimizers = 2;
+    private InterfaceOptimizationProblem m_Problem = new FM0Problem();
+    private String m_Identifier = "";
     transient private InterfacePopulationChangedEventListener m_Listener;
 
     public WingedMultiObjectiveEA() {
     }
 
     public WingedMultiObjectiveEA(WingedMultiObjectiveEA a) {
-        this.m_Problem                      = (InterfaceOptimizationProblem)a.m_Problem.clone();
-        this.m_MOOptimizer                  = (InterfaceOptimizer)a.m_MOOptimizer.clone();
-        this.m_SOOptimizer                  = (InterfaceOptimizer)a.m_SOOptimizer.clone();
+        this.m_Problem = (InterfaceOptimizationProblem) a.m_Problem.clone();
+        this.m_MOOptimizer = (InterfaceOptimizer) a.m_MOOptimizer.clone();
+        this.m_SOOptimizer = (InterfaceOptimizer) a.m_SOOptimizer.clone();
         if (a.m_SOOptimizers != null) {
             this.m_SOOptimizers = new InterfaceOptimizer[a.m_SOOptimizers.length];
             for (int i = 0; i < this.m_SOOptimizers.length; i++) {
-                this.m_SOOptimizers[i]      = (InterfaceOptimizer)a.m_SOOptimizers[i].clone();
+                this.m_SOOptimizers[i] = (InterfaceOptimizer) a.m_SOOptimizers[i].clone();
             }
         }
-        this.m_MigrationRate                = a.m_MigrationRate;
-        this.m_Population                   = (Population)a.m_Population.clone();
+        this.m_MigrationRate = a.m_MigrationRate;
+        this.m_Population = (Population) a.m_Population.clone();
     }
 
     @Override
@@ -61,18 +58,18 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
     @Override
     public void init() {
         if (this.m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) {
-            AbstractMultiObjectiveOptimizationProblem   tmpProb = (AbstractMultiObjectiveOptimizationProblem)this.m_Problem;
-            AbstractMultiObjectiveOptimizationProblem   tmpP;
-            MOSOWeightedFitness                         tmpWF;
-            PropertyDoubleArray                         tmpDA;
-            int         dim = this.m_OutputDimension;
-            double[]    weights;
+            AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem;
+            AbstractMultiObjectiveOptimizationProblem tmpP;
+            MOSOWeightedFitness tmpWF;
+            PropertyDoubleArray tmpDA;
+            int dim = this.m_OutputDimension;
+            double[] weights;
             // dim = tmpProb.getOutputDimension();
-            this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem)this.m_Problem.clone());
+            this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem) this.m_Problem.clone());
             this.m_MOOptimizer.init();
             this.m_SOOptimizers = new InterfaceOptimizer[dim];
             for (int i = 0; i < dim; i++) {
-                tmpP = (AbstractMultiObjectiveOptimizationProblem)this.m_Problem.clone();
+                tmpP = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem.clone();
                 weights = new double[dim];
                 for (int j = 0; j < dim; j++) {
                     weights[j] = 0;
@@ -82,7 +79,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
                 tmpWF = new MOSOWeightedFitness();
                 tmpWF.setWeights(tmpDA);
                 tmpP.setMOSOConverter(tmpWF);
-                this.m_SOOptimizers[i] = (InterfaceOptimizer)this.m_SOOptimizer.clone();
+                this.m_SOOptimizers[i] = (InterfaceOptimizer) this.m_SOOptimizer.clone();
                 this.m_SOOptimizers[i].setProblem(tmpP);
                 this.m_SOOptimizers[i].init();
             }
@@ -94,26 +91,27 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-
-    /** This method will init the optimizer with a given population
-     * @param pop       The initial population
-     * @param reset     If true the population is reset.
+    /**
+     * This method will init the optimizer with a given population
+     *
+     * @param pop The initial population
+     * @param reset If true the population is reset.
      */
     @Override
     public void initByPopulation(Population pop, boolean reset) {
         if (this.m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) {
-            AbstractMultiObjectiveOptimizationProblem   tmpProb = (AbstractMultiObjectiveOptimizationProblem)this.m_Problem;
-            AbstractMultiObjectiveOptimizationProblem   tmpP;
-            MOSOWeightedFitness                         tmpWF;
-            PropertyDoubleArray                         tmpDA;
-            int         dim = 2;
-            double[]    weights;
+            AbstractMultiObjectiveOptimizationProblem tmpProb = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem;
+            AbstractMultiObjectiveOptimizationProblem tmpP;
+            MOSOWeightedFitness tmpWF;
+            PropertyDoubleArray tmpDA;
+            int dim = 2;
+            double[] weights;
             // dim = tmpProb.getOutputDimension();
-            this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem)this.m_Problem.clone());
+            this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem) this.m_Problem.clone());
             this.m_MOOptimizer.initByPopulation(pop, reset);
             this.m_SOOptimizers = new InterfaceOptimizer[dim];
             for (int i = 0; i < dim; i++) {
-                tmpP = (AbstractMultiObjectiveOptimizationProblem)this.m_Problem.clone();
+                tmpP = (AbstractMultiObjectiveOptimizationProblem) this.m_Problem.clone();
                 weights = new double[dim];
                 for (int j = 0; j < dim; j++) {
                     weights[j] = 0;
@@ -123,7 +121,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
                 tmpWF = new MOSOWeightedFitness();
                 tmpWF.setWeights(tmpDA);
                 tmpP.setMOSOConverter(tmpWF);
-                this.m_SOOptimizers[i] = (InterfaceOptimizer)this.m_SOOptimizer.clone();
+                this.m_SOOptimizers[i] = (InterfaceOptimizer) this.m_SOOptimizer.clone();
                 this.m_SOOptimizers[i].setProblem(tmpP);
                 this.m_SOOptimizers[i].initByPopulation(pop, reset);
             }
@@ -135,7 +133,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** The optimize method will compute a 'improved' and evaluated population
+    /**
+     * The optimize method will compute a 'improved' and evaluated population
      */
     @Override
     public void optimize() {
@@ -154,8 +153,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
 
