Merging MK revs 383:386 (pimpifications)

src/eva2/OptimizerFactory.java

@@ -1174,8 +1174,8 @@ public class OptimizerFactory {
 		es.setPlusStrategy(false);
 		cbn.setOptimizer(es);
 		ClusteringDensityBased clustering = new ClusteringDensityBased(0.1);
-		cbn.setConvergenceCA((ClusteringDensityBased) clustering.clone());
+		cbn.setMergingCA((ClusteringDensityBased) clustering.clone());
-		cbn.setDifferentationCA(clustering);
+		cbn.setDifferentiationCA(clustering);
 		cbn.setShowCycle(0); // don't do graphical output
 
 		return makeParams(cbn, 100, problem, randSeed, makeDefaultTerminator());
@@ -1186,8 +1186,8 @@ public class OptimizerFactory {
 		GeneticAlgorithm ga = new GeneticAlgorithm();
 		cbn.setOptimizer(ga);
 		ClusteringDensityBased clustering = new ClusteringDensityBased(0.1);
-		cbn.setConvergenceCA((ClusteringDensityBased) clustering.clone());
+		cbn.setMergingCA((ClusteringDensityBased) clustering.clone());
-		cbn.setDifferentationCA(clustering);
+		cbn.setDifferentiationCA(clustering);
 		cbn.setShowCycle(0); // don't do graphical output
 
 		return makeParams(cbn, 100, problem, randSeed, makeDefaultTerminator());

src/eva2/gui/GOEPanel.java

@@ -73,7 +73,7 @@ public class GOEPanel extends JPanel implements ItemListener {
 		m_Support = support;
 		m_goe  = goe;
 		
-		//System.out.println("GOEPanel.Constructor !!");
+		//System.out.println("GOEPanel.Constructor ! Backup is " + m_Backup + " " +  BeanInspector.toString(m_goe.getValue()));
 		if (!(Proxy.isProxyClass(m_Object.getClass()))) m_Backup = copyObject(m_Object);
 		m_ObjectNames = new DefaultComboBoxModel(new String [0]);
 		m_ObjectChooser = new JComboBox(m_ObjectNames);

src/eva2/server/ModuleServer.java

@@ -29,7 +29,7 @@ import eva2.tools.jproxy.RMIProxyLocal;
  * CLASS DECLARATION
  *==========================================================================*/
 /**
- *
+ * Collect available ModuleAdapter implementations and load them on request.
  */
 public class ModuleServer {
 	public static boolean TRACE = false;
@@ -117,7 +117,11 @@ public class ModuleServer {
 	}
 
 	/**
+	 * Load the module indicated by the selectedModuleName from all available
+	 * module classes; if necessary through a remote proxy. Try to load a given
+	 * parameter file in case its a GOModuleAdapter.
 	 * 
+	 * @return the loaded module adapter instance
 	 */
 	public ModuleAdapter createModuleAdapter(String selectedModuleName,
 			MainAdapterClient Client, boolean runWithoutRMI,

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

@@ -53,8 +53,8 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     public boolean							m_isPenalized			= false;	// may be set true for penalty based constraints
 
     protected double[]                      m_SelectionProbability  = new double[1];;
-    public double                           m_CrossoverProbability  = 1.0;
+    protected double                           m_CrossoverProbability  = 1.0;
-    public double                           m_MutationProbability   = 0.2;
+    protected 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];

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

@@ -23,7 +23,7 @@ import java.util.Comparator;
 public class AbstractEAIndividualComparator implements Comparator<Object>, Serializable {
 
 	// flag whether a data field should be used.
-	String indyDataKey = null;
+	String indyDataKey = "";
 	int fitCriterion = -1;
 	
 	/**
@@ -34,7 +34,7 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 	 *
 	 */
 	public AbstractEAIndividualComparator() {
-		this(null, -1);
+		this("", -1);
 	}
 	
 	/**
@@ -57,7 +57,7 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 	 * @param fitnessCriterion
 	 */
 	public AbstractEAIndividualComparator(int fitnessCriterion) {
-		this(null, fitnessCriterion);
+		this("", fitnessCriterion);
 	}
 	
 	/**
@@ -92,7 +92,7 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 	public int compare(Object o1, Object o2) {
 		boolean o1domO2, o2domO1;
 		
-		if (indyDataKey != null) {
+		if (indyDataKey != null && (indyDataKey.length()>0)) {
 			double[] fit1 = (double[])((AbstractEAIndividual)o1).getData(indyDataKey);
 			double[] fit2 = (double[])((AbstractEAIndividual)o2).getData(indyDataKey);
 			if (fitCriterion < 0) {
@@ -114,4 +114,31 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 		if (o1domO2 ^ o2domO1) return (o1domO2 ? -1 : 1);
 		else return 0; // these are not comparable
 	}
+
+	public String getIndyDataKey() {
+		return indyDataKey;
+	}
+	public void setIndyDataKey(String indyDataKey) {
+		this.indyDataKey = indyDataKey;
+	}
+	public String indyDataKeyTipText() {
+		return "A String can be given which retrievies individual properties based on which the comparison is performed.";
+	}
+
+	public int getFitCriterion() {
+		return fitCriterion;
+	}
+	public void setFitCriterion(int fitCriterion) {
+		this.fitCriterion = fitCriterion;
+	}
+	public String fitCriterionTipText() {
+		return "If -1, dominance is used, otherwise the indexed fitness criterion (for multiobjective problems)"; 
+	}
+	
+	public String globalInfo() {
+		return "A comparator class for general EA individuals. Compares individuals based on their fitness in context of minimization.";
+	}
+	public String getName() {
+		return "IndividualComparator";
+	}
 }

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

@@ -4,6 +4,7 @@ package eva2.server.go.individuals;
 import java.util.BitSet;
 
 import eva2.server.go.operators.crossover.CrossoverGANPoint;
+import eva2.server.go.operators.crossover.InterfaceCrossover;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateGAStandard;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
@@ -40,7 +41,7 @@ public class GAIndividualBinaryData extends AbstractEAIndividual implements Inte
 
