Added @Description annotations to all optimization strategies.

refs #9

src/eva2/optimization/strategies/BinaryScatterSearch.java

@@ -15,6 +15,7 @@ import eva2.optimization.problems.B1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.tools.Pair;
 import eva2.tools.math.RNG;
+import eva2.util.annotation.Description;
 
 import java.util.ArrayList;
 import java.util.BitSet;
@@ -22,12 +23,12 @@ import java.util.BitSet;
 /**
  * A BinaryScatterSearch implementation taken mainly from [i].
  *
- * @author Alex
  *         <p/>
  *         F. Gortazar, A. Duarte, M. Laguna and R. Marti: Black Box Scatter Search for
  *         General Classes of Binary Optimization Problems Computers and Operations
  *         research, vol. 37, no. 11, pp. 1977-1986 (2010)
  */
+@Description("A basic implementation of a Binary ScatterSearch")
 public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
 
     private static boolean TRACE = false;
@@ -910,8 +911,4 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
     public String perCentSecondGenMethodTipText() {
         return "The number of individuals generated with the second Generation Method. The percentage, that is not covered with the first and the second method will be covered with a third method";
     }
-
-    public static String globalInfo() {
-        return "A basic implementation of a Binary ScatterSearch";
-    }
 }

src/eva2/optimization/strategies/CBNPSO.java

@@ -16,9 +16,11 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.optimization.problems.InterfaceProblemDouble;
 import eva2.tools.EVAERROR;
 import eva2.tools.ToolBox;
+import eva2.util.annotation.Description;
 
 import java.io.Serializable;
 
+@Description("A CBN-EA variant employing PSO and dynamic variation of the clustering parameter by default.")
 public class CBNPSO extends ClusterBasedNichingEA implements Serializable {
     private boolean forceUpperClustDist = true;
 
@@ -158,10 +160,6 @@ public class CBNPSO extends ClusterBasedNichingEA implements Serializable {
         }
     }
 
-    public static String globalInfo() {
-        return "A CBN-EA variant employing PSO and dynamic variation of the clustering parameter by default.";
-    }
-
     public void setForceUpperClustDist(boolean forceUpperClustDist) {
         this.forceUpperClustDist = forceUpperClustDist;
     }

src/eva2/optimization/strategies/CHCAdaptiveSearchAlgorithm.java

@@ -22,15 +22,10 @@ import java.util.BitSet;
  * 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:
- * <p/>
- * Copyright: Copyright (c) 2003 Company: University of Tuebingen, Computer
- * Architecture
  *
- * @author Felix Streichert
+ * ToDo: Check implementation for correctness.
- * @version: $Revision: 307 $ $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec
- * 2007) $ $Author: mkron $
  */
+@eva2.util.annotation.Description("This is an implementation of the CHC Adaptive Search Algorithm by Eselman.")
 public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.Serializable {
 
     private double initialDifferenceThreshold = 0.25;
@@ -310,19 +305,6 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         return this.identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * 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
      *

src/eva2/optimization/strategies/ClusterBasedNichingEA.java

@@ -26,6 +26,7 @@ import eva2.optimization.problems.*;
 import eva2.tools.EVAERROR;
 import eva2.tools.chart2d.*;
 import eva2.tools.math.Mathematics;
+import eva2.util.annotation.Description;
 
 import java.util.*;
 
@@ -41,17 +42,13 @@ import java.util.*;
  * For the CBN-PSO remember to use the IndividualDataMetric so that the
  * remembered positions are used for clustering (which are rel. stable - so that
  * species clustering actually makes sense).
- * <p/>
- * Copyright: Copyright (c) 2010 Company: University of Tuebingen, Computer
- * Architecture
- *
- * @author Felix Streichert, Marcel Kronfeld
  */
+@Description("This is a versatile species based niching EA method.")
 public class ClusterBasedNichingEA implements InterfacePopulationChangedEventListener, InterfaceAdditionalPopulationInformer, InterfaceOptimizer, java.io.Serializable {
 
