Some repairs (GradientDescentAlgorithm, F1 and F2 are now derivable); renamed Population.m_Size to m_TargetSize to avoid misunderstandings; Exceptions are now caught and their messages tried to display in a JOptionPane in addition to printing to System.err.

src/eva2/OptimizerFactory.java

@@ -143,7 +143,7 @@ public class OptimizerFactory {
 
 		DifferentialEvolution de = new DifferentialEvolution();
 		de.SetProblem(problem);
-		de.getPopulation().setPopulationSize(popsize);
+		de.getPopulation().setTargetSize(popsize);
 		de.setDEType(DETypeEnum.DE2_CurrentToBest);
 		de.setF(f);
 		de.setK(CR);
@@ -260,7 +260,7 @@ public class OptimizerFactory {
 
 		GeneticAlgorithm ga = new GeneticAlgorithm();
 		ga.SetProblem(problem);
-		ga.getPopulation().setPopulationSize(popsize);
+		ga.getPopulation().setTargetSize(popsize);
 		ga.setParentSelection(select);
 		ga.setPartnerSelection(select);
 		ga.addPopulationChangedEventListener(listener);
@@ -374,7 +374,7 @@ public class OptimizerFactory {
 		tmpIndi.setCrossoverProbability(0);
 
 		HillClimbing hc = new HillClimbing();
-		hc.getPopulation().setPopulationSize(pop);
+		hc.getPopulation().setTargetSize(pop);
 		hc.addPopulationChangedEventListener(listener);
 		hc.SetProblem(problem);
 		hc.init();
@@ -405,7 +405,7 @@ public class OptimizerFactory {
 		tmpIndi.setCrossoverProbability(0);
 
 		MonteCarloSearch mc = new MonteCarloSearch();
-		mc.getPopulation().setPopulationSize(popsize);
+		mc.getPopulation().setTargetSize(popsize);
 		mc.addPopulationChangedEventListener(listener);
 		mc.SetProblem(problem);
 		mc.init();
@@ -447,7 +447,7 @@ public class OptimizerFactory {
 
 		ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
 		pso.SetProblem(problem);
-		pso.getPopulation().setPopulationSize(popsize);
+		pso.getPopulation().setTargetSize(popsize);
 		pso.setPhi1(phi1);
 		pso.setPhi2(phi2);
 		pso.setSpeedLimit(k);
@@ -493,7 +493,7 @@ public class OptimizerFactory {
 		sa.setAlpha(alpha);
 		sa.setInitialTemperature(temperature);
 		sa.SetProblem(problem);
-		sa.getPopulation().setPopulationSize(popsize);
+		sa.getPopulation().setTargetSize(popsize);
 		sa.addPopulationChangedEventListener(listener);
 		sa.init();
 
@@ -732,7 +732,7 @@ public class OptimizerFactory {
 	 * @return
 	 */
 	public static GOParameters makeParams(InterfaceOptimizer opt, AbstractOptimizationProblem problem, InterfaceTerminator term) {
-		return makeParams(opt, opt.getPopulation().getPopulationSize(), problem, randSeed, term);
+		return makeParams(opt, opt.getPopulation().getTargetSize(), problem, randSeed, term);
 	}
 	/**
 	 * Set the population size, initialize the population and return a parameter structure containing all 

src/eva2/server/go/GOStandaloneVersion.java

@@ -317,7 +317,7 @@ public class GOStandaloneVersion implements InterfaceGOStandalone, InterfacePopu
                 ga.setParentSelection(tour);
                 ga.setPartnerSelection(tour);
                 this.m_GO.setOptimizer(ga);
-                this.m_GO.getOptimizer().getPopulation().setPopulationSize(100);
+                this.m_GO.getOptimizer().getPopulation().setTargetSize(100);
                 F1Problem problem = new F1Problem();
                 tmpIndy = new GAIndividualDoubleData();
                 ((GAIndividualDoubleData)tmpIndy).setCrossoverOperator(new CrossoverGANPoint());
@@ -335,7 +335,7 @@ public class GOStandaloneVersion implements InterfaceGOStandalone, InterfacePopu
             	this.m_OutputFileName = "X360_StandardES";
                 EvolutionStrategies es = new EvolutionStrategies();
                 this.m_GO.setOptimizer(es);
-                this.m_GO.getOptimizer().getPopulation().setPopulationSize(50);
+                this.m_GO.getOptimizer().getPopulation().setTargetSize(50);
                 F1Problem problem = new F1Problem();
                 tmpIndy = new ESIndividualDoubleData();
                 ((AbstractEAIndividual)tmpIndy).setMutationOperator(new MutateESLocal());

src/eva2/server/go/MOCCOStandalone.java

@@ -128,7 +128,7 @@ public class MOCCOStandalone implements InterfaceGOStandalone, InterfacePopulati
                 while (!tmpP.isFinished()) { try { Thread.sleep(1000); } catch (java.lang.InterruptedException e) { }}
                 this.m_State.m_InitialPopulationSize = Math.max(1, this.m_State.m_InitialPopulationSize);
                 Population pop = new Population();
-                pop.setPopulationSize(this.m_State.m_InitialPopulationSize);
+                pop.setTargetSize(this.m_State.m_InitialPopulationSize);
                 this.m_State.m_CurrentProblem = (InterfaceOptimizationProblem) this.m_State.m_OriginalProblem.clone();
                 this.m_State.m_CurrentProblem.initPopulation(pop);
                 this.m_State.m_CurrentProblem.evaluate(pop);