         // cloning the members of AbstractEAIndividual
         this.m_Age                      = individual.m_Age;
-        this.m_CrossoverOperator        = individual.m_CrossoverOperator;
+        this.m_CrossoverOperator        = (InterfaceCrossover)individual.m_CrossoverOperator.clone();
         this.m_CrossoverProbability     = individual.m_CrossoverProbability;
         this.m_MutationOperator         = (InterfaceMutation)individual.m_MutationOperator.clone();
         this.m_MutationProbability      = individual.m_MutationProbability;

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

@@ -6,6 +6,7 @@ import java.util.BitSet;
 import eva2.server.go.individuals.codings.ga.GAStandardCodingDouble;
 import eva2.server.go.individuals.codings.ga.InterfaceGADoubleCoding;
 import eva2.server.go.operators.crossover.CrossoverGANPoint;
+import eva2.server.go.operators.crossover.InterfaceCrossover;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateGAStandard;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
@@ -55,7 +56,7 @@ public class GAIndividualDoubleData extends AbstractEAIndividual implements Inte
 
         // cloning the members of AbstractEAIndividual
         this.m_Age                      = individual.m_Age;
-        this.m_CrossoverOperator        = individual.m_CrossoverOperator;
+        this.m_CrossoverOperator        = (InterfaceCrossover)individual.m_CrossoverOperator.clone();
         this.m_CrossoverProbability     = individual.m_CrossoverProbability;
         this.m_MutationOperator         = (InterfaceMutation)individual.m_MutationOperator.clone();
         this.m_MutationProbability      = individual.m_MutationProbability;

src/eva2/server/go/operators/cluster/ClusteringDensityBased.java

@@ -151,7 +151,7 @@ public class ClusteringDensityBased implements InterfaceClustering, java.io.Seri
      * @param species2  The second species.
      * @return True if species converge, else False.
      */
-    public boolean convergingSpecies(Population species1, Population species2) {
+    public boolean mergingSpecies(Population species1, Population species2) {
         if (m_TestConvergingSpeciesOnBestOnly) {
             if (this.m_Metric.distance(species1.getBestEAIndividual(), species2.getBestEAIndividual()) < this.m_ClusterDistance) return true;
             else return false;

src/eva2/server/go/operators/cluster/ClusteringHierarchical.java

@@ -167,7 +167,7 @@ public class ClusteringHierarchical implements InterfaceClustering, Serializable
      * @param species2  The second species.
      * @return True if species converge, else False.
      */
-    public boolean convergingSpecies(Population species1, Population species2) {
+    public boolean mergingSpecies(Population species1, Population species2) {
         if (testConvergingSpeciesOnBestOnly) {
             if (this.metric.distance(species1.getBestEAIndividual(), species2.getBestEAIndividual()) < this.currentDistThreshold()) return true;
             else return false;

src/eva2/server/go/operators/cluster/ClusteringKMeans.java

@@ -24,7 +24,7 @@ public class ClusteringKMeans implements InterfaceClustering, java.io.Serializab
 
     public int                         m_K                 = 5;
     public double[][]                  m_C                 = null;
-    public boolean                     m_UseSearchSpace    = false;
+    public boolean                     m_UseSearchSpace    = true;
     public boolean                     m_ReuseC            = false;
     public boolean                     m_Debug             = false;
 
@@ -278,7 +278,7 @@ public class ClusteringKMeans implements InterfaceClustering, java.io.Serializab
      * @param species2  The second species.
      * @return True if species converge, else False.
      */
-    public boolean convergingSpecies(Population species1, Population species2) {
+    public boolean mergingSpecies(Population species1, Population species2) {
         // @todo i could use the BIC metric from X-means to calculate this
         return false;
     }

src/eva2/server/go/operators/cluster/ClusteringXMeans.java

@@ -221,7 +221,7 @@ public class ClusteringXMeans implements InterfaceClustering, java.io.Serializab
      * @param species2  The second species.
      * @return True if species converge, else False.
      */
-    public boolean convergingSpecies(Population species1, Population species2) {
+    public boolean mergingSpecies(Population species1, Population species2) {
         // @todo i could use the BIC metric from X-means to calculate this
         return false;
     }

src/eva2/server/go/operators/cluster/InterfaceClustering.java

@@ -35,12 +35,12 @@ public interface InterfaceClustering {
      */
     public Population[] cluster(Population pop);
 
-    /** This method allows you to decide if two species converge.
+    /** This method allows you to decide if two species are to be merged regarding this clustering algorithm.
      * @param species1  The first species.
      * @param species2  The second species.
      * @return True if species converge, else False.
      */
-    public boolean convergingSpecies(Population species1, Population species2);
+    public boolean mergingSpecies(Population species1, Population species2);
 
     /** This method decides if an unclustered individual belongs to an already established species.
      * @param indy          A unclustered individual.

src/eva2/server/go/operators/distancemetric/EuclideanMetric.java

@@ -35,6 +35,44 @@ public class EuclideanMetric implements InterfaceDistanceMetric {
 		return Math.sqrt(result);
 	}
 	