     private static final long serialVersionUID = -3143069327594708609L;
     private Population population = new Population();
-    private transient Population poulationArchive = new Population();
+    private transient Population populationArchive = new Population();
     private ArrayList<Population> species = new ArrayList<Population>();
     private Population undifferentiatedPopulation = new Population();
     private transient Population doomedPopulation = new Population();
@@ -127,7 +124,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     public ClusterBasedNichingEA(ClusterBasedNichingEA a) {
         this.epsilonBound = a.epsilonBound;
         this.population = (Population) a.population.clone();
-        this.poulationArchive = (Population) a.poulationArchive.clone();
+        this.populationArchive = (Population) a.populationArchive.clone();
         this.doomedPopulation = (Population) a.doomedPopulation.clone();
         this.optimizationProblem = (InterfaceOptimizationProblem) a.optimizationProblem.clone();
         this.optimizer = (InterfaceOptimizer) a.optimizer.clone();
@@ -199,7 +196,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         this.optimizer.addPopulationChangedEventListener(this);
         this.undifferentiatedPopulation.setTargetSize(this.populationSize);
         this.species = new ArrayList<Population>();
-        this.poulationArchive = undifferentiatedPopulation.cloneWithoutInds();
+        this.populationArchive = undifferentiatedPopulation.cloneWithoutInds();
 //    	if (useDistraction) distraction = new Distraction(distrDefaultStrength, Distraction.METH_BEST);
         convergedCnt = 0;
         collisions = 0;
@@ -296,8 +293,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                 plotPopConnected(topologyPlot, pop);
             }
             if (!useDistraction) {
-                for (int i = 0; i < this.poulationArchive.size(); i++) {
+                for (int i = 0; i < this.populationArchive.size(); i++) {
-                    plotIndy(topologyPlot, 'x', (InterfaceDataTypeDouble) poulationArchive.get(i));
+                    plotIndy(topologyPlot, 'x', (InterfaceDataTypeDouble) populationArchive.get(i));
                 }
             } else {
 //            	for (int i = 0; i < this.distraction.getDistractorSetSize(); i++) {
@@ -606,7 +603,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 //                	}
                 int toReinit = 0;
                 if (true) { //if (m_UseArchive) {
-                    poulationArchive.add(best);
+                    populationArchive.add(best);
 //                    	System.out.println((""+ population.getFunctionCalls() + " " + (BeanInspector.toString(best.getDoublePosition())).replaceAll(";|\\[|\\]", "")));
                     species.remove(i);  // remove the converged Species
                     toReinit = curSpecies.size();
@@ -757,11 +754,11 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                 }
                 Population spec1, spec2;
                 // test if species are close to already archived solutions - deactivate them if so
-                assocSpec = caForSpeciesMerging.associateLoners(poulationArchive, species.toArray(new Population[species.size()]), population);
+                assocSpec = caForSpeciesMerging.associateLoners(populationArchive, species.toArray(new Population[species.size()]), population);
                 PriorityQueue<Integer> specToRemove = new PriorityQueue<Integer>(5, Collections.reverseOrder()); // backwards sorted or DIE!
-                for (int i = poulationArchive.size() - 1; i >= 0; i--) {
+                for (int i = populationArchive.size() - 1; i >= 0; i--) {
                     if (assocSpec[i] >= 0) {
-                        AbstractEAIndividual aIndy = poulationArchive.getEAIndividual(i);
+                        AbstractEAIndividual aIndy = populationArchive.getEAIndividual(i);
                         spec1 = (Population) this.species.get(assocSpec[i]);
                         // archived solution corresponds to an existing species
                         if (!specToRemove.contains(assocSpec[i])) {
@@ -773,7 +770,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                             }
                             if (spec1.getBestEAIndividual().isDominating(aIndy)) {
                                 // update the archived one with the better one? No rather not - it may happen that a large species is assoctiated which is quite large and spans over several optima - in that case an earlier found may get lost
-//                				poulationArchive.set(i, spec1.getBestEAIndividual());
+//                				populationArchive.set(i, spec1.getBestEAIndividual());
                             }
                             if (TRACE_EVTS) {
                                 System.out.println("!!! Reinit Spec " + assocSpec[i] + ", fit " + spec1.getBestEAIndividual());
@@ -872,7 +869,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
             }
             if (TRACE_STATE) {
                 printState("---- EoCBN", doomedPopulation);
-                System.out.println("Archive: " + poulationArchive.getStringRepresentation());
+                System.out.println("Archive: " + populationArchive.getStringRepresentation());
             }
         }
 //        if (TRACE) {
@@ -1111,18 +1108,6 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         return this.identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * These are for GUI
-     */
-    /**
-     * This method returns a global info string
-     *
-     * @return description
-     */
-    public static String globalInfo() {
-        return "This is a versatile species based niching EA method.";
-    }
 