src/eva2/server/go/operators/archiving/ArchivingAllDominating.java

@@ -46,7 +46,7 @@ public class ArchivingAllDominating extends AbstractArchiving implements java.io
             if ((pop.getArchive().size() == 0) && (pop.size() > 0)) {
                 SelectBestIndividuals select = new SelectBestIndividuals();
                 select.setObeyDebsConstViolationPrinciple(true);
-                pop.getArchive().addPopulation(select.selectFrom(pop, pop.getArchive().getPopulationSize()));
+                pop.getArchive().addPopulation(select.selectFrom(pop, pop.getArchive().getTargetSize()));
             }
         } else {
             // test for each element in population if it

src/eva2/server/go/operators/archiving/ArchivingMaxiMin.java

@@ -59,8 +59,8 @@ public class ArchivingMaxiMin implements InterfaceArchiving, java.io.Serializabl
         this.m_MaxiMin.convertMultiObjective2SingleObjective(tmpPop);
         this.m_Selection.setObeyDebsConstViolationPrinciple(this.m_ObeyDebsConstViolationPrinciple);
         this.m_Selection.prepareSelection(tmpPop);
-        archive = this.m_Selection.selectFrom(tmpPop, pop.getArchive().getPopulationSize());
+        archive = this.m_Selection.selectFrom(tmpPop, pop.getArchive().getTargetSize());
-        archive.setPopulationSize(pop.getArchive().getPopulationSize());
+        archive.setTargetSize(pop.getArchive().getTargetSize());
 
         // now unconvert from SO to MO
         for (int i = 0; i < archive.size(); i++) {

src/eva2/server/go/operators/archiving/ArchivingNSGAII.java

@@ -74,21 +74,21 @@ public class ArchivingNSGAII extends ArchivingNSGA implements java.io.Serializab
 
         // Now init the new archive
         Population archive = new Population();
-        archive.setPopulationSize(pop.getArchive().getPopulationSize());
+        archive.setTargetSize(pop.getArchive().getTargetSize());
 
         // Now add the fronts to the archive
         int index = 0;
-        while ((index < fronts.length) && ((archive.size() + fronts[index].size()) < archive.getPopulationSize())) {
+        while ((index < fronts.length) && ((archive.size() + fronts[index].size()) < archive.getTargetSize())) {
             archive.addPopulation(fronts[index]);
             index++;
         }
 
-        if ((index < fronts.length) && (archive.size() < archive.getPopulationSize())) {
+        if ((index < fronts.length) && (!archive.targetSizeReached())) {
             // In this case there are still elements left in the front which could be added to the archive!
             // and there is still some place left in the archive
             // therefore we could add some individuals from front[index]
             // to the archive using the crowding distance sorting
-            fronts[index].setPopulationSize(archive.getPopulationSize() - archive.size());
+            fronts[index].setTargetSize(archive.getTargetSize() - archive.size());
             this.m_Cleaner.removeSurplusIndividuals(fronts[index]);
             archive.addPopulation(fronts[index]);
         }

src/eva2/server/go/operators/archiving/ArchivingPESAII.java

@@ -136,7 +136,7 @@ public class ArchivingPESAII extends AbstractArchiving implements java.io.Serial
         // Now check whether there are individuals to remove
         int         bigSqueeze, index;
         int[]       squeezeFactor;
-        while(archive.size() > archive.getPopulationSize()) {
+        while(archive.targetSizeExceeded()) {
             squeezeFactor   = this.calculateSqueezeFactor(archive);
             bigSqueeze = 0;
             index = -1;

src/eva2/server/go/operators/archiving/ArchivingSPEAII.java

@@ -98,7 +98,7 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial
 
         // Now init the new archive
         Population archive = new Population();
-        archive.setPopulationSize(pop.getArchive().getPopulationSize());
+        archive.setTargetSize(pop.getArchive().getTargetSize());
 
 //        archive = this.m_Selection.selectFrom(tmpPop, archive.getPopulationSize());
 
@@ -160,7 +160,7 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial
 
         // if there is some place left let's add some more
         int currentLevel = 0;
-        while (archive.size() < archive.getPopulationSize()) {
+        while (!archive.targetSizeReached()) {
             currentLevel++;
             for (int i = 0; i < RawFitness.length; i++) {
                 if ((RawFitness[i] >= currentLevel) && (RawFitness[i] < currentLevel +1)) {
@@ -194,7 +194,7 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial
         // RawFitness for the lowest kthDistance()!!!
         int     ICurSma;
         double  curSmall, highestLevel;
-        while (archive.size() > archive.getPopulationSize()) {
+        while (archive.targetSizeExceeded()) {
             highestLevel    = 0;
             RawFitness      = this.calculateRawFitness(archive);
             for (int i = 0; i < RawFitness.length; i++) {

src/eva2/server/go/operators/archiving/InformationRetrievalInserting.java

@@ -31,9 +31,9 @@ public class InformationRetrievalInserting implements InterfaceInformationRetrie
         Population archive = pop.getArchive();
 