+    /**
+     * The euclidean distance normed by the given ranges lying between 0 and sqrt(n)
+     * for n dimensions.
+     * 
+     * @param pos1
+     * @param range1
+     * @param pos2
+     * @param range2
+     * @return
+     */
+    public static double normedEuclideanDistance(double[] pos1, double[][] range1, double[] pos2, double[][] range2) {
+    	double      tmpResult = 0, tmp=0;
+
+    	for (int i = 0; (i < pos1.length) && (i < pos2.length); i++) {
+    		tmp=((pos1[i] - range1[i][0])/(range1[i][1] - range1[i][0])) - ((pos2[i] - range2[i][0])/(range2[i][1] - range2[i][0]));
+    		tmpResult += (tmp*tmp);
+    	}
+    	return Math.sqrt(tmpResult);
+    }
+    
+    public static double squaredEuclideanDistance(double[] v1, double[] v2) {
+        double      tmp, result = 0;
+        for (int i = 0; (i < v1.length) && (i < v2.length); i++) {
+        	tmp=v1[i] - v2[i];
+        	result += (tmp*tmp);
+        }
+        return result;
+    }
+    
+    public static double euclideanDistance(double[] v1, double[] v2) {
+        double      result = 0, tmp=0;
+        for (int i = 0; (i < v1.length) && (i < v2.length); i++) {
+        	tmp = v1[i] - v2[i];
+        	result += (tmp*tmp);
+        }
+        return Math.sqrt(result);
+    }
+	
 	/** This method returns a global info string
 	 * @return description
 	 */

src/eva2/server/go/operators/distancemetric/PhenotypeMetric.java

@@ -158,34 +158,6 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
     	return pMetric.distance(indy1, indy2);
     }
 
-    public static double euclidianDistance(double[] v1, double[] v2) {
-        double      result = 0, tmp=0;
-        for (int i = 0; (i < v1.length) && (i < v2.length); i++) {
-        	tmp = v1[i] - v2[i];
-        	result += (tmp*tmp);
-        }
-        return Math.sqrt(result);
-    }
-    
-    public static double squaredEuclidianDistance(double[] v1, double[] v2) {
-        double      tmp, result = 0;
-        for (int i = 0; (i < v1.length) && (i < v2.length); i++) {
-        	tmp=v1[i] - v2[i];
-        	result += (tmp*tmp);
-        }
-        return result;
-    }
-    
-    public static double normedDistance(double[] pos1, double[][] range1, double[] pos2, double[][] range2) {
-    	double      tmpResult = 0, tmp=0;
-
-    	for (int i = 0; (i < pos1.length) && (i < pos2.length); i++) {
-    		tmp=((pos1[i] - range1[i][0])/(range1[i][1] - range1[i][0])) - ((pos2[i] - range2[i][0])/(range2[i][1] - range2[i][0]));
-    		tmpResult += (tmp*tmp);
-    	}
-    	return Math.sqrt(tmpResult);
-    }
-
     public static double norm(AbstractEAIndividual indy) {
         double      result = 0;
         if (indy instanceof InterfaceDataTypeBinary) {

src/eva2/server/go/operators/mutation/MutateESLocal.java

@@ -202,7 +202,7 @@ public class MutateESLocal implements InterfaceMutation, java.io.Serializable {
         return this.m_MutationStepSize;
     }
     public String mutationStepSizeTipText() {
-        return "Choose the initial mutation step size.";
+        return "Choose the initial mutation step size sigma.";
     }
 
     /** Set the lower limit for the mutation step size with this method.

src/eva2/server/go/operators/mutation/MutateESSuccessRule.java

@@ -121,6 +121,11 @@ public class MutateESSuccessRule extends MutateESFixedStepSize implements Interf
         return "Choose the factor > 1 by which the mutation step size is to be increased/decreased.";
     }
     
+    @Override
+    public String sigmaTipText() {
+    	return "The initial step size.";
+    }
+
 	public void adaptAfterSelection(Population oldGen, Population selected) {
 		// nothing to do here		
 	}

src/eva2/server/go/operators/postprocess/PostProcess.java

@@ -20,6 +20,7 @@ import eva2.server.go.individuals.InterfaceESIndividual;
 import eva2.server.go.operators.cluster.ClusteringDensityBased;
 import eva2.server.go.operators.cluster.InterfaceClustering;
 import eva2.server.go.operators.crossover.CrossoverESDefault;
+import eva2.server.go.operators.distancemetric.EuclideanMetric;
 import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
 import eva2.server.go.operators.mutation.InterfaceMutation;
@@ -845,7 +846,7 @@ public class PostProcess {
 		
 		clust.SetFunctionCalls(evalsBefore + evalsDone);
 		
-		double improvement = PhenotypeMetric.euclidianDistance(meanFit, clust.getMeanFitness());
+		double improvement = EuclideanMetric.euclideanDistance(meanFit, clust.getMeanFitness());
 		if (TRACE) System.out.println("improvement by " + improvement + " funcalls done: " + evalsDone);
 		return new Pair<Population, Double>(clust, improvement);
 	}
@@ -1007,7 +1008,7 @@ public class PostProcess {
 		boolean found=false;
 		for (int k=0; k<candidates.size(); k++) {
 			if (k!=i) {
-				double dist = PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePositionShallow(indy), AbstractEAIndividual.getDoublePositionShallow(candidates.getEAIndividual(k)));
+				double dist = EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(indy), AbstractEAIndividual.getDoublePositionShallow(candidates.getEAIndividual(k)));
 				if (dist == 0.) {
 //					System.err.println("error, equal candidates in findNMSPerturb!");
 				} else if (dist < minDistNeighbour) {
@@ -1093,7 +1094,7 @@ public class PostProcess {
 		else {
 			double[] indyPos = AbstractEAIndividual.getDoublePositionShallow(pop.getEAIndividual(index));
 			for (int i=0; i<pop.size(); i++) {
-				if (i!=index) distSum += PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePositionShallow(pop.getEAIndividual(i)), indyPos); 
+				if (i!=index) distSum += EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(pop.getEAIndividual(i)), indyPos); 
 			}
 			return distSum/((double)cnt);
 		}

src/eva2/server/go/operators/terminators/FitnessConvergenceTerminator.java

@@ -16,9 +16,9 @@ package eva2.server.go.operators.terminators;
 import java.io.Serializable;
 
 import eva2.gui.BeanInspector;
-import eva2.server.go.IndividualInterface;
 import eva2.server.go.InterfaceTerminator;
 import eva2.server.go.PopulationInterface;
+import eva2.server.go.operators.distancemetric.EuclideanMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
 import eva2.server.go.populations.InterfaceSolutionSet;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
@@ -139,7 +139,7 @@ Serializable {
 	 */
 	protected boolean isStillConverged(PopulationInterface pop) {
 		double[] curFit = pop.getBestFitness();
-		double dist = PhenotypeMetric.euclidianDistance(oldFit, curFit);
+		double dist = EuclideanMetric.euclideanDistance(oldFit, curFit);
 		boolean ret;
 		if (convergenceCondition.isSelectedString("Relative")) {
 			ret = (dist < (oldNorm * convThresh));

