Merging mk branch rev 130.

src/eva2/OptimizerRunnable.java

@@ -16,9 +16,11 @@ import eva2.server.go.populations.Population;
 import eva2.server.modules.GOParameters;
 import eva2.server.modules.Processor;
 import eva2.server.stat.AbstractStatistics;
+import eva2.server.stat.InterfaceStatistics;
 import eva2.server.stat.StatisticsDummy;
 import eva2.server.stat.InterfaceTextListener;
 import eva2.server.stat.StatisticsStandalone;
+import eva2.server.stat.StatsParameter;
 
 
 /**
@@ -77,6 +79,10 @@ public class OptimizerRunnable implements Runnable {
 		return proc.getGOParams();
 	}
 	
+	public InterfaceStatistics getStats() {
+		return proc.getStatistics();
+	}
+	
 	public void setTextListener(InterfaceTextListener lsnr) {
 		proc.getStatistics().removeTextListener(listener);
 		this.listener = lsnr;
@@ -175,7 +181,9 @@ public class OptimizerRunnable implements Runnable {
 	}
 	
 	/**
-	 * Set the verbosity level in the statistics module to the given value. See StatsParameter.
+	 * Set the verbosity level in the statistics module to the given value.
+	 * 
+	 * @see StatsParameter
 	 * @param vLev
 	 */
 	public void setVerbosityLevel(int vLev) {
@@ -183,4 +191,35 @@ public class OptimizerRunnable implements Runnable {
 			proc.getStatistics().getStatisticsParameter().getOutputVerbosity().setSelectedTag(vLev);
 		} else System.err.println("Invalid verbosity leveln in OptimizerRunnable.setVerbosityLevel!");
 	}
+	
+	/**
+	 * Set the output direction in the statistics module.
+	 * 
+	 * @see StatsParameter
+	 * @param outp
+	 */
+	public void setOutputTo(int outp) {
+		((StatsParameter)proc.getStatistics().getStatisticsParameter()).setOutputTo(outp);
+	}
+	
+	/**
+	 * Set the number of multiruns in the statistics module.
+	 * @param multis
+	 */
+	public void setMultiRuns(int multis) {
+		((AbstractStatistics)proc.getStatistics()).getStatisticsParameter().setMultiRuns(multis);
+	}
+	
+	/**
+	 * Set the additional info output.
+	 * @see StatsParameter
+	 * @param addInfo
+	 */
+	public void setOutputAdditionalInfo(boolean addInfo) {
+		((AbstractStatistics)proc.getStatistics()).getStatisticsParameter().setOutputAdditionalInfo(addInfo);
+	}
+	
+//	public void configureStats(int verbosityLevel, int outputDirection, int multiRuns, boolean additionalInfo) {
+//		asdf
+//	}
 }

src/eva2/server/go/individuals/AbstractEAIndividual.java

@@ -3,6 +3,7 @@ package eva2.server.go.individuals;
 
 import java.util.ArrayList;
 import java.util.BitSet;
+import java.util.HashMap;
 import java.util.List;
 
 import wsi.ra.math.RNG;
@@ -57,8 +58,9 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     public double                           m_MutationProbability   = 0.2;
     protected InterfaceMutation             m_MutationOperator      = new NoMutation();
     protected InterfaceCrossover            m_CrossoverOperator     = new NoCrossover();
-    protected String[]                      m_Identifiers           = new String[m_ObjectIncrement];
+//    protected String[]                      m_Identifiers           = new String[m_ObjectIncrement];
-    protected Object[]                      m_Objects               = new Object[m_ObjectIncrement];
+//    protected Object[]                      m_Objects               = new Object[m_ObjectIncrement];
+    protected HashMap<String,Object> 		m_dataHash 				= new HashMap<String,Object>();
     
     public AbstractEAIndividual() {
     	m_IDcounter++;
@@ -111,8 +113,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      */
     public void cloneAEAObjects(AbstractEAIndividual individual) {
 //        m_Name              = new String(individual.m_Name);
-        m_Identifiers       = new String[individual.m_Identifiers.length];
+    	m_dataHash			= (HashMap<String,Object>)(individual.m_dataHash.clone());
-        m_Objects           = new Object[individual.m_Identifiers.length];
         m_ConstraintViolation = individual.m_ConstraintViolation;
         m_AreaConst4ParallelViolated = individual.m_AreaConst4ParallelViolated;
         m_Marked            = individual.m_Marked;
@@ -122,8 +123,6 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
         	parentTree = new AbstractEAIndividual[individual.parentTree.length];
         	for (int i=0; i<parentTree.length; i++) parentTree[i] = individual.parentTree[i];
         }
-        System.arraycopy(individual.m_Identifiers,0,m_Identifiers,0,individual.m_Identifiers.length);
-        System.arraycopy(individual.m_Objects,0,m_Objects,0,individual.m_Objects.length);
     }
 
     /** This method allows you to compare two individuals
@@ -199,11 +198,12 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
 //        }
     }
 
-    /** Returns a hash code value for the object. This method is supported for the
+    /** 
+     * Returns a hash code value for the object. This method is supported for the
      * benefit of hashtables such as those provided by java.util.Hashtable
      */
     public int hashCode() {
-        String t = this.getStringRepresentation();
+        String t = AbstractEAIndividual.getDefaultStringRepresentation(this);
         return t.hashCode();
     }
 
@@ -412,8 +412,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     /** This method allows you to reset the user data
      */
     public void resetUserData() {
-        this.m_Identifiers  = new String[m_ObjectIncrement];
+    	m_dataHash.clear();
-        this.m_Objects      = new Object[m_ObjectIncrement];
     }
 