         if (archive == null) return;
-        if (archive.size() < pop.getPopulationSize()) {
+        if (archive.size() < pop.getTargetSize()) {
             // remove archive size individuals from pop
-            pop.removeNIndividuals(archive.size()-(pop.getPopulationSize()-pop.size()));
+            pop.removeNIndividuals(archive.size()-(pop.getTargetSize()-pop.size()));
         } else {
             pop.clear();
         }

src/eva2/server/go/operators/archiving/InformationRetrievalReplacing.java

@@ -33,7 +33,7 @@ public class InformationRetrievalReplacing implements InterfaceInformationRetrie
         Population tmp = new Population();
 
         tmp.addPopulation(archive);
-        while (tmp.size() < archive.getPopulationSize()) {
+        while (tmp.size() < archive.getTargetSize()) {
             tmp.add(pop.get(RNG.randomInt(0,pop.size()-1)));
         }
 

src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsDynamicHyperCube.java

@@ -35,7 +35,7 @@ public class RemoveSurplusIndividualsDynamicHyperCube implements InterfaceRemove
         double[][]  fitness;
         double[]    space;
         int         indexSmallHyperCube;
-        while(archive.size() > archive.getPopulationSize()) {
+        while(archive.targetSizeExceeded()) {
             // select the individual with the least space around him
             // to do this i got to find the next smaller and the next bigger one
             fitness = new double[archive.size()][];

src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsStaticHyperCube.java

@@ -47,7 +47,7 @@ public class RemoveSurplusIndividualsStaticHyperCube extends RemoveSurplusIndivi
             ((AbstractEAIndividual)archive.get(i)).putData("HyperCube", new Double(space[i]));
         }
 
-        while(archive.size() > archive.getPopulationSize()) {
+        while(archive.targetSizeExceeded()) {
             // select the individual with the least space around him
             // to do this i got to find the next smaller and the next bigger one
             smallestHyperCube   = ((Double)((AbstractEAIndividual)archive.get(0)).getData("HyperCube")).doubleValue();

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

@@ -273,7 +273,7 @@ public class ClusteringXMeans implements InterfaceClustering, java.io.Serializab
         ckm.setUseSearchSpace(true);
         ckm.m_Debug = true;
         Population pop = new Population();
-        pop.setPopulationSize(100);
+        pop.setTargetSize(100);
         F1Problem f1 = new F1Problem();
         f1.setProblemDimension(2);
         f1.setEAIndividual(new ESIndividualDoubleData());

src/eva2/server/go/operators/crossover/TestESCrossover.java

@@ -96,7 +96,7 @@ public class TestESCrossover implements java.io.Serializable {
     ActionListener initListener = new ActionListener() {
         public void actionPerformed(ActionEvent event) {
             m_Partners = new Population();
-            m_Partners.setPopulationSize(m_NumberOfPartners);
+            m_Partners.setTargetSize(m_NumberOfPartners);
             m_Partners.clear();
 
             InterfaceDataTypeDouble tmpIndyD = new ESIndividualDoubleData();
@@ -108,7 +108,7 @@ public class TestESCrossover implements java.io.Serializable {
             }
             tmpIndyD.setDoubleDataLength(m_Dimension);
             tmpIndyD.SetDoubleRange(newRange);
-            for (int i = 0; i < m_Partners.getPopulationSize(); i++) {
+            for (int i = 0; i < m_Partners.getTargetSize(); i++) {
                 tmpIndyEA = (AbstractEAIndividual)((AbstractEAIndividual)tmpIndyD).clone();
                 tmpIndyEA.init(m_Problem);
                 m_Partners.add(tmpIndyEA);
@@ -134,7 +134,7 @@ public class TestESCrossover implements java.io.Serializable {
     ActionListener init2Listener = new ActionListener() {
         public void actionPerformed(ActionEvent event) {
             m_Partners = new Population();
-            m_Partners.setPopulationSize(2);
+            m_Partners.setTargetSize(2);
             m_Partners.clear();
 
             InterfaceDataTypeDouble tmpIndyD = new ESIndividualDoubleData();
@@ -177,7 +177,7 @@ public class TestESCrossover implements java.io.Serializable {
     ActionListener init3Listener = new ActionListener() {
         public void actionPerformed(ActionEvent event) {
             m_Partners = new Population();
-            m_Partners.setPopulationSize(3);
+            m_Partners.setTargetSize(3);
             m_Partners.clear();
 
             InterfaceDataTypeDouble tmpIndyD = new ESIndividualDoubleData();