     /**
      * This method will return a naming String
@@ -1145,10 +1130,10 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     @Override
     public void setPopulation(Population pop) {
         this.undifferentiatedPopulation = pop;
-        if (poulationArchive == null) {
+        if (populationArchive == null) {
-            poulationArchive = new Population();
+            populationArchive = new Population();
         }
-        poulationArchive.setPopMetric(pop.getPopMetric());
+        populationArchive.setPopMetric(pop.getPopMetric());
         population.setPopMetric(pop.getPopMetric());
         doomedPopulation.setPopMetric(pop.getPopMetric());
 
@@ -1166,7 +1151,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     }
 
     public Population getArchivedSolutions() {
-        return (Population) poulationArchive.clone();
+        return (Population) populationArchive.clone();
     }
 
     @Override
@@ -1469,12 +1454,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                 undifferentiatedPopulation.size(),
                 species.size(),
                 getAvgSpeciesMeasures()[0],
-                poulationArchive.size(),
+                populationArchive.size(),
-                poulationArchive.getCorrelations()[3],
+                populationArchive.getCorrelations()[3],
-                poulationArchive.getPopulationMeasures()[0],
+                populationArchive.getPopulationMeasures()[0],
                 collisions,
                 getClusterDiffDist()};
-//		return undifferentiatedPopulation.size() + " \t " + species.size() + " \t " + BeanInspector.toString(getAvgSpeciesMeasures()[0]) + " \t " + (poulationArchive.size());
+//		return undifferentiatedPopulation.size() + " \t " + species.size() + " \t " + BeanInspector.toString(getAvgSpeciesMeasures()[0]) + " \t " + (populationArchive.size());
     }
 
     /**

src/eva2/optimization/strategies/ClusteringHillClimbing.java

@@ -14,6 +14,7 @@ import eva2.optimization.problems.F1Problem;
 import eva2.optimization.problems.InterfaceAdditionalPopulationInformer;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.tools.Pair;
+import eva2.util.annotation.Description;
 
 import java.io.Serializable;
 
@@ -32,8 +33,9 @@ import java.io.Serializable;
  * number of optima that may be found and returned by getAllSolutions is higher
  * than the population size.
  *
- * @author mkron
  */
+@Description("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.")
 public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener,
         InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
 
@@ -331,11 +333,6 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
         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
      */

src/eva2/optimization/strategies/DynamicParticleSwarmOptimization.java

@@ -10,14 +10,12 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.tools.SelectedTag;
 import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;
+import eva2.util.annotation.Description;
 
 /**
  * This extends our particle swarm implementation to dynamic optimization problems.
- * <p/>
- * User: marcekro
- * Date: 2007
- * Time: 11:23:21
  */
+@Description("Particle Swarm Optimization tuned for tracking a dynamic target")
 public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization {
 
     private boolean envHasChanged = false;
@@ -507,19 +505,6 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
         return strB.toString();
     }
 