     /** This method allows you to reset the level of constraint violation for an
@@ -741,28 +740,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @param obj       The object that is to be stored.
      */
     public void SetData(String name, Object obj) {
-        for (int i = 0; i < this.m_Identifiers.length; i++) {
+   		m_dataHash.put(name, obj);
-            if (this.m_Identifiers[i] == null) {
-                // Identifier has not been found, but there is empty space to store it
-                this.m_Identifiers[i]   = name;
-                this.m_Objects[i]       = obj;
-                return;
-            }
-            if (this.m_Identifiers[i].equalsIgnoreCase(name)) {
-                this.m_Objects[i] = obj;
-                return;
-            }
-        }
-        // The identifier could not be found and there is not enough space
-        String[] tmpId = new String[this.m_Identifiers.length + this.m_ObjectIncrement];
-        Object[] tmpOb = new Object[this.m_Identifiers.length + this.m_ObjectIncrement];
-        for (int i = 0; i < this.m_Identifiers.length; i++) {
-            tmpId[i] = this.m_Identifiers[i];
-            tmpOb[i] = this.m_Objects[i];
-        }
-        this.m_Identifiers = tmpId;
-        this.m_Objects = tmpOb;
-        this.SetData(name, obj);
     }
 
     /** This method will return a stored object.
@@ -770,19 +748,13 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @return Object
      */
     public Object getData(String name) {
-        if (name.equalsIgnoreCase("SelectionProbability")) return this.getSelectionProbability();
+//        if (name.equalsIgnoreCase("SelectionProbability")) return this.getSelectionProbability();
-        if (name.equalsIgnoreCase("SelectionProbabilityArray")) return this.getSelectionProbability();
+//        if (name.equalsIgnoreCase("SelectionProbabilityArray")) return this.getSelectionProbability();
-        if (name.equalsIgnoreCase("Fitness")) return this.getFitness();
+//        if (name.equalsIgnoreCase("Fitness")) return this.getFitness();
-        if (name.equalsIgnoreCase("FitnessArray")) return this.getFitness();
+//        if (name.equalsIgnoreCase("FitnessArray")) return this.getFitness();
-        for (int i = 0; i < this.m_Identifiers.length; i++) {
+    	Object data = m_dataHash.get(name);
-            if (this.m_Identifiers[i] == null) {
+    	if (data==null) System.err.println("Warning: data key " + name + " unknown!");
-                return null;
+        return data;
-            }
-            if (this.m_Identifiers[i].equalsIgnoreCase(name)) {
-                return this.m_Objects[i];
-            }
-        }
-        return null;
     }
 
     /** This method will return a string description of the Individal
@@ -799,6 +771,8 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @return The description.
      */
     public static String getDefaultStringRepresentation(AbstractEAIndividual individual) {
+    	// Note that changing this method might change the hashcode of an individual 
+    	// which might interfere with some functionality.
         StringBuffer sb = new StringBuffer(getDefaultDataString(individual));
 
         sb.append(", fitness: ");
@@ -818,6 +792,8 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @return
      */
     public static String getDefaultDataString(IndividualInterface individual) {
+    	// Note that changing this method might change the hashcode of an individual 
+    	// which might interfere with some functionality.
     	return getDefaultDataString(individual, "; ");
     }
     
@@ -829,6 +805,8 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
      * @return
      */
     public static String getDefaultDataString(IndividualInterface individual, String separator) {
+    	// Note that changing this method might change the hashcode of an individual 
+    	// which might interfere with some functionality.
     	if (individual == null) return "null";
         StringBuffer sb = new StringBuffer("");
         char left = '[';

src/eva2/server/go/populations/Population.java

@@ -17,12 +17,13 @@ import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
 import eva2.tools.EVAERROR;
 import eva2.tools.Mathematics;
+import eva2.tools.Pair;
 
 
 /** This is a basic implementation for a EA Population.
  * Copyright:       Copyright (c) 2003
  * Company:         University of Tuebingen, Computer Architecture
- * @author          Felix Streichert
+ * @author          Felix Streichert, Marcel Kronfeld
  * @version:  $Revision: 307 $
  *            $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
  *            $Author: mkron $
@@ -34,9 +35,11 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     protected int           m_FunctionCalls = 0;
     protected int           m_Size          = 50;
     protected Population    m_Archive       = null;
+
     transient private ArrayList<AbstractEAIndividual> sortedArr = null;
-    private int lastQModCount = -1;
     transient protected InterfacePopulationChangedEventListener	m_Listener = null;
+
+    // the evaluation interval at which listeners are notified
     protected int 			notifyEvalInterval	= 0;
     protected HashMap<String, Object>		additionalPopData = null;
     
@@ -45,6 +48,13 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     boolean useHistory						= false;
     public ArrayList<AbstractEAIndividual>  m_History       = new ArrayList<AbstractEAIndividual>();
 
+    // remember when the last sorted queue was prepared
+    private int lastQModCount = -1;
+    // remember when the last evaluation was performed
+	private Pair<Integer,Integer> evaluationTimeHashes = null;
+    // remember when the last evaluation was performed
+	private int evaluationTimeModCount = -1;
+
     public Population() {
     }
     