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

@@ -15,6 +15,7 @@ import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.AbstractEAIndividualComparator;
 import eva2.server.go.individuals.GAIndividualBinaryData;
 import eva2.server.go.individuals.InterfaceDataTypeDouble;
+import eva2.server.go.operators.distancemetric.EuclideanMetric;
 import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
 import eva2.server.go.operators.distancemetric.PhenotypeMetric;
 import eva2.server.go.operators.selection.probability.AbstractSelProb;
@@ -26,7 +27,14 @@ import eva2.tools.math.Jama.Matrix;
 import eva2.tools.tool.StatisticUtils;
 
 
-/** This is a basic implementation for a EA Population.
+/** 
+ * A basic implementation of an EA population. Manage a set of potential solutions
+ * in form of AbstractEAIndividuals. They can be sorted using an AbstractEAIndividualComparator.
+ * Optionally, a history list is kept storing a clone of the best individual of any generation.
+ * The Population also provides for appropriate counting of function calls performed.
+ * For initialization, the default individual initialization method may be used, as well as a
+ * random latin hypercube implementation for InterfaceDataTypeDouble individuals. 
+ * 
  * Copyright:       Copyright (c) 2003
  * Company:         University of Tuebingen, Computer Architecture
  * @author          Felix Streichert, Marcel Kronfeld
@@ -342,7 +350,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * Stagnation measured etc. pp.
      */
     public void incrGeneration() {
-        if (useHistory && (this.size() >= 1)) this.m_History.add(this.getBestEAIndividual());
+        if (useHistory && (this.size() >= 1)) this.m_History.add((AbstractEAIndividual)this.getBestEAIndividual().clone());
         for (int i=0; i<size(); i++) ((AbstractEAIndividual)get(i)).incrAge(); 
         this.m_Generation++;
         firePropertyChangedEvent(nextGenerationPerformed);
@@ -947,9 +955,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         }
     }
 
-    /** This method will remove double instances from the population.
+    /** This method will remove instances with equal fitness from the population.
-     * This method relies on the implementation of the equals method
-     * in the individuals.
      */
     public void removeDoubleInstancesUsingFitness() {
         for (int i = 0; i < this.size(); i++) {
@@ -1255,7 +1261,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	
         for (int i = 0; i < this.size(); i++) {
         	for (int j = i+1; j < this.size(); j++) {
-        		if (metric == null) d = PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePositionShallow(getEAIndividual(i)), 
+        		if (metric == null) d = EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(getEAIndividual(i)), 
                 		AbstractEAIndividual.getDoublePositionShallow(getEAIndividual(j)));
         		else d = metric.distance((AbstractEAIndividual)this.get(i), (AbstractEAIndividual)this.get(j));
                 meanDist += d;
@@ -1329,7 +1335,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		 for (int i = 0; i < pop.size(); i++) {
 			 AbstractEAIndividual indy = pop.getEAIndividual(i);
 			 double[] indyPos = AbstractEAIndividual.getDoublePositionShallow(indy);
-			 double curDist = PhenotypeMetric.euclidianDistance(pos, indyPos);
+			 double curDist = EuclideanMetric.euclideanDistance(pos, indyPos);
 			 if ((dist<0) 	|| (!closestOrFarthest && (dist < curDist)) 
 					 		|| (closestOrFarthest && (dist > curDist))) {
 				 dist = curDist;
@@ -1411,7 +1417,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		for (int i = 0; i < size(); ++i){ 
 			AbstractEAIndividual currentindy = getEAIndividual(i);
 			if (!indy.equals(currentindy)){ // dont compare particle to itself or a copy of itself
-				double dist = PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePositionShallow(indy),
+				double dist = EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(indy),
 						AbstractEAIndividual.getDoublePositionShallow(currentindy));
 				if (dist  < mindist){ 
 					mindist = dist;
@@ -1448,7 +1454,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 			if (normalizedPhenoMetric){
 				d = metric.distance(indy, neighbor);
 			} else { 
-				d = PhenotypeMetric.euclidianDistance(AbstractEAIndividual.getDoublePositionShallow(indy),
+				d = EuclideanMetric.euclideanDistance(AbstractEAIndividual.getDoublePositionShallow(indy),
 						AbstractEAIndividual.getDoublePositionShallow(neighbor));
 			}
 			if (calcVariance) distances.add(d);

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

@@ -17,7 +17,7 @@ import eva2.tools.EVAERROR;
 public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDouble implements Interface2DBorderProblem, InterfaceMultimodalProblemKnown {
 	protected static InterfaceDistanceMetric   m_Metric = new PhenotypeMetric();
 	private double                    m_GlobalOpt = 0;
-	protected Population                  m_Optima;
+	protected Population                  m_ListOfOptima;
 	protected double                    m_Epsilon = 0.05;
 //	protected double[][]                m_Range;
 //	protected double[]                  m_Extrema;
@@ -36,8 +36,8 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 
 	protected void cloneObjects(AbstractMultiModalProblemKnown b) {
 		super.cloneObjects(b);
-		if (b.m_Optima != null)
+		if (b.m_ListOfOptima != null)
-			this.m_Optima           = (Population)((Population)b.m_Optima).clone();
+			this.m_ListOfOptima           = (Population)((Population)b.m_ListOfOptima).clone();
 //		if (b.m_Range != null) {
 //			this.m_Range          = new double[b.m_Range.length][b.m_Range[0].length];
 //			for (int i = 0; i < this.m_Range.length; i++) {
@@ -87,9 +87,9 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 		// population init must be last
 		// it set's fitcalls and generation to zero
 		population.init();
-		if (m_Optima == null) {
+		if (m_ListOfOptima == null) {
 			this.m_GlobalOpt = Double.NEGATIVE_INFINITY;
-			m_Optima = new Population();
+			m_ListOfOptima = new Population();
 			this.initListOfOptima();
 		}
 	}
@@ -97,7 +97,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	public void initProblem() {
 		super.initProblem();
 		this.m_GlobalOpt = Double.NEGATIVE_INFINITY;
-		m_Optima = new Population();
+		m_ListOfOptima = new Population();
 		this.initListOfOptima();
 	}
 