-
-/**********************************************************************************************************************
- * These are for GUI
- */
-    /**
-     * This method returns a global info string
-     *
-     * @return description
-     */
-    public static String globalInfo() {
-        return "Particle Swarm Optimization tuned for tracking a dynamic target";
-    }
-
     /**
      * This method will return a naming String
      *

src/eva2/optimization/strategies/EsDpiNiching.java

@@ -24,6 +24,7 @@ import eva2.optimization.problems.InterfaceAdditionalPopulationInformer;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;
+import eva2.util.annotation.Description;
 
 import java.io.Serializable;
 import java.util.Arrays;
@@ -84,9 +85,10 @@ import java.util.Formatter;
  * <p/>
  * TODO Add adaptive niche radius. Add parameter to every indy which is adapted
  * after all new peaks have been found.
- *
- * @author mkron
  */
+@Description("A niching ES with dynamic peak identification, after Shir and Bäck: Niching in Evolution Strategies, "
+        + "GECCO 2005. Basically, there are several variants of a (mu,lambda)-ES performed "
+        + "in parallel, which are reclustered in each iteration based on the dynamic peak set.")
 public class EsDpiNiching implements InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer, InterfacePopulationChangedEventListener {
 
     private static final boolean TRACE = false, TRACE_DEMES = false;
@@ -1019,12 +1021,6 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
         return identifier + "_" + getExpectedPeaks() + "_" + getNicheRadius();
     }
 
-    public static String globalInfo() {
-        return "A niching ES with dynamic peak identification, after Shir and Bäck: Niching in Evolution Strategies, "
-                + "GECCO 2005. Basically, there are several variants of a (mu,lambda)-ES performed "
-                + "in parallel, which are reclustered in each iteration based on the dynamic peak set.";
-    }
-
     @Override
     public void addPopulationChangedEventListener(
             InterfacePopulationChangedEventListener ea) {

src/eva2/optimization/strategies/EsDpiNichingCma.java

@@ -1,7 +1,10 @@
 package eva2.optimization.strategies;
 
 import eva2.optimization.operator.selection.SelectBestSingle;
+import eva2.util.annotation.Description;
 
+@Description("A variant of the DPI Niching ES to be usable with CMA-Mutation (Shir&Bäck, CEC'05). " +
+        "Remember to turn off crossover for lambda=1, and to set CMA as mutation in the individual template.")
 public class EsDpiNichingCma extends EsDpiNiching {
 
     /**
@@ -39,9 +42,4 @@ public class EsDpiNichingCma extends EsDpiNiching {
     public String getName() {
         return "CMA-" + super.getName();
     }
-
-    public static String globalInfo() {
-        return "A variant of the DPI Niching ES to be usable with CMA-Mutation (Shir&Bäck, CEC'05). " +
-                "Remember to turn off crossover for lambda=1, and to set CMA as mutation in the individual template.";
-    }
 }

src/eva2/optimization/strategies/EvolutionStrategyIPOP.java

@@ -13,6 +13,7 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.PopulationInterface;
 import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.InterfaceAdditionalPopulationInformer;
+import eva2.util.annotation.Description;
 
 import java.util.Arrays;
 import java.util.LinkedList;
@@ -32,9 +33,8 @@ import java.util.LinkedList;
  * Refer to Auger&Hansen 05 for more details.
  * <p/>
  * A.Auger & N.Hansen. A Restart CMA Evolution Strategy With Increasing Population Size. CEC 2005.
- *
- * @author mkron
  */
+@Description("An ES with increasing population size.")
 public class EvolutionStrategyIPOP extends EvolutionStrategies implements InterfacePopulationChangedEventListener, InterfaceAdditionalPopulationInformer {
     private static final long serialVersionUID = 4102736881931867818L;
     int dim = -1;
@@ -270,10 +270,6 @@ public class EvolutionStrategyIPOP extends EvolutionStrategies implements Interf
     public String getName() {
         return getIncPopSizeFact() + "-IPOP-ES";
     }
-
-    public static String globalInfo() {
-        return "An ES with increasing population size.";
-    }
 //
 //	protected void checkPopulationConstraints() {
 //		if (getLambda()!=initialLambda) setLambda(initialLambda);