@@ -129,6 +139,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         this.m_History = new ArrayList();
         this.m_Generation       = 0;
         this.m_FunctionCalls    = 0;
+    	evaluationTimeHashes = null;
+    	evaluationTimeModCount = -1;
         if (this.m_Archive != null) {
             this.m_Archive.clear();
             this.m_Archive.init();
@@ -178,7 +190,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * 
      * @param d     The number of function calls to increment.
      */
-    public void incrFunctionCallsby(int d) {
+    public void incrFunctionCallsBy(int d) {
     	if (doEvalNotify()) {
 //    		System.out.println("checking funcall event...");
     		int nextStep; // next interval boundary
@@ -271,7 +283,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     }
     
     /**
-     * Resets the fitness to the maximum possible value for the given individual.
+     * Resets the fitnes to the maximum possible value for the given individual.
      *
      * @param indy	an individual whose fitness will be reset
      */
@@ -915,4 +927,39 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 	public void setNotifyEvalInterval(int notifyEvalInterval) {
 		this.notifyEvalInterval = notifyEvalInterval;
 	}
+
+	/**
+	 * Check whether the population at the current state has been marked as
+	 * evaluated. This allows to avoid double evaluations. 
+	 * 
+	 * @return true if the population has been marked as evaluated in its current state, else false 
+	 */
+	public boolean isEvaluated() {
+		Pair<Integer,Integer> hashes = getIndyHashSums();
+		
+		if (evaluationTimeHashes == null) return false;
+		else return (hashes.head()==evaluationTimeHashes.head() && (hashes.tail() == evaluationTimeHashes.tail()) && (evaluationTimeModCount == modCount));
+	}
+
+	/**
+	 * Mark the population at the current state as evaluated. Changes to the modCount or hashes of individuals
+	 * will invalidate the mark.
+	 *  
+	 * @see  isEvaluated()
+	 */
+	public void setEvaluated() {
+		evaluationTimeModCount = modCount;
+		evaluationTimeHashes = getIndyHashSums();
+	}
+	
+	private Pair<Integer,Integer> getIndyHashSums() {
+		int hashSum = 0, hashSumAbs = 0;
+		int hash;
+		for (int i=0; i<size(); i++) {
+			hash = get(i).hashCode();
+			hashSum += hash;
+			hashSumAbs += Math.abs(hash);
+		}
+		return new Pair(hashSum, hashSumAbs);
+	}
 }

src/eva2/server/go/problems/AbstractOptimizationProblem.java

@@ -53,16 +53,21 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
     public void evaluate(Population population) {
         AbstractEAIndividual    tmpIndy;
 
-        // @todo This is the position to implement a granular
+        if (population.isEvaluated()) {
-        // @todo paralliziation scheme
+        	System.err.println("Population evaluation seems not required!");
-        evaluatePopulationStart(population);
+        } else {
-        for (int i = 0; i < population.size(); i++) {
+	        // @todo This is the position to implement a granular
-            tmpIndy = (AbstractEAIndividual) population.get(i);
+	        // @todo paralliziation scheme
-            tmpIndy.resetConstraintViolation();
+	        evaluatePopulationStart(population);
-            this.evaluate(tmpIndy);
+	        for (int i = 0; i < population.size(); i++) {
-            population.incrFunctionCalls();
+	            tmpIndy = (AbstractEAIndividual) population.get(i);
+	            tmpIndy.resetConstraintViolation();
+	            this.evaluate(tmpIndy);
+	            population.incrFunctionCalls();
+	        }
+	        evaluatePopulationEnd(population);
+	        population.setEvaluated();
         }
-        evaluatePopulationEnd(population);
     }
     
     /**

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

@@ -425,7 +425,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                     }
                     // reinit the surplus individuals and add these new individuals to undifferentiated
                     m_Undifferentiated.addPopulation(this.initializeIndividuals(reinitCount));
-                    m_Undifferentiated.incrFunctionCallsby(reinitCount);
+                    m_Undifferentiated.incrFunctionCallsBy(reinitCount);
                     m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()+reinitCount);
 //                    if (this.m_Debug) {
 //                        System.out.println("Undiff.Size: " + this.m_Undifferentiated.size() +"/"+this.m_Undifferentiated.getPopulationSize());
@@ -454,12 +454,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         //////////////////////
     	if (!isActive(m_Undifferentiated)) {
     		if (TRACE) System.out.println("Inactive Undiff-pop, adding " + m_Undifferentiated.size() + " fun calls...");
-    		m_Undifferentiated.incrFunctionCallsby(m_Undifferentiated.size());
+    		m_Undifferentiated.incrFunctionCallsBy(m_Undifferentiated.size());
     	}
         if (this.m_Undifferentiated.getFunctionCalls() % this.m_PopulationSize != 0) {
         	if (TRACE) System.out.println("### mismatching number of funcalls, inactive species? Correcting by " + (m_PopulationSize - (m_Undifferentiated.getFunctionCalls() % m_PopulationSize)));
         	if (TRACE) System.out.println("### undiff " + ((isActive(m_Undifferentiated)) ? "active!" : "inactive!"));
-        	m_Undifferentiated.incrFunctionCallsby(m_PopulationSize - (m_Undifferentiated.getFunctionCalls() % m_PopulationSize));
+        	m_Undifferentiated.incrFunctionCallsBy(m_PopulationSize - (m_Undifferentiated.getFunctionCalls() % m_PopulationSize));
         } else if (TRACE) System.out.println("### undiff active: " + isActive(m_Undifferentiated));        
         
         // possible species differentiation and convergence

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

@@ -166,7 +166,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 				// reset population while keeping function calls etc.
 				m_Population.clear();
 				m_Population.addPopulation(tmpPop);
-				m_Population.incrFunctionCallsby(tmpPop.size());
+				m_Population.incrFunctionCallsBy(tmpPop.size());
 		