@@ -194,7 +194,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 				m_GlobalOpt = tmp;
 			}
 		} 
-		this.m_Optima.add(tmpIndy);
+		this.m_ListOfOptima.add(tmpIndy);
 	}
 
 	/** 
@@ -210,7 +210,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 	 * @return population
 	 */
 	public Population getRealOptima() {
-		return this.m_Optima;
+		return this.m_ListOfOptima;
 	}
 
 	/** This method returns the Number of Identified optima

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

@@ -5,6 +5,7 @@ import java.util.BitSet;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.GAIndividualBinaryData;
 import eva2.server.go.individuals.InterfaceDataTypeBinary;
+import eva2.server.go.individuals.InterfaceGAIndividual;
 import eva2.server.go.populations.Population;
 import eva2.server.go.strategies.InterfaceOptimizer;
 
@@ -16,8 +17,10 @@ public abstract class AbstractProblemBinary extends AbstractOptimizationProblem
 	}
 	
 	protected void initTemplate() {
-		this.m_Template         = new GAIndividualBinaryData();
+		if (m_Template == null) this.m_Template         = new GAIndividualBinaryData();
-        ((InterfaceDataTypeBinary)this.m_Template).setBinaryDataLength(this.getProblemDimension());
+		if (((InterfaceGAIndividual)this.m_Template).getGenotypeLength()!=this.getProblemDimension()) {
+			((InterfaceDataTypeBinary)this.m_Template).setBinaryDataLength(this.getProblemDimension());
+		}
 	}
 	
 	public void cloneObjects(AbstractProblemBinary o) {

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

@@ -23,7 +23,7 @@ import eva2.server.go.strategies.InterfaceOptimizer;
 
 import eva2.server.go.problems.Interface2DBorderProblem;
 
-public class ExternalRuntimeProblem extends AbstractOptimizationProblem implements Interface2DBorderProblem {
+public class ExternalRuntimeProblem extends AbstractOptimizationProblem implements Interface2DBorderProblem, InterfaceProblemDouble {
 
 	protected AbstractEAIndividual      m_OverallBest       = null;
     protected int                       m_ProblemDimension  = 10;
@@ -53,8 +53,17 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem implemen
 					writer.write(c);
 					writer.flush();
 				}
+//				System.out.println("monitor-thread finished!");
 			} catch (IOException ioe) {
 				ioe.printStackTrace(System.err);
+			} finally {
+				try {
+					reader.close();
+					writer.close();
+				} catch (IOException e) {
+					// TODO Auto-generated catch block
+					e.printStackTrace();
+				}
 			}
 		}
 	}
@@ -115,7 +124,7 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem implemen
         population.init();
     }
     
-    protected double[][] makeRange() {
+    public double[][] makeRange() {
 	    double[][] range = new double[this.m_ProblemDimension][2];
 	    for (int i = 0; i < range.length; i++) {
 	        range[i][0] = getRangeLowerBound(i);
@@ -124,11 +133,11 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem implemen
 	    return range;
     }
     
-    protected double getRangeLowerBound(int dim) {
+    public double getRangeLowerBound(int dim) {
     	return m_lowerBound;
     }
     
-    protected double getRangeUpperBound(int dim) {
+    public double getRangeUpperBound(int dim) {
     	return m_upperBound;
     }
     
@@ -137,14 +146,13 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem implemen
     }
 
     /** This method evaluate a single individual and sets the fitness values
-     * @param individual    The individual that is to be evalutated
+     * @param individual    The individual that is to be evaluatated
      */
     public void evaluate(AbstractEAIndividual individual) {
         double[]        x;
 //        double[]        fitness;
 
-        x = new double[((InterfaceDataTypeDouble) individual).getDoubleData().length];
+        x = getXVector(individual);
-        System.arraycopy(((InterfaceDataTypeDouble) individual).getDoubleData(), 0, x, 0, x.length);
 
      //TODO call external runtime
         double[] fit = eval(x);
@@ -158,22 +166,20 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem implemen
         }
     }
     