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

@@ -0,0 +1,75 @@
+package eva2.server.go.operators.distancemetric;
+
+import java.io.Serializable;
+
+import eva2.server.go.individuals.AbstractEAIndividual;
+import eva2.server.go.individuals.InterfaceDataTypeDouble;
+import eva2.server.go.strategies.ParticleSwarmOptimization;
+
+/**
+ * Define a metric on data stored within individuals, such as the personal best position
+ * in PSO.
+ * @author mkron
+ *
+ */
+public class IndividualDataMetric implements InterfaceDistanceMetric, Serializable {
+	private String dataKey = ParticleSwarmOptimization.partBestPosKey;
+	private boolean normedDistance = true; // flag whether to use normed distances (for InterfaceDataTypeDouble)
+	
+    public IndividualDataMetric() {}    
+    
+    public IndividualDataMetric(String key) {
+		dataKey = key;
+	}
+    
+    public IndividualDataMetric(IndividualDataMetric pBestMetric) {
+		// TODO Auto-generated constructor stub
+	}
+
+	/** This method allows you to make a deep clone of
+     * the object
+     * @return the deep clone
+     */
+    public Object clone() {
+    	return new IndividualDataMetric(this);
+    }
+    
+	public double distance(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
+		if (dataKey==null) throw new RuntimeException("Error, no data key defined in " + this.getClass().getName() + "::distance()");
+		else {
+			Object data1 = indy1.getData(dataKey);
+			Object data2 = indy2.getData(dataKey);
+			if (data1 instanceof double[] && (data2 instanceof double[])) {
+				if (normedDistance) {
+					double[][] range1 = ((InterfaceDataTypeDouble)indy1).getDoubleRange();
+					double[][] range2 = ((InterfaceDataTypeDouble)indy2).getDoubleRange();
+					return EuclideanMetric.normedEuclideanDistance((double[])data1, range1, (double[])data2, range2);
+				} else return EuclideanMetric.euclideanDistance((double[])data1, (double[])data2);
+			} else throw new RuntimeException("Error, invalid key data, double array required by " + this.getClass().getName());
+		}
+	}
+
+	public String dataKeyTipText() {
+		return "Name of the data key to use to retrieve individual data (double[] for now).";
+	}
+	public String getDataKey() {
+		return dataKey;
+	}
+	public void setDataKey(String dataKey) {
+		this.dataKey = dataKey;
+	}
+
+	public String normedDistanceTipText() {
+		return "Flag whether to use euclidean distance directly or normed by the double range."; 
+	}
+	public boolean isNormedDistance() {
+		return normedDistance;
+	}
+	public void setNormedDistance(boolean normedDistance) {
+		this.normedDistance = normedDistance;
+	}
+	
+	public String globalInfo() {
+		return "Uses individual object data (so far only double[]) to calculate the distance.";
+	}
+}

src/eva2/server/go/operators/migration/MOClusteringSeparation.java

@@ -217,8 +217,8 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria
             oldIPOP[i].clear();
             oldIPOP[i].addPopulation(newIPOP[i]);
             // todo remove this for nice pictures
-            if (oldIPOP[i].size() < oldIPOP[i].getPopulationSize()) {
+            if (!oldIPOP[i].targetSizeReached()) {
-                oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getPopulationSize()-oldIPOP[i].size()));
+                oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getTargetSize()-oldIPOP[i].size()));
             }
             if (this.m_Debug) System.out.println("Setting "+i+" to population size " + oldIPOP[i].size());
             islands[i].setPopulation(oldIPOP[i]);

src/eva2/server/go/operators/migration/MOConeSeparation.java

@@ -111,8 +111,8 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl
             oldIPOP[i].clear();
             oldIPOP[i].addPopulation(newIPOP[i]);
             // todo remove this for nice pictures
-            if (oldIPOP[i].size() < oldIPOP[i].getPopulationSize()) {
+            if (!oldIPOP[i].targetSizeReached()) {
-                oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getPopulationSize()-oldIPOP[i].size()));
+                oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getTargetSize()-oldIPOP[i].size()));
             }
             if (this.m_Debug) System.out.println("Setting island "+i+" to population size " + oldIPOP[i].size());
             allDom.addElementsToArchive(oldIPOP[i]);

src/eva2/server/go/operators/migration/MOXMeansSeparation.java

@@ -137,7 +137,7 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa
         newIPOP = this.m_XMeans.cluster(collector, c);
         for (int i = 0; i < islands.length; i++) {
             islands[i].getPopulation().clear();
-            islands[i].getPopulation().setPopulationSize(0);
+            islands[i].getPopulation().setTargetSize(0);
         }
         if (this.m_Debug) {
             Plot        plot;
@@ -218,12 +218,12 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa
             oldIPOP[i].clear();
             oldIPOP[i].addPopulation(newIPOP[i]);
             // todo remove this for nice pictures
-            if (oldIPOP[i].size() < oldIPOP[i].getPopulationSize()) {
+            if (!oldIPOP[i].targetSizeReached()) {
-                oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getPopulationSize()-oldIPOP[i].size()));
+                oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getFreeSlots()));
             }
             if (this.m_Debug) System.out.println("Setting "+i+" to population size " + oldIPOP[i].size());
             islands[i].setPopulation(oldIPOP[i]);
-            islands[i].getPopulation().setPopulationSize(oldIPOP[i].size());
+            islands[i].getPopulation().setTargetSize(oldIPOP[i].size());
         }
     }
 