 			} else  {  // decrease step size
 				mutator.setSigma(mutator.getSigma()*reduceFactor);

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

@@ -63,8 +63,8 @@ public interface InterfaceOptimizer {
      * Return all found solutions (local optima) if they are not contained in the current population. Be
      * sure to set the Population properties, especially function calls and generation, with respect
      * to the ongoing optimization.
-     * May return the same as getPopulation if the optimizer makes no distinction, i.e. does not collect
+     * May return the the same set as getPopulation if the optimizer makes no distinction, i.e. does 
-     * solutions outside the current population.
+     * not collect solutions outside the current population.
      * 
      * @return A population of found solutions.
      */

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

@@ -133,7 +133,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         for (int i = 0; i < this.m_Islands.length; i++) {
             pop = (Population)this.m_Islands[i].getPopulation().clone();
             this.m_Population.addPopulation(pop);
-            this.m_Population.incrFunctionCallsby(pop.getFunctionCalls());
+            this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
         this.firePropertyChangedEvent("NextGenerationPerformed", this.m_Optimizer.getPopulation());
     }
@@ -192,7 +192,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         for (int i = 0; i < this.m_Islands.length; i++) {
             pop = (Population)this.m_Islands[i].getPopulation().clone();
             this.m_Population.addPopulation(pop);
-            this.m_Population.incrFunctionCallsby(pop.getFunctionCalls());
+            this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
         this.firePropertyChangedEvent("NextGenerationPerformed", this.m_Optimizer.getPopulation());
     }
@@ -246,7 +246,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         for (int i = 0; i < this.m_Islands.length; i++) {
             pop = (Population)this.m_Islands[i].getPopulation().clone();
             this.m_Population.addPopulation(pop);
-            this.m_Population.incrFunctionCallsby(pop.getFunctionCalls());
+            this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
 //        System.out.println("Fitnesscalls :" + this.m_Population.getFunctionCalls());
         this.firePropertyChangedEvent("NextGenerationPerformed", this.m_Optimizer.getPopulation());

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

@@ -107,7 +107,7 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
 
         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.incrFunctionCallsBy(this.m_Population.size());
             this.m_Population.incrGeneration();
             this.firePropertyChangedEvent("NextGenerationPerformed");
         }

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

@@ -162,11 +162,11 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         // first collect all the data
         pop = (Population)this.m_MOOptimizer.getPopulation().clone();
         this.m_Population.addPopulation(pop);
-        this.m_Population.incrFunctionCallsby(pop.getFunctionCalls());
+        this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         for (int i = 0; i < this.m_SOOptimizers.length; i++) {
             pop = (Population)this.m_SOOptimizers[i].getPopulation().clone();
             this.m_Population.addPopulation(pop);
-            this.m_Population.incrFunctionCallsby(pop.getFunctionCalls());
+            this.m_Population.incrFunctionCallsBy(pop.getFunctionCalls());
         }
         oldFunctionCalls = this.m_Population.getFunctionCalls();
         this.m_Problem.evaluate(this.m_Population);

src/eva2/server/stat/AbstractStatistics.java

@@ -150,8 +150,9 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 			if (Mathematics.norm(bestCurrentIndividual.getFitness()) < this.m_StatsParams.getConvergenceRateThreshold()) {
 				convergenceCnt++;
 			}
-			if (printRunStoppedVerbosity()) printToTextListener(" Best solution: " + BeanInspector.toString(bestCurrentIndividual) + "\n");
+			if (printRunStoppedVerbosity()) printToTextListener(" Run best individual	: " + BeanInspector.toString(bestCurrentIndividual) + "\n");
-			if (printRunStoppedVerbosity()) printToTextListener(AbstractEAIndividual.getDefaultDataString(bestCurrentIndividual) + "\n");
+			if (printRunStoppedVerbosity()) printToTextListener("	run solution data	: " + AbstractEAIndividual.getDefaultDataString(bestCurrentIndividual) + "\n");
+			if (printRunStoppedVerbosity()) printToTextListener("	run solution fit	: " + BeanInspector.toString(bestCurrentIndividual.getFitness()) + "\n");
 		}
 		if (currentBestFit!= null) {
 			if (printRunStoppedVerbosity()) printToTextListener(" Best Fitness: " + BeanInspector.toString(currentBestFit) + "\n");
@@ -162,9 +163,9 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 	protected void finalizeOutput() {
 		if (printFinalVerbosity()) printToTextListener("*******\n Runs performed: " + optRunsPerformed + ", reached target " + convergenceCnt + " times with threshold " + m_StatsParams.getConvergenceRateThreshold() + ", rate " + convergenceCnt/(double)m_StatsParams.getMultiRuns() + '\n');
 		if (printFinalVerbosity()) printToTextListener(" Average function calls: " + (functionCallSum/optRunsPerformed) + "\n");
-		if (printFinalVerbosity() && (bestIndividualAllover != null)) printToTextListener("Best overall individual: " + BeanInspector.toString(bestIndividualAllover) + '\n');
+		if (printFinalVerbosity() && (bestIndividualAllover != null)) printToTextListener(" Overall best individual		: " + BeanInspector.toString(bestIndividualAllover) + '\n');
-		if (printFinalVerbosity() && (bestIndividualAllover != null)) printToTextListener("             solution	: " + AbstractEAIndividual.getDefaultDataString(bestIndividualAllover) + '\n');
+		if (printFinalVerbosity() && (bestIndividualAllover != null)) printToTextListener("     overall solution data	: " + AbstractEAIndividual.getDefaultDataString(bestIndividualAllover) + '\n');
-		if (printFinalVerbosity() && (bestIndividualAllover != null)) printToTextListener("             fitness	: " + BeanInspector.toString(bestIndividualAllover.getFitness()) + '\n');
+		if (printFinalVerbosity() && (bestIndividualAllover != null)) printToTextListener("     overall solution fit	: " + BeanInspector.toString(bestIndividualAllover.getFitness()) + '\n');
 		if (refineMultiRuns && (optRunsPerformed>1) && (meanCollection != null)) {
 			if (printFinalVerbosity()) printToTextListener("Averaged performance:\n");
 			for (int i=0; i<meanCollection.size(); i++) divideMean(meanCollection.get(i), optRunsPerformed);