src/eva2/optimization/strategies/EvolutionaryProgramming.java

@@ -9,20 +9,15 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.F1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 /**
  * 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).
- * <p/>
- * 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 $
  */
+@Description("This is a basic Evolutionary Programming scheme.")
 public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Serializable {
 
     private int m_PopulationSize = 0;
@@ -192,19 +187,6 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * These are for GUI
-     */
-    /**
-     * 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
      *

src/eva2/optimization/strategies/FloodAlgorithm.java

@@ -8,6 +8,7 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.B1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 /**
  * The flood algorithm, and alternative to the threshold algorithms. No really
@@ -16,10 +17,9 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
  * 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:
+ * one-dimensional fitness.
- * 01.10.2004 Time: 13:46:02 To change this template use File | Settings | File
- * Templates.
  */
+@Description("The flood algorithm uses an declining flood peak to accpect new solutions (*shudder* check inital flood peak and drain very carefully!).")
 public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable {
     // These variables are necessary for the simple testcase
 
@@ -250,19 +250,6 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * 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
      *

src/eva2/optimization/strategies/GradientDescentAlgorithm.java

@@ -12,6 +12,7 @@ import eva2.optimization.problems.InterfaceFirstOrderDerivableProblem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.tools.EVAERROR;
 import eva2.tools.ReflectPackage;
+import eva2.util.annotation.Description;
 
 /**
  * A gradient descent algorithm by hannes planatscher don't expect any
@@ -19,9 +20,8 @@ import eva2.tools.ReflectPackage;
  * <p/>
  * mkron added some!
  *
- * @author not attributable
- * @version 1.0
  */
+@Description("Gradient Descent can be applied to derivable functions (InterfaceFirstOrderDerivableProblem).")
 public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Serializable {
 
     private InterfaceOptimizationProblem m_Problem;
@@ -399,9 +399,6 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
         }
     }
 
-    public static String globalInfo() {
-        return "Gradient Descent can be applied to derivable functions (" + InterfaceFirstOrderDerivableProblem.class.getSimpleName() + ").";
-    }
 //////////////// for global adaption
 
     public boolean isAdaptStepSizeGlobally() {

src/eva2/optimization/strategies/IslandModelEA.java

@@ -12,6 +12,7 @@ import eva2.optimization.problems.F1Problem;
 import eva2.optimization.problems.F8Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.optimization.problems.TF1Problem;
+import eva2.util.annotation.Description;
 
 /**
  * The one and only island model for parallelization. Since parallelization
@@ -29,10 +30,8 @@ import eva2.optimization.problems.TF1Problem;
  * after which a communication step is performed according to the migration
  * model. Only after migration is a main cycle complete, the statistics updated
  * etc.
- * <p/>
- * Created by IntelliJ IDEA. User: streiche Date: 12.09.2004 Time: 14:48:20 To
- * change this template use File | Settings | File Templates.
  */
+@Description("This is an island model EA distributing the individuals across several (remote) CPUs for optimization.")
 public class IslandModelEA implements InterfacePopulationChangedEventListener, InterfaceOptimizer, java.io.Serializable {
 
     private Population m_Population = new Population();
@@ -475,18 +474,6 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         //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
-     *
-     * @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

src/eva2/optimization/strategies/LTGA.java

@@ -13,6 +13,7 @@ import eva2.optimization.problems.BKnapsackProblem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.tools.Pair;
 import eva2.tools.math.SpecialFunction;
+import eva2.util.annotation.Description;
 
 import java.util.BitSet;
 import java.util.HashSet;
@@ -21,10 +22,11 @@ import java.util.Stack;
 import java.util.logging.Level;
 import java.util.logging.Logger;
 