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

@@ -413,6 +413,7 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali
 		}
 
 		if (TRACE_1) {
+			System.out.println("sigma=" + params.sigma);
 			System.out.print("psLen=" + (psNorm) + " ");
 			outputParams(params, mu);
 		}

src/eva2/server/go/operators/paretofrontmetrics/MetricSWithReference.java

@@ -105,7 +105,7 @@ public class MetricSWithReference implements InterfaceParetoFrontMetric, java.io
         if (this.m_ReferenceSMetric < 0) {
             Population              tmpPop = new Population();
             AbstractEAIndividual  tmpIndy;
-            tmpPop.setPopulationSize(this.m_Reference.length);
+            tmpPop.setTargetSize(this.m_Reference.length);
             tmpPop.clear();
             for (int i = 0; i < this.m_Reference.length; i++) {
                 tmpIndy = new ESIndividualDoubleData();

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

@@ -134,7 +134,7 @@ public class PostProcess {
     	for (int i=0; i<optsFound.length; i++) {
 			if (optsFound[i] != null) result.add(optsFound[i]);
 		}
-    	result.setPopulationSize(result.size());
+    	result.synchSize();
     	return result;
     }
 
@@ -212,7 +212,7 @@ public class PostProcess {
         		}
         	}
         }
-        result.setPopulationSize(result.size());
+        result.synchSize();
         return result;
     }
     
@@ -387,8 +387,8 @@ public class PostProcess {
 		hc.SetProblem(problem);
 		mute.init(problem.getIndividualTemplate(), problem);
 		hc.SetMutationOperator(mute);
-		if (pop.size() != pop.getPopulationSize()) {
+		if (pop.size() != pop.getTargetSize()) {
-			System.err.println(pop.size() + " vs. "+ pop.getPopulationSize());
+			System.err.println(pop.size() + " vs. "+ pop.getTargetSize());
 			System.err.println("warning: population size and vector size dont match! (PostProcess::processWithHC)");
 		}
 		hc.setPopulation(pop);

src/eva2/server/go/operators/selection/SelectAll.java

@@ -47,7 +47,7 @@ public class SelectAll implements InterfaceSelection, java.io.Serializable {
      */
     public Population selectFrom(Population population, int size) {
         Population result = new Population();
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
         if (this.m_ObeyDebsConstViolationPrinciple) {
             int index = 0;
             while (result.size() < size) {

src/eva2/server/go/operators/selection/SelectBestSingle.java

@@ -52,7 +52,7 @@ public class SelectBestSingle implements InterfaceSelection, java.io.Serializabl
         double                  currentBestValue;
 
         critSize = ((AbstractEAIndividual)population.get(0)).getFitness().length;
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
         if (this.m_ObeyDebsConstViolationPrinciple) {
             for (int i = 0; i < size; i++) {
                 currentCriteria     = RNG.randomInt(0, critSize-1);

src/eva2/server/go/operators/selection/SelectMONSGAIICrowedTournament.java

@@ -54,7 +54,7 @@ public class SelectMONSGAIICrowedTournament implements InterfaceSelection, java.
      */
     public Population selectFrom(Population population, int size) {
         Population result = new Population();
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
         for (int i = 0; i < size; i++) {
             result.add(this.select(population));
         }

src/eva2/server/go/operators/selection/SelectMOPESA.java

@@ -52,7 +52,7 @@ public class SelectMOPESA implements InterfaceSelection, java.io.Serializable {
      */
     public Population selectFrom(Population population, int size) {
         Population result = new Population();
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
         for (int i = 0; i < size; i++) {
             result.add(this.select(population));
         }

src/eva2/server/go/operators/selection/SelectMOPESAII.java

@@ -86,7 +86,7 @@ public class SelectMOPESAII implements InterfaceSelection, java.io.Serializable
      */
     public Population selectFrom(Population population, int size) {
         Population result = new Population();
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
         for (int i = 0; i < size; i++) {
             result.add(this.select(population));
         }

src/eva2/server/go/operators/selection/SelectParticleWheel.java

@@ -63,7 +63,7 @@ public class SelectParticleWheel implements InterfaceSelection, java.io.Serializ
      */
     public Population selectFrom(Population population, int size) {
         Population result = new Population();
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
         
         if (selectFixedSteps ) selectFixed(population, size, result);
         else selectDrawIndependent(population, size, result);

src/eva2/server/go/operators/selection/SelectRandom.java

@@ -55,7 +55,7 @@ public class SelectRandom implements InterfaceSelection, java.io.Serializable {
      */
     public Population selectFrom(Population population, int size) {
         Population result = new Population();
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
         if (this.m_ObeyDebsConstViolationPrinciple) {
             int index = 0, rand;
             while (result.size() < size) {

src/eva2/server/go/operators/selection/SelectTournament.java

@@ -50,7 +50,7 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
      */
     public Population selectFrom(Population population, int size) {
         Population result = new Population();
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
         for (int i = 0; i < size; i++) {
             result.add(this.select(population));
         }

src/eva2/server/go/operators/selection/SelectXProbRouletteWheel.java

@@ -95,7 +95,7 @@ public class SelectXProbRouletteWheel implements InterfaceSelection, java.io.Ser
      */
     public Population selectFrom(Population population, int size) {
         Population result = new Population();
-        result.setPopulationSize(size);
+        result.setTargetSize(size);
 
         if (true) {
             //this.m_TreeRoot = this.buildSelectionTree(population);