src/eva2/server/stat/StatsParameter.java

@@ -29,318 +29,322 @@ import eva2.tools.Tag;
  *
  */
 public class StatsParameter implements InterfaceStatisticsParameter, Serializable {
-  public final static int PLOT_BEST = 0;
+	public final static int PLOT_BEST = 0;
-  public final static int PLOT_WORST = 1;
+	public final static int PLOT_WORST = 1;
-  public final static int PLOT_BEST_AND_WORST = 2;
+	public final static int PLOT_BEST_AND_WORST = 2;
-  public final static int PLOT_BEST_AND_MEASURES = 3;
+	public final static int PLOT_BEST_AND_MEASURES = 3;
-  public final static Tag[] TAGS_PLOT_FITNESS = {
+	public final static Tag[] TAGS_PLOT_FITNESS = {
-                                                new Tag(PLOT_BEST, "plot best fitness"),
+		new Tag(PLOT_BEST, "plot best fitness"),
-                                                new Tag(PLOT_WORST, "plot worst fitness"),
+		new Tag(PLOT_WORST, "plot worst fitness"),
-                                                new Tag(PLOT_BEST_AND_WORST, "both best and worst"),
+		new Tag(PLOT_BEST_AND_WORST, "both best and worst"),
-                                                new Tag(PLOT_BEST_AND_MEASURES, "both best and population measures")
+		new Tag(PLOT_BEST_AND_MEASURES, "both best and population measures")
-  };
+	};
 
-  public final static int VERBOSITY_NONE = 0;
+	public final static int VERBOSITY_NONE = 0;
-  public final static int VERBOSITY_FINAL = 1;
+	public final static int VERBOSITY_FINAL = 1;
-  public final static int VERBOSITY_KTH_IT = 2;
+	public final static int VERBOSITY_KTH_IT = 2;
-  public final static int VERBOSITY_ALL = 3;
+	public final static int VERBOSITY_ALL = 3;
-  SelectedTag outputVerbosity = new SelectedTag("No output", "Final results", "K-th iterations", "All iterations");
+	SelectedTag outputVerbosity = new SelectedTag("No output", "Final results", "K-th iterations", "All iterations");
-  SelectedTag outputTo = new SelectedTag("File (current dir.)", "Text-window", "Both file and text-window");
-  private int verboK = 10;
 
-  private int m_PlotFitness = PLOT_BEST;
+	public final static int OUTPUT_FILE = 0;
-  private int m_Textoutput = 0;
+	public final static int OUTPUT_WINDOW = 1;
-  private int m_Plotoutput = 1;
+	public final static int OUTPUT_FILE_WINDOW = 2;
-  private int m_MultiRuns = 1;
+	SelectedTag outputTo = new SelectedTag("File (current dir.)", "Text-window", "Both file and text-window");
-  private String m_ResultFilePrefix = "JE2";
+	private int verboK = 10;
-  protected String m_Name = "not defined";
-  protected String m_InfoString = "";
-  private boolean m_useStatPlot = true;
-  private boolean showAdditionalProblemInfo = false;
-  private double m_ConvergenceRateThreshold=0.001;
 
-  /**
+	private int m_PlotFitness = PLOT_BEST;
-   *
+	private int m_Textoutput = 0;
-   */
+	private int m_Plotoutput = 1;
-  public static StatsParameter getInstance() {
+	private int m_MultiRuns = 1;
-    StatsParameter Instance = (StatsParameter) Serializer.loadObject("Statistics.ser");
+	private String m_ResultFilePrefix = "JE2";
-    if (Instance == null)
+	protected String m_Name = "not defined";
-      Instance = new StatsParameter();
+	protected String m_InfoString = "";
-    return Instance;
+	private boolean m_useStatPlot = true;
-  }
+	private boolean showAdditionalProblemInfo = false;
+	private double m_ConvergenceRateThreshold=0.001;
 
-  /**
+	/**
-   *
+	 *
-   */
+	 */
-  public StatsParameter() {
+	public static StatsParameter getInstance() {
-    m_Name = "Statistics";
+		StatsParameter Instance = (StatsParameter) Serializer.loadObject("Statistics.ser");
-    outputVerbosity.setSelectedTag(2);
+		if (Instance == null)
-    outputTo.setSelectedTag(1);
+			Instance = new StatsParameter();
-  }
+		return Instance;
+	}
 