-    /** This method will manage comunication between the
-     * islands
+    /**
+     * This method will manage comunication between the islands
      */
     private void communicate() {
         int oldFunctionCalls;
@@ -163,11 +162,11 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         this.m_Population.SetFunctionCalls(0);
         Population pop;
         // first collect all the data
-        pop = (Population)this.m_MOOptimizer.getPopulation().clone();
+        pop = (Population) this.m_MOOptimizer.getPopulation().clone();
         this.m_Population.addPopulation(pop);
         this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         for (int i = 0; i < this.m_SOOptimizers.length; i++) {
-            pop = (Population)this.m_SOOptimizers[i].getPopulation().clone();
+            pop = (Population) this.m_SOOptimizers[i].getPopulation().clone();
             this.m_Population.addPopulation(pop);
             this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
@@ -180,75 +179,86 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         this.migrate();
     }
 
-    /** This method implements the migration between the optimzers
+    /**
+     * This method implements the migration between the optimzers
      *
      */
     private void migrate() {
         AbstractEAIndividual[] bestIndys = new AbstractEAIndividual[this.m_OutputDimension];
-        double                 tmpF1, tmpF2;
+        double tmpF1, tmpF2;
         // for each dimension find the best
         for (int i = 0; i < this.m_OutputDimension; i++) {
-            bestIndys[i] = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Population.get(0)).clone();
+            bestIndys[i] = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(0)).clone();
             tmpF1 = bestIndys[i].getFitness(i);
             // for each individual find the best
             for (int j = 0; j < this.m_Population.size(); j++) {
-                 if (((AbstractEAIndividual)this.m_Population.get(j)).getFitness(i) < tmpF1) {
-                     bestIndys[i] = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Population.get(j)).clone();
-                     tmpF1 = bestIndys[i].getFitness(i);
-                 }
+                if (((AbstractEAIndividual) this.m_Population.get(j)).getFitness(i) < tmpF1) {
+                    bestIndys[i] = (AbstractEAIndividual) ((AbstractEAIndividual) this.m_Population.get(j)).clone();
+                    tmpF1 = bestIndys[i].getFitness(i);
+                }
             }
         }
         // now perform the migration
         AbstractEAIndividual tmpIndy;
         for (int i = 0; i < this.m_OutputDimension; i++) {
-            tmpIndy = (AbstractEAIndividual)bestIndys[i].clone();
+            tmpIndy = (AbstractEAIndividual) bestIndys[i].clone();
             this.m_MOOptimizer.getProblem().evaluate(tmpIndy);
             this.m_MOOptimizer.getPopulation().set(i, tmpIndy);
-            tmpIndy = (AbstractEAIndividual)bestIndys[i].clone();
+            tmpIndy = (AbstractEAIndividual) bestIndys[i].clone();
             this.m_SOOptimizers[i].getProblem().evaluate(tmpIndy);
             this.m_SOOptimizers[i].getPopulation().set(0, bestIndys[i]);
         }
     }
 
-    /** This method allows you to add the LectureGUI as listener to the Optimizer
+    /**
+     * This method allows you to add the LectureGUI as listener to the Optimizer
+     *
      * @param ea
      */
     @Override
     public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
         this.m_Listener = ea;
     }
+
     @Override
-	public boolean removePopulationChangedEventListener(
-			InterfacePopulationChangedEventListener ea) {
-		if (m_Listener==ea) {
-			m_Listener=null;
-			return true;
-		} else {
-                                return false;
-                            }
-	}
-    /** Something has changed
+    public boolean removePopulationChangedEventListener(
+            InterfacePopulationChangedEventListener ea) {
+        if (m_Listener == ea) {
+            m_Listener = null;
+            return true;
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Something has changed
      */
-    protected void firePropertyChangedEvent (String name) {
+    protected void firePropertyChangedEvent(String name) {
         if (this.m_Listener != null) {
             this.m_Listener.registerPopulationStateChanged(this, name);
         }
     }
 