+@Description("Basic implementation of the Linkage Tree Genetic Algorithm based on the works by Dirk Thierens.")
 public class LTGA implements InterfaceOptimizer, java.io.Serializable, InterfacePopulationChangedEventListener {
 
     private static final Logger LOGGER = Logger.getLogger(LTGA.class.getName());
-    transient private InterfacePopulationChangedEventListener m_Listener = null;
+    transient private InterfacePopulationChangedEventListener populationChangedEventListener = null;
     private String m_Identifier = "LTGA";
     private int probDim = 8;
     private int fitCrit = -1;
@@ -39,7 +41,7 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
     }
 
     public LTGA(LTGA l) {
-        this.m_Listener = l.m_Listener;
+        this.populationChangedEventListener = l.populationChangedEventListener;
         this.m_Identifier = l.m_Identifier;
         this.probDim = l.probDim;
         this.popSize = l.popSize;
@@ -58,22 +60,18 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
         return "Linkage Tree Genetic Algorithm";
     }
 
-    public static String globalInfo() {
-        return "Basic implementation of the Linkage Tree Genetic Algorithm based on the works by Dirk Thierens.";
-    }
-
     @Override
     public void addPopulationChangedEventListener(
             InterfacePopulationChangedEventListener ea) {
-        this.m_Listener = ea;
+        this.populationChangedEventListener = ea;
 
     }
 
     @Override
     public boolean removePopulationChangedEventListener(
             InterfacePopulationChangedEventListener ea) {
-        if (m_Listener == ea) {
+        if (populationChangedEventListener == ea) {
-            m_Listener = null;
+            populationChangedEventListener = null;
             return true;
         } else {
             return false;
@@ -308,8 +306,8 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
      * Something has changed
      */
     protected void firePropertyChangedEvent(String name) {
-        if (this.m_Listener != null) {
+        if (this.populationChangedEventListener != null) {
-            this.m_Listener.registerPopulationStateChanged(this, name);
+            this.populationChangedEventListener.registerPopulationStateChanged(this, name);
         }
     }
 

src/eva2/optimization/strategies/MemeticAlgorithm.java

@@ -10,18 +10,20 @@ import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.F1Problem;
 import eva2.optimization.problems.InterfaceLocalSearchable;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 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>
+ * local search will not be activated at all.
- * Description: </p> <p> Copyright: Copyright (c) 2003 </p> <p> Company: </p>
  *
- * @author not attributable
  * @version 1.0
  */
+@Description("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 class MemeticAlgorithm implements InterfaceOptimizer,
         java.io.Serializable {
 
@@ -259,22 +261,6 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
     }
 
 
-    /*
-     * ========================================================================================
-     * 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
      *

src/eva2/optimization/strategies/MultiObjectiveCMAES.java

@@ -11,13 +11,15 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.AbstractOptimizationProblem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 import java.io.Serializable;
 import java.util.HashMap;
 
 /**
- * @author mkron
+ * ToDo: Document
  */
+@Description("A multi-objective CMA-ES variant after Igel, Hansen and Roth 2007 (EC 15(1),1-28).")
 public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
 
     /**
@@ -131,10 +133,6 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
         return "(1+" + m_lambda + ") MO-CMA-ES";
     }
 
-    public static String globalInfo() {
-        return "A multi-objective CMA-ES variant after Igel, Hansen and Roth 2007 (EC 15(1),1-28).";
-    }
-
     /*
      * (non-Javadoc)
      * 

src/eva2/optimization/strategies/MultiObjectiveEA.java

@@ -14,6 +14,7 @@ import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.AbstractOptimizationProblem;
 import eva2.optimization.problems.FM0Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 /**
  * A generic framework for multi-objecitve optimization, you need to specify an
@@ -24,10 +25,9 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
  * 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:
+ * or hill-climbing might even fail on that.
- * streiche Date: 05.06.2003 Time: 11:03:50 To change this template use Options
- * | File Templates.
  */
+@Description("This is a general Multi-objective Evolutionary Optimization Framework.")
 public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
 
     private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm();
@@ -230,19 +230,6 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * 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
      *

src/eva2/optimization/strategies/NelderMeadSimplex.java

@@ -11,6 +11,7 @@ import eva2.optimization.problems.AbstractOptimizationProblem;
 import eva2.optimization.problems.AbstractProblemDouble;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.Mathematics;
+import eva2.util.annotation.Description;
 