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

@@ -29,12 +29,8 @@ public class PBILPopulation extends Population implements Cloneable, java.io.Ser
     }
     
     public PBILPopulation(PBILPopulation population) {
-        this.m_Generation       = population.m_Generation;
+    	super(population);
-        this.m_FunctionCalls    = population.m_FunctionCalls;
+
-        this.m_Size             = population.m_Size;
-        for (int i = 0; i < population.size(); i++) {
-            this.add((((AbstractEAIndividual)population.get(i))).clone());
-        }
         this.m_ProbabilityVector = new double[population.m_ProbabilityVector.length];
         for (int i = 0; i < this.m_ProbabilityVector.length; i++) {
             this.m_ProbabilityVector[i] = population.m_ProbabilityVector[i];
@@ -86,7 +82,7 @@ public class PBILPopulation extends Population implements Cloneable, java.io.Ser
         BitSet                  tmpBitSet;
 
         this.clear();
-        for (int i = 0; i < this.m_Size; i++) {
+        for (int i = 0; i < this.getTargetSize(); i++) {
             tmpIndy = (InterfaceGAIndividual)((AbstractEAIndividual)template).clone();
             tmpBitSet = tmpIndy.getBGenotype();
             for (int j = 0; j < this.m_ProbabilityVector.length; j++) {

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

@@ -47,7 +47,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 
     protected int           m_Generation    = 0;
     protected int           m_FunctionCalls = 0;
-    protected int           m_Size          = 50;
+    protected int           m_TargetSize          = 50;
     protected Population    m_Archive       = null;
     PopulationInitMethod initMethod = PopulationInitMethod.individualDefault;
 	transient private ArrayList<InterfacePopulationChangedEventListener> listeners = null;
@@ -86,7 +86,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      */
     public Population(int initialCapacity) {
     	super(initialCapacity);
-    	setPopulationSize(initialCapacity);
+    	setTargetSize(initialCapacity);
     }
 
     public Population(Population population) {
@@ -110,7 +110,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     public void setSameParams(Population population) {
         this.m_Generation       = population.m_Generation;
         this.m_FunctionCalls    = population.m_FunctionCalls;
-        this.m_Size             = population.m_Size;
+        this.m_TargetSize             = population.m_TargetSize;
         this.useHistory 		= population.useHistory;
         this.notifyEvalInterval = population.notifyEvalInterval;
 //        this.m_Listener			= population.m_Listener;
@@ -196,15 +196,13 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * are reset and m_Size default Individuals are created and initialized by
      * the GAIndividual default init() method.
       */
-    public void defaultInitPopulation() {
+    public void defaultInit(AbstractEAIndividual template) {
-        GAIndividualBinaryData tmpIndy;
-
         this.m_Generation       = 0;
         this.m_FunctionCalls    = 0;
         this.m_Archive          = null;
         this.clear();
-        for (int i = 0; i < this.m_Size; i++) {
+        for (int i = 0; i < this.m_TargetSize; i++) {
-            tmpIndy = new GAIndividualBinaryData();
+            AbstractEAIndividual tmpIndy = (AbstractEAIndividual)template.clone();
             tmpIndy.defaultInit();
             super.add(tmpIndy);
         }
@@ -239,8 +237,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     		return;
     	}
     	AbstractEAIndividual template = pop.getEAIndividual(0);
-    	if (fillPop && (pop.size()<pop.getPopulationSize())) {
+    	if (fillPop && (pop.size()<pop.getTargetSize())) {
-    		for (int i=pop.size(); i<pop.getPopulationSize(); i++) pop.add((AbstractEAIndividual)template.clone());
+    		for (int i=pop.size(); i<pop.getTargetSize(); i++) pop.add((AbstractEAIndividual)template.clone());
     	}
     	if (template instanceof InterfaceDataTypeDouble) {
     		double[][] range = ((InterfaceDataTypeDouble)template).getDoubleRange();
@@ -695,7 +693,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     public Population getSortedBestFirst() {
     	Population result = this.cloneWithoutInds();
     	getSortedNIndividuals(size(), true, result);
-    	result.setPopulationSize(result.size());
+    	result.synchSize();
     	return result;
     }
     
@@ -741,7 +739,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         for (int i = skip; i < skip+n; i++) {
         	res.add(sorted.get(i));
         }
-        res.setPopulationSize(res.size());
+        res.synchSize();
     }
     
     /**
@@ -1041,7 +1039,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     }
 
     public String getName() {
-    	return "Population-"+getPopulationSize();
+    	return "Population-"+getTargetSize();
     }
     
     /**
@@ -1094,8 +1092,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * 
      * @param size
      */
-    public void setPopulationSize(int size) {
+    public void setTargetSize(int size) {
-        this.m_Size = size;
+        this.m_TargetSize = size;
     }
     
     /**
@@ -1104,16 +1102,16 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @param size
      * @return
      */
-    public Population setPopSize(int size) {
+    public Population setTargetPopSize(int size) {
-        this.m_Size = size;
+        setTargetSize(size);
         return this;
     }
     
-    public int getPopulationSize() {
+    public int getTargetSize() {
-        return this.m_Size;
+        return this.m_TargetSize;
     }
-    public String populationSizeTipText() {
+    public String targetSizeTipText() {
-        return "The population size.";
+        return "The initial population size.";
     }   
     public AbstractEAIndividual getEAIndividual(int i) {
         return (AbstractEAIndividual)this.get(i);
@@ -1255,7 +1253,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	double d;
     	double[] res = new double[3];
     	