-  /**
+	/**
-   *
+	 *
-   */
+	 */
-  public String toString() {
+	public StatsParameter() {
-    String ret = "\r\nStatParameter:\r\nm_MultiRuns=" + m_MultiRuns +
+		m_Name = "Statistics";
-                 "\r\nm_Textoutput=" + m_Textoutput +
+		outputVerbosity.setSelectedTag(2);
-                 "\r\nm_Plotoutput=" + m_Plotoutput;
+		outputTo.setSelectedTag(1);
-    return ret;
+	}
-  }
 
-  /**
+	/**
-   * Return a list of String arrays describing the selected plot options, e.g. {"Best"} or {"Best", "Worst"}.
+	 *
-   * For now, only one array is returned.
+	 */
-   * 
+	public String toString() {
-   * @return a list of String arrays describing the selected plot options
+		String ret = "\r\nStatParameter:\r\nm_MultiRuns=" + m_MultiRuns +
-   */
+		"\r\nm_Textoutput=" + m_Textoutput +
-  public ArrayList<String[]> getPlotDescriptions() {
+		"\r\nm_Plotoutput=" + m_Plotoutput;
-	  ArrayList<String[]> desc = new ArrayList<String[]>();
+		return ret;
-	  switch (getPlotData().getSelectedTagID()) {
+	}
-	  case StatsParameter.PLOT_BEST_AND_WORST:
-		  desc.add(new String[] {"Best", "Worst"});
-		  break;
-	  case StatsParameter.PLOT_BEST:
-		  desc.add(new String[] {"Best"});
-		  break;
-	  case StatsParameter.PLOT_WORST:
-		  desc.add(new String[] {"Worst"});
-		  break;
-	  case StatsParameter.PLOT_BEST_AND_MEASURES:
-		  desc.add(new String[] {"Best", "AvgDist", "MaxDist"});
-		  break;
-	  }
-	  return desc;
-  }
 
-  /**
+	/**
-   *
+	 * Return a list of String arrays describing the selected plot options, e.g. {"Best"} or {"Best", "Worst"}.
-   */
+	 * For now, only one array is returned.
-  public void saveInstance() {
+	 * 
-    Serializer.storeObject("Statistics.ser", this);
+	 * @return a list of String arrays describing the selected plot options
-  }
+	 */
+	public ArrayList<String[]> getPlotDescriptions() {
+		ArrayList<String[]> desc = new ArrayList<String[]>();
+		switch (getPlotData().getSelectedTagID()) {
+		case StatsParameter.PLOT_BEST_AND_WORST:
+			desc.add(new String[] {"Best", "Worst"});
+			break;
+		case StatsParameter.PLOT_BEST:
+			desc.add(new String[] {"Best"});
+			break;
+		case StatsParameter.PLOT_WORST:
+			desc.add(new String[] {"Worst"});
+			break;
+		case StatsParameter.PLOT_BEST_AND_MEASURES:
+			desc.add(new String[] {"Best", "AvgDist", "MaxDist"});
+			break;
+		}
+		return desc;
+	}
 
-  /**
+	/**
-   *
+	 *
-   */
+	 */
-  private StatsParameter(StatsParameter Source) {
+	public void saveInstance() {
-    m_ConvergenceRateThreshold = Source.m_ConvergenceRateThreshold;
+		Serializer.storeObject("Statistics.ser", this);
-    m_useStatPlot = Source.m_useStatPlot;
+	}
-    m_Textoutput = Source.m_Textoutput;
-    m_Plotoutput = Source.m_Plotoutput;
-    m_PlotFitness = Source.m_PlotFitness;
-    m_MultiRuns = Source.m_MultiRuns;
-    m_ResultFilePrefix = Source.m_ResultFilePrefix;
-    verboK = Source.verboK;
-  }
 
-  /**
+	/**
-   *
+	 *
-   */
+	 */
-  public Object getClone() {
+	private StatsParameter(StatsParameter Source) {
-    return new StatsParameter(this);
+		m_ConvergenceRateThreshold = Source.m_ConvergenceRateThreshold;
-  }
+		m_useStatPlot = Source.m_useStatPlot;
+		m_Textoutput = Source.m_Textoutput;
+		m_Plotoutput = Source.m_Plotoutput;
+		m_PlotFitness = Source.m_PlotFitness;
+		m_MultiRuns = Source.m_MultiRuns;
+		m_ResultFilePrefix = Source.m_ResultFilePrefix;
+		verboK = Source.verboK;
+	}
 
-  /**
+	/**
-   *
+	 *
-   */
+	 */
-  public String getName() {
+	public Object getClone() {
-    return m_Name;
+		return new StatsParameter(this);
-  }
+	}
 
-  public String globalInfo() {
+	/**
-    return "Configure statistics and output of the optimization run.";
+	 *
-  }
+	 */
+	public String getName() {
+		return m_Name;
+	}
 
-  /**
+	public String globalInfo() {
-   *
+		return "Configure statistics and output of the optimization run.";
-   */
+	}
-  public void setPlotoutput(int i) {
-    m_Plotoutput = i;
-  }
 
-  /**
+	/**
-   *
+	 *
-   */
+	 */
-  public int GetPlotoutput() {
+	public void setPlotoutput(int i) {
-    return m_Plotoutput;
+		m_Plotoutput = i;
-  }
+	}
 