-	protected double[] eval(double[] x) {
+	protected double[] getXVector(AbstractEAIndividual individual) {
+		double[] x;
+		x = new double[((InterfaceDataTypeDouble) individual).getDoubleData().length];
+        System.arraycopy(((InterfaceDataTypeDouble) individual).getDoubleData(), 0, x, 0, x.length);
+		return x;
+	}
+    
+    public static List<String> runProcess(List<String> parameters, String workingDir) {
 		Process process;
         ProcessBuilder pb;
-        
+		List<String> results  = new ArrayList<String>(); 
-        ArrayList<Double> fitList = new ArrayList<Double>();
 		try {
-			List<String> parameters=new ArrayList<String>();
-			parameters.add(this.m_Command);
-			if (additionalArg!=null && (additionalArg.length()>0)) {
-				parameters.add(additionalArg);
-			}
-			for(int i=0;i<this.m_ProblemDimension;i++){
-				parameters.add(new String(""+x[i]));
-			}
 			pb = new ProcessBuilder(parameters);
-			pb.directory(new File(this.m_WorkingDir));
+			pb.directory(new File(workingDir));
 			process=pb.start();
 			BufferedReader br = new BufferedReader(new InputStreamReader(process.getInputStream()));
             Thread thread = new MonitorInputStreamThread(process.getErrorStream());//grab the Error Stream
@@ -184,24 +190,58 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem implemen
 				if (line.contains(" ")) {
 					String[] parts = line.split(" ");
 					for (String str : parts) {
-						fitList.add(new Double(str));
+						results.add(str);
 					}
 				} else {
-					fitList.add(new Double(line)); 
+					results.add(line); 
 				}
 			}
+			br.close();
 		} catch (IOException e) {
-			System.err.println("IO Error in ExternalRuntimeProblem!");
+			System.err.println("IO Error when calling external command!");
-			e.printStackTrace();
-		} catch (NumberFormatException e) {
-			System.err.println("Error: " + m_Command + " delivered malformatted output for " + BeanInspector.toString(x));
 			e.printStackTrace();
 		}
-		double[] fit = new double[fitList.size()];
+		return results;
-		for (int i=0; i<fit.length; i++) {
+	}
-			fit[i] = fitList.get(i);
+
-		}
+    public double[] eval(double[] x) {
-		return fit;
+    	if (x==null) throw new RuntimeException("Error, x=null value received in ExternalRuntimeProblem.eval");
+    	ArrayList<Double> fitList = new ArrayList<Double>();
+
+    	List<String> parameters=new ArrayList<String>();
+    	parameters.add(this.m_Command);
+    	if (additionalArg!=null && (additionalArg.length()>0)) parameters.add(additionalArg);
+    	for(int i=0;i<this.m_ProblemDimension;i++){
+    		String p = prepareParameter(x, i);
+    		parameters.add(p);
+    	}
+
+    	List<String> res = runProcess(parameters, m_WorkingDir);
+    	try {
+    		for (String str : res) {
+    			fitList.add(new Double(str));
+    		}
+    	} catch (NumberFormatException e) {
+    		System.err.println("Error: " + m_Command + " delivered malformatted output for " + BeanInspector.toString(x));
+    		e.printStackTrace();
+    	}
+    	double[] fit = new double[fitList.size()];
+    	for (int i=0; i<fit.length; i++) {
+    		fit[i] = fitList.get(i);
+    	}
+    	return fit;
+    }
+
+    /**
+     * How to prepare a given parameter within a double array to present it 
+     * to the external program.
+     * 
+     * @param x
+     * @param i
+     * @return
+     */
+    protected String prepareParameter(double[] x, int i) {
+		return new String(""+x[i]);
 	}
 
     /** This method returns a string describing the optimization problem.

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

@@ -13,9 +13,18 @@ import eva2.server.go.populations.Population;
 
 public interface InterfaceLocalSearchable extends InterfaceOptimizationProblem {
 
-
+  /**
+   * Perform a single local search step on each of the given individuals.
+   * 
+   * @param pop
+   */
   public void doLocalSearch(Population pop);
 
+  /**
+   * Estimate the cost of one local search step -- more precisely the cost of the doLocalSearch call per one individual.
+   * 
+   * @return
+   */
   public double getLocalSearchStepFunctionCallEquivalent();
 
 }

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

@@ -5,6 +5,7 @@ import java.util.ArrayList;
 import eva2.gui.BeanInspector;
 import eva2.gui.Chart2DDPointIconCircle;
 import eva2.gui.Chart2DDPointIconText;
+import eva2.gui.GenericObjectEditor;
 import eva2.gui.GraphPointSet;
 import eva2.gui.Plot;
 import eva2.gui.TopoPlot;
@@ -65,7 +66,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 //    private boolean                         m_UseClearing                   = false;
     private boolean							m_UseArchive					= true;
     private boolean                         m_UseSpeciesDifferentation      = true;
-    private boolean                         m_UseSpeciesConvergence         = true;
+    private boolean                         m_UseSpeciesMerging         = true;
 //    private boolean                         m_UseHaltingWindow              = true;
     private int                             m_PopulationSize                = 50;
     private int								convergedCnt					= 0;
@@ -96,7 +97,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         this.m_minGroupSize					= a.m_minGroupSize;
 //        this.m_UseClearing                  = a.m_UseClearing;
         this.m_UseSpeciesDifferentation     = a.m_UseSpeciesDifferentation;
-        this.m_UseSpeciesConvergence        = a.m_UseSpeciesConvergence;
+        this.m_UseSpeciesMerging        = a.m_UseSpeciesMerging;
 //        this.m_UseHaltingWindow             = a.m_UseHaltingWindow;
         this.m_PopulationSize               = a.m_PopulationSize;
         this.haltingWindow					= a.haltingWindow;
@@ -141,6 +142,10 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         this.firePropertyChangedEvent("FirstGenerationPerformed");
     }
     
+    public void hideHideable() {
+    	GenericObjectEditor.setHideProperty(this.getClass(), "population", true);
+    	setUseMerging(isUseMerging());
+    }
     /** This method will evaluate the current population using the
      * given problem.
      * @param population The population that is to be evaluated
@@ -511,7 +516,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                 //if (this.m_Show) this.plot();
             }
 