-    	double meanDist = 0.;
+    	double distSum = 0.;
     	double maxDist = Double.MIN_VALUE;
     	double minDist = Double.MAX_VALUE;
     	
@@ -1264,14 +1262,14 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         		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;
+                distSum += d;
                 if (d < minDist) minDist = d;
                 if (d > maxDist) maxDist = d;
             }
         }
         res[1] = minDist;
         res[2] = maxDist;
-        if (this.size() > 1) res[0] = meanDist / (this.size() * (this.size()-1) / 2);
+        if (this.size() > 1) res[0] = distSum / (this.size() * (this.size()-1) / 2);
         else { // only one indy?
         	res[1]=0;
         	res[2]=0;
@@ -1496,14 +1494,14 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 	 * this method cannot grow, of course.
 	 */
 	public void fitToSize() {
-		if (size() != getPopulationSize()) {
+		if (size() != getTargetSize()) {
-			while (size() > getPopulationSize()) remove(size()-1);
+			while (size() > getTargetSize()) remove(size()-1);
-			if (size() < getPopulationSize()) {
+			if (size() < getTargetSize()) {
 				if (size() == 0) System.err.println("Cannot grow empty population!");
 				else {
 					int origSize=size();
 					int k=0;
-					while (size()< getPopulationSize()) {
+					while (size()< getTargetSize()) {
 						addIndividual((AbstractEAIndividual)getEAIndividual(k%origSize).clone());
 					}
 				}
@@ -1530,7 +1528,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 	 * 
 	 */
 	public void synchSize() {
-		setPopulationSize(size());
+		setTargetSize(size());
 	}
 
 	/**
@@ -1577,6 +1575,30 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 //		else return ((hashes.head().equals(evaluationTimeHashes.head())) && (hashes.tail().equals(evaluationTimeHashes.tail())) && (evaluationTimeModCount == modCount));
 //	}
 
+	/**
+	 * Return true if the targeted size of the population has been reached.
+	 */
+	public boolean targetSizeReached() {
+		return this.size()>=this.getTargetSize();
+	}
+	
+	/**
+	 * Return true if the targeted size of the population has been exceeded.
+	 */
+	public boolean targetSizeExceeded() {
+		return this.size()>this.getTargetSize();
+	}
+
+	/**
+	 * Return the number of free individual slots until the target size is reached.
+	 * Returns zero if the target size is already reached or exceeded.
+	 * 
+	 * @return
+	 */
+	public int getFreeSlots() {
+		return Math.max(0, this.getTargetSize() - this.size());
+	}
+
 //	/**
 //	 * Mark the population at the current state as evaluated. Changes to the modCount or hashes of individuals
 //	 * will invalidate the mark.

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

@@ -158,7 +158,7 @@ public abstract class AbstractDynTransProblem extends AbstractSynchronousOptimiz
 		this.prob = prob;
 		/* to get the right values for problemDimension and Range */
 		Population pop = new Population();
-		pop.setPopulationSize(1);		
+		pop.setTargetSize(1);		
 		prob.initPopulation(pop);
 		AbstractEAIndividual indy = (AbstractEAIndividual)pop.get(0);
 		if (indy instanceof InterfaceDataTypeDouble) {

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

@@ -79,7 +79,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub
 //		this.m_ProblemDimension = 2;
 		((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.m_ProblemDimension);
 		((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(makeRange());
-		for (int i = 0; i < population.getPopulationSize(); i++) {
+		for (int i = 0; i < population.getTargetSize(); i++) {
 			tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
 			tmpIndy.init(this);
 			population.add(tmpIndy);

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

@@ -182,67 +182,21 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract
         this.m_ParetoFront = new Population();
     }
     
-    /** This method evaluates a given population and set the fitness values
+    public void evaluatePopulationStart(Population population) {
-     * accordingly
+    	super.evaluatePopulationStart(population);
-     * @param population    The population that is to be evaluated.
+    	if (this.m_Show && (this.m_Plot==null)) this.initProblemFrame();
-     */
+    }
-    public void evaluate(Population population) {
-        AbstractEAIndividual    tmpIndy;
-        double[]                fitness;
     
-        evaluatePopulationStart(population);
+    public void evaluatePopulationEnd(Population population) {
+    	super.evaluatePopulationEnd(population);
+    	double[]                fitness;
     	