-//  /**
+	/**
-//   *
+	 *
-//   */
+	 */
-//  public String plotFrequencyTipText() {
+	public int GetPlotoutput() {
-//    return "Frequency how often the fitness plot gets an update. plotoutput=1 -> there is a output every generation. plotoutput<0 -> there is no plot output";
+		return m_Plotoutput;
-//  }
+	}
-
-//  /**
-//   *
-//   */
-//  public String printMeanTipText() {
-//    return "Prints the mean of the fitness plot. Makes only sense when multiRuns > 1;";
-//  }
-
-  /**
-   *
-   */
-  public void setMultiRuns(int x) {
-    m_MultiRuns = x;
-  }
-
-  /**
-   *
-   */
-  public int getMultiRuns() {
-    return m_MultiRuns;
-  }
-
-  /**
-   *
-   */
-  public String multiRunsTipText() {
-    return "Number of independent optimization runs to evaluate.";
-  }
-
-  /**
-   *
-   */
-  public String GetInfoString() {
-    return m_InfoString;
-  }
-
-  /**
-   *
-   */
-  public void setInfoString(String s) {
-    m_InfoString = s;
-  }
-
-  /**
-   *
-   */
-  public String infoStringTipText() {
-    return "Infostring displayed on fitness graph by prssing the right mouse button.";
-  }
-
-  /**
-   *
-   */
-  public boolean GetuseStatPlot() {
-    return m_useStatPlot;
-  }
-
-  /**
-   *
-   */
-  public void setuseStatPlot(boolean x) {
-    m_useStatPlot = x;
-  }
-
-  /**
-   *
-   */
-  public String useStatPlotTipText() {
-    return "Plotting each fitness graph separate if multiruns > 1.";
-  }
-
-  /**
-   *
-   */
-  public SelectedTag getPlotData() {
-    return new SelectedTag(m_PlotFitness, TAGS_PLOT_FITNESS);
-  }
-
-  /**
-   *
-   */
-  public void setPlotData(SelectedTag newMethod) {
-    m_PlotFitness = newMethod.getSelectedTag().getID();
-  }
-
-  /**
-   *
-   */
-  public String plotDataTipText() {
-    return "The data to be plotted: best fitness, worst fitness or average/max distance in population.";
-  }
-
-//  /**
-//   *
-//   */
-//  public String plotObjectivesTipText() {
-//    return "The individual of which the objectives are plotted.";
-//  }
-
-
-  /**
-   *
-   */
-  public void SetResultFilePrefix(String x) {
-	  if (x==null) m_ResultFilePrefix = "";
-	  else m_ResultFilePrefix = x;
-  }
-
-  /**
-   *
-   */
-  public String getResultFilePrefix() {
-	  return m_ResultFilePrefix;
-  }
-  
-  public void SetShowTextOutput(boolean show) {
-	  // activate if not activated
-	  if (show && outputTo.getSelectedTagID() == 0) outputTo.setSelectedTag(2);
-	  // deactivate if activated
-	  else if (!show && outputTo.getSelectedTagID()>0) outputTo.setSelectedTag(0);
-  }
-  
-  public boolean isShowTextOutput() {
-	  return outputTo.getSelectedTagID()>0;
-  }
-//
-//  /**
-//   *
-//   */
-//  public String resultFileNameTipText() {
-//    return "File name for the result file. If empty or 'none', no output file will be created.";
-//  }
-
-  public String convergenceRateThresholdTipText() {
-	  return "Provided the optimal fitness is at zero, give the threshold below which it is considered as 'reached'";
-  }
-  
-  /**
-   *
-   * @param x
-   */
-  public void setConvergenceRateThreshold(double x) {
-    m_ConvergenceRateThreshold = x;
-  }
-
-  /**
-   *
-   */
-  public double getConvergenceRateThreshold() {
-    return m_ConvergenceRateThreshold;
-  }
 