-    /** This method will set the problem that is to be optimized
+    /**
+     * This method will set the problem that is to be optimized
+     *
      * @param problem
      */
     @Override
-    public void setProblem (InterfaceOptimizationProblem problem) {
+    public void setProblem(InterfaceOptimizationProblem problem) {
         this.m_Problem = problem;
     }
+
     @Override
-    public InterfaceOptimizationProblem getProblem () {
+    public InterfaceOptimizationProblem getProblem() {
         return this.m_Problem;
     }
 
-    /** This method will return a string describing all properties of the optimizer
-     * and the applied methods.
+    /**
+     * This method will return a string describing all properties of the
+     * optimizer and the applied methods.
+     *
      * @return A descriptive string
      */
     @Override
@@ -256,39 +266,42 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         String result = "";
         result += "EMO:\n";
         result += "Optimization Problem: ";
-        result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
+        result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
         result += this.m_Population.getStringRepresentation();
         return result;
     }
-    /** This method allows you to set an identifier for the algorithm
-     * @param name      The indenifier
+
+    /**
+     * This method allows you to set an identifier for the algorithm
+     *
+     * @param name The indenifier
      */
     @Override
-     public void setIdentifier(String name) {
+    public void setIdentifier(String name) {
         this.m_Identifier = name;
     }
-    @Override
-     public String getIdentifier() {
-         return this.m_Identifier;
-     }
 
-    /** This method is required to free the memory on a RMIServer,
-     * but there is nothing to implement.
-     */
     @Override
-    public void freeWilly() {
-
+    public String getIdentifier() {
+        return this.m_Identifier;
     }
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /** This method returns a global info string
+
+    /**
+     * ********************************************************************************************************************
+     * These are for GUI
+     */
+    /**
+     * This method returns a global info string
+     *
      * @return description
      */
     public static String globalInfo() {
         return "This is Evolutionary Multi-Criteria Optimization Algorithm hybridized with Local Searchers to span the Pareto-Front.";
     }
-    /** This method will return a naming String
+
+    /**
+     * This method will return a naming String
+     *
      * @return The name of the algorithm
      */
     @Override
@@ -296,62 +309,79 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         return "EMO-LS";
     }
 
-    /** Assuming that all optimizer will store their data in a population
-     * we will allow access to this population to query to current state
-     * of the optimizer.
+    /**
+     * Assuming that all optimizer will store their data in a population we will
+     * allow access to this population to query to current state of the
+     * optimizer.
+     *
      * @return The population of current solutions to a given problem.
      */
     @Override
     public Population getPopulation() {
         return this.m_Population;
     }
+
     @Override
-    public void setPopulation(Population pop){
+    public void setPopulation(Population pop) {
         this.m_Population = pop;
     }
+
     public String populationTipText() {
         return "(Defunct)";
     }
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
+        return new SolutionSet(getPopulation());
     }
-    /** This method allows you to set/get the optimizing technique to use.
+
+    /**
+     * This method allows you to set/get the optimizing technique to use.
+     *
      * @return The current optimizing method
      */
     public InterfaceOptimizer getMOOptimizer() {
         return this.m_MOOptimizer;
     }
-    public void setMOOptimizer(InterfaceOptimizer b){
+
+    public void setMOOptimizer(InterfaceOptimizer b) {
         this.m_MOOptimizer = b;
     }
+
     public String mOOptimizerTipText() {
         return "Choose a population based optimizing technique to use.";
     }
 
-    /** This method allows you to set/get the optimizing technique to use.
+    /**
+     * This method allows you to set/get the optimizing technique to use.
+     *
      * @return The current optimizing method
      */
     public InterfaceOptimizer getSOOptimizer() {
         return this.m_SOOptimizer;
     }
-    public void setSOOptimizer(InterfaceOptimizer b){
+
+    public void setSOOptimizer(InterfaceOptimizer b) {
         this.m_SOOptimizer = b;
     }
+
     public String sOOptimizerTipText() {
         return "Choose a population based optimizing technique to use.";
     }
 
-    /** This method allows you to set/get the archiving strategy to use.
+    /**
+     * This method allows you to set/get the archiving strategy to use.
+     *
      * @return The current optimizing method
      */
     public int getMigrationRate() {
         return this.m_MigrationRate;
     }
-    public void setMigrationRate(int b){
+
+    public void setMigrationRate(int b) {
         this.m_MigrationRate = b;
     }
+
     public String migrationRateTipText() {
         return "Choose a proper migration rate.";
     }