-            if (this.m_UseSpeciesConvergence) {
+            if (this.m_UseSpeciesMerging) {
 ///////////////////////////// species convergence phase
                 if (TRACE) {
                 	System.out.println("-Species convergence:");
@@ -549,7 +554,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                     spec1 = (Population)this.m_Species.get(i1);
                     for (int i2 = i1+1; i2 < this.m_Species.size(); i2++) {
                         spec2 = (Population)this.m_Species.get(i2);
-                        if (this.m_CAForSpeciesConvergence.convergingSpecies(spec1, spec2)) {
+                        if (this.m_CAForSpeciesConvergence.mergingSpecies(spec1, spec2)) {
                             if (TRACE) System.out.println("--------------------Merging species (" + i1 +", " +i2 +") ["+spec1.size()+"/"+spec2.size()+"]");
 //                            this.m_CAForSpeciesConvergence.convergingSpecies(spec1, spec2);
                             if (isActive(spec1) && isActive(spec2)) {
@@ -756,13 +761,13 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
      * of species differentiation.
      * @return The current status of this flag
      */
-    public boolean getApplyDifferentation() {
+    public boolean getApplyDifferentiation() {
         return this.m_UseSpeciesDifferentation;
     }
-    public void setApplyDifferentation(boolean b){
+    public void setApplyDifferentiation(boolean b){
         this.m_UseSpeciesDifferentation = b;
     }
-    public String applyDifferentationTipText() {
+    public String applyDifferentiationTipText() {
         return "Toggle the species differentation mechanism.";
     }
 
@@ -792,18 +797,19 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         return "With a halting window converged species are frozen.";
     }
 
-    /** This method allows you to set/get the switch that toggels the use
+    /** This method allows you to set/get the switch that toggles the use
      * of species convergence.
      * @return The current status of this flag
      */
-    public boolean getApplyConvergence() {
+    public boolean isUseMerging() {
-        return this.m_UseSpeciesConvergence;
+        return this.m_UseSpeciesMerging;
     }
-    public void setApplyConvergence(boolean b){
+    public void setUseMerging(boolean b){
-        this.m_UseSpeciesConvergence = b;
+        this.m_UseSpeciesMerging = b;
+        GenericObjectEditor.setHideProperty(this.getClass(), "mergingCA", !m_UseSpeciesMerging);
     }
-    public String applyConvergenceTipText() {
+    public String useMergingTipText() {
-        return "Toggle the species convergence mechanism.";
+        return "Toggle the use of species merging.";
     }
 
     /** Choose a population based optimizing technique to use
@@ -826,27 +832,27 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     /** The cluster algorithm on which the species differentiation is based
      * @return The current clustering method
      */
-    public InterfaceClustering getDifferentationCA() {
+    public InterfaceClustering getDifferentiationCA() {
         return this.m_CAForSpeciesDifferentation;
     }
-    public void setDifferentationCA(InterfaceClustering b){
+    public void setDifferentiationCA(InterfaceClustering b){
         this.m_CAForSpeciesDifferentation = b;
     }
-    public String differentationCATipText() {
+    public String differentiationCATipText() {
         return "The cluster algorithm on which the species differentation is based.";
     }
 
     /** The Cluster Algorithm on which the species convergence is based.
      * @return The current clustering method
      */
-    public InterfaceClustering getConvergenceCA() {
+    public InterfaceClustering getMergingCA() {
         return this.m_CAForSpeciesConvergence;
     }
-    public void setConvergenceCA(InterfaceClustering b){
+    public void setMergingCA(InterfaceClustering b){
         this.m_CAForSpeciesConvergence = b;
     }
-    public String convergenceCATipText() {
+    public String mergingCATipText() {
-        return "The cluster algorithm on which the species convergence is based.";
+        return "The cluster algorithm on which the species merging is based.";
     }
 
     public void setUseArchive(boolean v) {
@@ -869,7 +875,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         this.m_SpeciesCycle = b;
     }
     public String speciesCycleTipText() {
-        return "Determines how often species differentation/convergence is performed.";
+        return "Determines how often species differentation/merging is performed.";
     }
 
     /** TDetermines how often show is performed.
@@ -884,6 +890,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     public String showCycleTipText() {
         return "Determines how often show is performed (generations); set to zero to deactivate.";
     }
+    
     /** Determines the size of the initial population.
      * @return This number gives initial population size.
      */
@@ -922,14 +929,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 	public int getHaltingWindow() {
 		return haltingWindow;
 	}
-
 	/**
 	 * @param haltingWindow the haltingWindow to set
 	 */
 	public void setHaltingWindow(int haltingWindow) {
 		this.haltingWindow = haltingWindow;
 	}
-
 	public String haltingWindowTipText() {
 		return "Lenght of the halting window defining when a cluster is seen as converged and frozen; set to zero to disable.";
 	}
@@ -943,4 +948,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 		return m_Undifferentiated.size() + " \t " + actives + " \t " + (m_Species.size()-actives);
 	}
 
+	public String[] customPropertyOrder() {
+		return new String[]{"mergingCA", "differentiationCA"};
+	}
 }

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

@@ -690,6 +690,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
     public void setDEType(DETypeEnum s) {
         this.m_DEType = s;
         // show mt for trig. DE only
+        GenericObjectEditor.setShowProperty(this.getClass(), "lambda", s==DETypeEnum.DE2_CurrentToBest);
         GenericObjectEditor.setShowProperty(this.getClass(), "mt", s==DETypeEnum.TrigonometricDE);
     }
     public DETypeEnum getDEType() {

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

@@ -47,7 +47,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 
 	private int	                                              subsetsize	      = 5;
 
-	private int	                                              globalSearchSteps	= 1;
+	private int	                                              globalSearchIterations	= 1;
 
 	private boolean	                                          lamarckism	      = true;
 
@@ -78,7 +78,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 		this.m_Identifier = a.m_Identifier;
 		this.localSearchSteps = a.localSearchSteps;
 		this.subsetsize = a.subsetsize;
-		this.globalSearchSteps = a.globalSearchSteps;
+		this.globalSearchIterations = a.globalSearchIterations;
 		this.lamarckism = a.lamarckism;
 	}
 
@@ -120,9 +120,8 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 		if (TRACE) System.out.println("global search");
 		this.m_GlobalOptimizer.optimize();
 