 import java.io.Serializable;
 import java.util.Vector;
@@ -19,9 +20,8 @@ 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.
- *
- * @author mkron
  */
+@Description("The Nelder-Mead simplex search algorithm for local search. Reflection on bounds may be used for constraint handling.")
 public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, InterfacePopulationChangedEventListener {
 
     private int populationSize = 100;
@@ -231,10 +231,6 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
         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.";
-    }
-
     @Override
     public Population getPopulation() {
         return m_Population;

src/eva2/optimization/strategies/ParticleFilterOptimization.java

@@ -17,20 +17,15 @@ import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.AbstractOptimizationProblem;
 import eva2.optimization.problems.F1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 /**
  * 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.
- * <p/>
- * 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 $
  */
+@Description("This is a Particle Filter Algorithm.")
 public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.Serializable {
 
     /**
@@ -41,7 +36,6 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
     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;
@@ -341,19 +335,6 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * 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
      *

src/eva2/optimization/strategies/ParticleSwarmOptimization.java

@@ -23,6 +23,7 @@ import eva2.tools.chart2d.DPointSet;
 import eva2.tools.math.Jama.Matrix;
 import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;
+import eva2.util.annotation.Description;
 
 import java.util.ArrayList;
 import java.util.Arrays;
@@ -38,6 +39,7 @@ import java.util.Vector;
  * Possible topologies are: "Linear", "Grid", "Star", "Multi-Swarm", "Tree",
  * "HPSO", "Random" in that order starting by 0.
  */
+@Description("Particle Swarm Optimization by Kennedy and Eberhart.")
 public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Serializable, InterfaceAdditionalPopulationInformer {
 
     /**
@@ -1725,19 +1727,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * These are for GUI
-     */
-    /**
-     * This method returns a global info string
-     *
-     * @return description
-     */
-    public static String globalInfo() {
-        return "Particle Swarm Optimization by Kennedy and Eberhart.";
-    }
-
     /**
      * This method will return a naming String
      *

src/eva2/optimization/strategies/ParticleSwarmOptimizationGCPSO.java

@@ -2,6 +2,7 @@ package eva2.optimization.strategies;
 
 import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.tools.math.RNG;
+import eva2.util.annotation.Description;
 
 /**
  * This extends the particle swarm optimization implementation
@@ -9,6 +10,7 @@ import eva2.tools.math.RNG;
  * Franz van den Bergh in "An Analysis of Particle Swarm Optimizers".
  * In this modification the velocity of the global best particle is updated differently.
  */
+@Description("Guaranteed Convergence Particle Swarm Optimiser (GCPSO) as proposed by F. van den Bergh.")
 public class ParticleSwarmOptimizationGCPSO extends ParticleSwarmOptimization {
     // choosable parameters:
     protected boolean gcpso;
@@ -71,16 +73,6 @@ public class ParticleSwarmOptimizationGCPSO extends ParticleSwarmOptimization {
         this.SetRhoDecreaseFactor(a.getRhoDecreaseFactor());
     }
 
-    /**
-     * This method returns a global info string
-     *
-     * @return description
-     */
-    public static String globalInfo() {
-        return "Guaranteed Convergence Particle Swarm Optimiser (GCPSO) " +
-                "as proposed by F. van den Bergh.";
-    }
-
 /**********************************************************************************************************************
  * overwritten
  */

src/eva2/optimization/strategies/PopulationBasedIncrementalLearning.java

@@ -12,6 +12,7 @@ import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.AbstractOptimizationProblem;
 import eva2.optimization.problems.B1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 /**
  * Population based incremental learning in the PSM by Monmarche version with
@@ -22,14 +23,8 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
  * 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.
- * <p/>
- * 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 $
  */
+@Description("The Population based incremental learning is based on a statistical distribution of bit positions. Please note: This optimizer requires a binary genotype!")
 public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, java.io.Serializable {
 
     // These variables are necessary for the simple testcase
@@ -221,19 +216,6 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * 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
      *

src/eva2/optimization/strategies/SimulatedAnnealing.java

@@ -9,15 +9,15 @@ import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.B1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
 import eva2.tools.math.RNG;
+import eva2.util.annotation.Description;
 