 //	/**
-//	 * @return the showOutputData
+//	*
-//	 */
+//	*/
+//	public String plotFrequencyTipText() {
+//	return "Frequency how often the fitness plot gets an update. plotoutput=1 -> there is a output every generation. plotoutput<0 -> there is no plot output";
+//	}
+
+//	/**
+//	*
+//	*/
+//	public String printMeanTipText() {
+//	return "Prints the mean of the fitness plot. Makes only sense when multiRuns > 1;";
+//	}
+
+	/**
+	 *
+	 */
+	public void setMultiRuns(int x) {
+		m_MultiRuns = x;
+	}
+
+	/**
+	 *
+	 */
+	public int getMultiRuns() {
+		return m_MultiRuns;
+	}
+
+	/**
+	 *
+	 */
+	public String multiRunsTipText() {
+		return "Number of independent optimization runs to evaluate.";
+	}
+
+	/**
+	 *
+	 */
+	public String GetInfoString() {
+		return m_InfoString;
+	}
+
+	/**
+	 *
+	 */
+	public void setInfoString(String s) {
+		m_InfoString = s;
+	}
+
+	/**
+	 *
+	 */
+	public String infoStringTipText() {
+		return "Infostring displayed on fitness graph by prssing the right mouse button.";
+	}
+
+	/**
+	 *
+	 */
+	public boolean GetuseStatPlot() {
+		return m_useStatPlot;
+	}
+
+	/**
+	 *
+	 */
+	public void setuseStatPlot(boolean x) {
+		m_useStatPlot = x;
+	}
+
+	/**
+	 *
+	 */
+	public String useStatPlotTipText() {
+		return "Plotting each fitness graph separate if multiruns > 1.";
+	}
+
+	/**
+	 *
+	 */
+	public SelectedTag getPlotData() {
+		return new SelectedTag(m_PlotFitness, TAGS_PLOT_FITNESS);
+	}
+
+	/**
+	 *
+	 */
+	public void setPlotData(SelectedTag newMethod) {
+		m_PlotFitness = newMethod.getSelectedTag().getID();
+	}
+
+	/**
+	 *
+	 */
+	public String plotDataTipText() {
+		return "The data to be plotted: best fitness, worst fitness or average/max distance in population.";
+	}
+
+//	/**
+//	*
+//	*/
+//	public String plotObjectivesTipText() {
+//	return "The individual of which the objectives are plotted.";
+//	}
+
+
+	/**
+	 *
+	 */
+	public void SetResultFilePrefix(String x) {
+		if (x==null) m_ResultFilePrefix = "";
+		else m_ResultFilePrefix = x;
+	}
+
+	/**
+	 *
+	 */
+	public String getResultFilePrefix() {
+		return m_ResultFilePrefix;
+	}
+
+	public void SetShowTextOutput(boolean show) {
+		// activate if not activated
+		if (show && outputTo.getSelectedTagID() == 0) outputTo.setSelectedTag(2);
+		// deactivate if activated
+		else if (!show && outputTo.getSelectedTagID()>0) outputTo.setSelectedTag(0);
+	}
+
+	public boolean isShowTextOutput() {
+		return outputTo.getSelectedTagID()>0;
+	}
+
+//	/**
+//	*
+//	*/
+//	public String resultFileNameTipText() {
+//	return "File name for the result file. If empty or 'none', no output file will be created.";
+//	}
+
+	public String convergenceRateThresholdTipText() {
+		return "Provided the optimal fitness is at zero, give the threshold below which it is considered as 'reached'";
+	}
+
+	/**
+	 *
+	 * @param x
+	 */
+	public void setConvergenceRateThreshold(double x) {
+		m_ConvergenceRateThreshold = x;
+	}
+
+	/**
+	 *
+	 */
+	public double getConvergenceRateThreshold() {
+		return m_ConvergenceRateThreshold;
+	}
+
+//	/**
+//	* @return the showOutputData
+//	*/
 //	public boolean isShowTextOutput() {
-//		return showTextOutput;
+//	return showTextOutput;
 //	}
-//
+
 //	/**
-//	 * 
+//	* 
-//	 * @param showOutputData the showOutputData to set
+//	* @param showOutputData the showOutputData to set
-//	 */
+//	*/
 //	public void setShowTextOutput(boolean bShow) {
-//		this.showTextOutput = bShow;
+//	this.showTextOutput = bShow;
 //	}
-//	
+
 //	public String showTextOutputTipText() {
-//		return "Indicates whether further text output should be printed";
+//	return "Indicates whether further text output should be printed";
 //	}
 
 	public boolean isOutputAdditionalInfo() {
@@ -358,29 +362,36 @@ public class StatsParameter implements InterfaceStatisticsParameter, Serializabl
 	public void hideHideable() {
 		setOutputVerbosity(getOutputVerbosity());
 	}
-	  public void setOutputVerbosity(SelectedTag sTag) {
-		  outputVerbosity = sTag;
-		  GenericObjectEditor.setHideProperty(this.getClass(), "outputVerbosityK", sTag.getSelectedTagID() != VERBOSITY_KTH_IT);
-	  }
-	  public SelectedTag getOutputVerbosity() {
-		  return outputVerbosity;
-	  }
 	
-	  public String outputVerbosityTipText() {
+	public void setOutputVerbosity(SelectedTag sTag) {
-		  return "Set the data output level.";
+		outputVerbosity = sTag;
-	  }
+		GenericObjectEditor.setHideProperty(this.getClass(), "outputVerbosityK", sTag.getSelectedTagID() != VERBOSITY_KTH_IT);
+	}
 	
-	  public int getOutputVerbosityK() {
+	public void setOutputVerbosity(int i) {
-		  return verboK;
+		outputVerbosity.setSelectedTag(i);
-	  }
+		GenericObjectEditor.setHideProperty(this.getClass(), "outputVerbosityK", outputVerbosity.getSelectedTagID() != VERBOSITY_KTH_IT);
+	}
 	
-	  public void setOutputVerbosityK(int k) {
+	public SelectedTag getOutputVerbosity() {
-		  verboK = k;
+		return outputVerbosity;
-	  }
+	}
 
-	  public String outputVerbosityKTipText() {
+	public String outputVerbosityTipText() {
-		  return "Set the interval of data output for intermediate verbosity (in generations).";
+		return "Set the data output level.";
-	  }
+	}
+
+	public int getOutputVerbosityK() {
+		return verboK;
+	}
+
+	public void setOutputVerbosityK(int k) {
+		verboK = k;
+	}
+
+	public String outputVerbosityKTipText() {
+		return "Set the interval of data output for intermediate verbosity (in generations).";
+	}
 
 	public SelectedTag getOutputTo() {
 		return outputTo;
@@ -390,6 +401,10 @@ public class StatsParameter implements InterfaceStatisticsParameter, Serializabl
 		outputTo = tag;
 	}
 
+	public void setOutputTo(int i) {
+		outputTo.setSelectedTag(i);
+	}
+
 	public String outputToTipText() {
 		return "Set the output destination; to deactivate output, set verbosity to none.";
 	}

src/eva2/tools/Pair.java

@@ -1,12 +1,18 @@
 package eva2.tools;
 
+import java.io.Serializable;
+
 /**
  * Simple pair structure of two types, Scheme style, but typed.
  * 
  * @author mkron
  *
  */
-public class Pair<S, T> {
+public class Pair<S, T> implements Serializable {
+	/**
+	 * 
+	 */
+	private static final long serialVersionUID = -3620465393975181451L;
 	public S head;
 	public T tail;