-		if ((this.m_GlobalOptimizer.getPopulation().getGeneration()
+		if (((this.m_GlobalOptimizer.getPopulation().getGeneration() % this.globalSearchIterations) == 0)
-		    % this.globalSearchSteps == 0)
+		    && (this.localSearchSteps > 0)
-		    && (this.localSearchSteps != 0)
 		    && (this.m_Problem instanceof InterfaceLocalSearchable)) {
 			// here the local search is performed
 			if (TRACE)
@@ -282,7 +281,9 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 	 * @return description
 	 */
 	public String globalInfo() {
-		return "This is a basic generational Memetic Algorithm.";
+		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.";
 	}
 
 	/**
@@ -291,7 +292,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 	 * @return The name of the algorithm
 	 */
 	public String getName() {
-		return "Memetic-Algorithm";
+		return "MemeticAlgorithm";
 	}
 
 	/**
@@ -332,18 +333,16 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 	}
 
 	/**
-	 * Choose the number of local search steps to perform per selected individual
+	 * Choose the number of local search steps to perform per selected individual.
 	 *
 	 * @param localSearchSteps
 	 */
 	public void setLocalSearchSteps(int localSearchSteps) {
 		this.localSearchSteps = localSearchSteps;
 	}
-
 	public int getLocalSearchSteps() {
 		return localSearchSteps;
 	}
-
 	public String localSearchStepsTipText() {
 		return "Choose the number of local search steps to perform per selected individual.";
 	}
@@ -353,18 +352,20 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 	 *
 	 * @param globalSearchSteps
 	 */
-	public void setGlobalSearchSteps(int globalSearchSteps) {
+	public void setGlobalSearchIterations(int globalSearchSteps) {
-		this.globalSearchSteps = globalSearchSteps;
+		this.globalSearchIterations = globalSearchSteps;
 	}
-
+	public int getGlobalSearchIterations() {
-	public int getGlobalSearchSteps() {
+		return globalSearchIterations;
-		return globalSearchSteps;
 	}
-
+	public String globalSearchIterationsTipText() {
-	public String globalSearchStepsTipText() {
 		return "Choose the interval between the application of the local search.";
 	}
 
+    public InterfaceSolutionSet getAllSolutions() {
+    	return new SolutionSet(getPopulation());
+    }
+
 	/**
 	 * Choose the number of individual to be locally optimized
 	 *
@@ -373,36 +374,35 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
 	public void setSubsetsize(int subsetsize) {
 		this.subsetsize = subsetsize;
 	}
-    
+    public int getSubsetsize() {
-    public InterfaceSolutionSet getAllSolutions() {
-    	return new SolutionSet(getPopulation());
-    }
-	public int getSubsetsize() {
 		return subsetsize;
 	}
-
 	public String subsetsizeTipText() {
-		return "Choose the number of individual to be locally optimized.";
+		return "Choose the number of individuals to be locally optimized.";
 	}
 
 	/**
-	 * Toggel between Lamarcksim and the Baldwin Effect
+	 * Toggle between Lamarckism and the Baldwin Effect
 	 *
 	 * @param lamarckism
 	 */
 	public void setLamarckism(boolean lamarckism) {
 		this.lamarckism = lamarckism;
 	}
-
-	public boolean getLamarckism() {
-		return this.lamarckism;
-	}
-
 	public String lamarckismTipText() {
-		return "Toggel between Lamarcksim and the Baldwin Effect.";
+		return "Toggle between Lamarckism and the Baldwin Effect.";
 	}
-
 	public boolean isLamarckism() {
 		return lamarckism;
 	}
+
+	public InterfaceSelection getSubSetSelector() {
+		return selectorPlug;
+	}
+	public void setSubSetSelector(InterfaceSelection selectorPlug) {
+		this.selectorPlug = selectorPlug;
+	}
+	public String subSetSelectorTipText() {
+		return "Selection method to select the subset on which local search is to be performed.";
+	}
 }

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

@@ -95,7 +95,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	public static final int						defaultType = 0;
 	public static final int						resetType = 99;
 	transient final static String				partTypeKey = "ParticleType";
-	transient final static String				partBestPosKey = "BestPosition";
+	public transient final static String		partBestPosKey = "BestPosition";
 	transient final static String				partBestFitKey = "BestFitness";
 	transient final static String				partVelKey = "Velocity";
 	transient final static String 				multiSwTypeKey="MultiSwarmType";
@@ -1580,7 +1580,8 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	public void setPopulation(Population pop){
 		this.m_Population = pop;
 		if (pop.size() != pop.getPopulationSize()) { // new particle count!
-			init();
+			tracedVelocity = null;
+			initByPopulation(null, false);
 		} else for (int i=0; i<pop.size(); i++) {
 			AbstractEAIndividual indy = pop.getEAIndividual(i);
 			if (indy==null) {

src/eva2/server/stat/AbstractStatistics.java

@@ -275,7 +275,7 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 		}
 
 		if (printFinalVerbosity() && (additionalInfoSums != null)) {
-			printToTextListener("     Averaged additional info: ");
+			printToTextListener("     Averaged additional info sums: ");
 			for (int i=0; i<additionalInfoSums.length; i++) if (additionalInfoSums[i]!=null) printToTextListener(" \t"+(additionalInfoSums[i]/optRunsPerformed));
 			printToTextListener("\n     Averaged last additional info: ");
 			for (int i=0; i<lastAdditionalInfoSums.length; i++) if (lastAdditionalInfoSums[i]!=null) printToTextListener(" \t"+(lastAdditionalInfoSums[i]/optRunsPerformed));

src/eva2/tools/Mathematics.java

@@ -1035,4 +1035,14 @@ public class Mathematics {
 			destRange[i][1] = Math.min(r1[i][1], r2[i][1]);
 		}
 	}
+
+	/**
+	 * Fill the front of an array with data from a given source array.
+	 * 
+	 * @param dest
+	 * @param src
+	 */
+	public static void fillFront(double[] dest, double[] src) {
+		System.arraycopy(src, 0, dest, 0, Math.min(dest.length, src.length));
+	}
 }