 /**
  * 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:
+ * size gives the number of multi-starts.
- * streiche Date: 13.05.2004 Time: 10:30:26 To change this template use File |
- * Settings | File Templates.
  */
+@Description("The simulated annealing uses an additional cooling rate instead of a simple dominate criteria to accept worse solutions by chance.")
 public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializable {
     // These variables are necessary for the simple testcase
 
@@ -248,18 +248,6 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * 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

src/eva2/optimization/strategies/SteadyStateGA.java

@@ -12,14 +12,14 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.B1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 /**
  * 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:
+ * are performed each time optimize() is called.
- * streiche Date: 19.07.2005 Time: 14:30:20 To change this template use File |
- * Settings | File Templates.
  */
+@Description("This is a Steady-State Genetic Algorithm.")
 public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
 
     private Population population = new Population();
@@ -191,19 +191,6 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
         return this.identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * These are for GUI
-     */
-    /**
-     * 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
      *

src/eva2/optimization/strategies/ThresholdAlgorithm.java

@@ -8,12 +8,13 @@ import eva2.optimization.population.Population;
 import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.B1Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 /**
  * Threshold accepting algorithm simliar strategy as the flood algorithm,
- * similar problems. Created by IntelliJ IDEA. User: streiche Date: 01.10.2004
+ * similar problems.
- * Time: 13:35:49 To change this template use File | Settings | File Templates.
  */
+@Description("The threshold algorithm uses an declining threshold to accpect new solutions.")
 public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializable {
     // These variables are necessary for the simple testcase
 
@@ -238,19 +239,6 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * 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
      *

src/eva2/optimization/strategies/Tribes.java

@@ -15,6 +15,7 @@ import eva2.optimization.strategies.tribes.TribesExplorer;
 import eva2.optimization.strategies.tribes.TribesParam;
 import eva2.optimization.strategies.tribes.TribesPosition;
 import eva2.optimization.strategies.tribes.TribesSwarm;
+import eva2.util.annotation.Description;
 
 import java.util.Iterator;
 import java.util.List;
@@ -130,6 +131,7 @@ import java.util.List;
  2005-11-21. Check if it is possible to easily find just the _value_ of the
  global minimum (not the position). "Chinese shadow" method?
  */
+@Description("TRIBES: a parameter free PSO implementation by Maurice Clerc.")
 public class Tribes implements InterfaceOptimizer, java.io.Serializable {
 
     public static final boolean TRACE = false;
@@ -667,12 +669,10 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
 
     @Override
     public String getStringRepresentation() {
-        return globalInfo();
+        // ToDo: Implement properly (was globalInfo)
+        return "Not implemented";
     }
 
-    public static String globalInfo() {
-        return "TRIBES: a parameter free PSO implementation by Maurice Clerc.";
-    }
 
     public void incEvalCnt() {
         population.incrFunctionCalls();

src/eva2/optimization/strategies/WingedMultiObjectiveEA.java

@@ -10,6 +10,7 @@ import eva2.optimization.population.SolutionSet;
 import eva2.optimization.problems.AbstractMultiObjectiveOptimizationProblem;
 import eva2.optimization.problems.FM0Problem;
 import eva2.optimization.problems.InterfaceOptimizationProblem;
+import eva2.util.annotation.Description;
 
 /**
  * The winged MOEA was a nice idea, which didn't really work out. Here a
@@ -17,9 +18,8 @@ import eva2.optimization.problems.InterfaceOptimizationProblem;
  * 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.
  */
+@Description("This is Evolutionary Multi-Criteria Optimization Algorithm hybridized with Local Searchers to span the Pareto-Front.")
 public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Serializable {
 
     private InterfaceOptimizer m_MOOptimizer = new MultiObjectiveEA();
@@ -28,7 +28,6 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
     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;
@@ -285,19 +284,6 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
         return this.m_Identifier;
     }
 
-    /**
-     * ********************************************************************************************************************
-     * 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
      *