-        
+    	for (int i = 0; i < population.size(); i++) {
-        if (this.parallelthreads > 1) {
+        	// check and update border if necessary
-        	Vector<AbstractEAIndividual> queue = new Vector<AbstractEAIndividual>();
+        	AbstractEAIndividual tmpIndy = (AbstractEAIndividual) population.get(i);
-        	Vector<AbstractEAIndividual> finished =  new Vector<AbstractEAIndividual>();
-        	queue.addAll(population);
-       /* 	Semaphore available=new Semaphore(parallelthreads);
-        	while (finished.size() < population.size()) {
-        		try {
-        			available.acquire();
-				} catch (InterruptedException e) {
-					// TODO Auto-generated catch block
-					e.printStackTrace();
-				}
-        		if ((population.size()-(queue.size() + finished.size())) < parallelthreads) {
-        			if (queue.size() > 0) {
-	        			AbstractEAIndividual tmpindy = queue.get(0);
-		        		queue.remove(0);
-		        		tmpindy.resetConstraintViolation();
-		        		MultiObjectiveEvalThread evalthread = new MultiObjectiveEvalThread(this,tmpindy,finished,population,available);
-		        		evalthread.start();
-		        		
-        			} 
-        		}
-        	}
-        	*/
-        	Semaphore sema=new Semaphore(0);
-        	ExecutorService pool = Executors.newFixedThreadPool(parallelthreads);     
-        	for (int i = 0; i < population.size(); i++){
-        		AbstractEAIndividual tmpindy =  (AbstractEAIndividual)population.get(i);   		
-        		tmpindy.resetConstraintViolation();
-        		EvalThread evalthread = new EvalThread(this,tmpindy,finished,population,sema);
-        		pool.execute(evalthread);
-
-        	}
-        	try {
-        		sema.acquire(population.size());
-        	} catch (InterruptedException e) {
-        		// TODO Auto-generated catch block
-        		e.printStackTrace();
-        	}
-        	pool.shutdownNow();
-        }else {
-        // first evaluate the population
-        for (int i = 0; i < population.size(); i++) {
-            tmpIndy = (AbstractEAIndividual) population.get(i);
-            tmpIndy.resetConstraintViolation();
-            this.evaluate(tmpIndy);
             fitness = tmpIndy.getFitness();
             // check and update border if necessary
-            if (m_Border == null)
+            if (m_Border == null) this.m_Border = new double[fitness.length][2];
-            	this.m_Border = new double[fitness.length][2];
             else if (fitness.length != this.m_Border.length) {
                 //System.out.println("AbstractMOOptimizationProblem: Warning fitness.length("+fitness.length+") doesn't fit border.length("+this.m_Border.length+")");
                 //System.out.println("Resetting the border!");
@@ -257,17 +211,8 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract
                 this.m_Border[j][0] = Math.min(this.m_Border[j][0], fitness[j]);
                 this.m_Border[j][1] = Math.max(this.m_Border[j][1], fitness[j]);
             }
-            population.incrFunctionCalls();
         }
-        }
-        evaluatePopulationEnd(population); // refactored by MK
-    }
     	
-    public void evaluatePopulationStart(Population population) {
-    	if (this.m_Show && (this.m_Plot==null)) this.initProblemFrame();
-    }
-    
-    public void evaluatePopulationEnd(Population population) {
         // So what is the problem:
         // on the one hand i want to log the pareto-front in the
         // multiobjective case

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

@@ -1,12 +1,11 @@
 package eva2.server.go.problems;
 
 import java.awt.BorderLayout;
-import java.util.Vector;
 import java.util.concurrent.ExecutorService;
 import java.util.concurrent.Executors;
 import java.util.concurrent.Semaphore;
 import java.io.Serializable;
-
+import java.util.Vector;
 
 import javax.swing.JComponent;
 import javax.swing.JPanel;
@@ -42,14 +41,14 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
 	class EvalThread extends Thread {
 		AbstractOptimizationProblem prob;
 		AbstractEAIndividual ind;
-		Vector<AbstractEAIndividual> resultrep;
+//		Vector<AbstractEAIndividual> resultrep;
 		Population pop;
 		Semaphore m_Semaphore;
 		
-		public EvalThread(AbstractOptimizationProblem prob, AbstractEAIndividual ind, Vector<AbstractEAIndividual> resultrep, Population pop,Semaphore sema) {
+		public EvalThread(AbstractOptimizationProblem prob, AbstractEAIndividual ind, Population pop,Semaphore sema) {
 			this.ind = ind;
 			this.prob = prob;
-			this.resultrep = resultrep;
+//			this.resultrep = resultrep;
 			this.pop = pop;
 			this.m_Semaphore=sema;
 		}
@@ -58,7 +57,7 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
 //			System.out.println("Running ET " + this);
 //			long time=System.nanoTime();
 			prob.evaluate(ind);
-			resultrep.add(ind);
+//			resultrep.add(ind);
 			pop.incrFunctionCalls();
 			m_Semaphore.release();
 //			long duration=System.nanoTime()-time;
@@ -110,7 +109,7 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
         
         if (this.parallelthreads > 1) {
         	Vector<AbstractEAIndividual> queue = new Vector<AbstractEAIndividual>(population.size());
-        	Vector<AbstractEAIndividual> finished =  new Vector<AbstractEAIndividual>(population.size());
+//        	Vector<AbstractEAIndividual> finished =  new Vector<AbstractEAIndividual>(population.size());
         	/* 	queue.addAll(population);
         	Semaphore sema=new Semaphore(parallelthreads);
         	while (finished.size() < population.size()) {
@@ -133,18 +132,18 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
         	}*/
         	Semaphore sema=new Semaphore(0);
         	ExecutorService pool = Executors.newFixedThreadPool(parallelthreads);
-        	for (int i = 0; i < population.size(); i++){
+        	int cntIndies=0;    
-        		AbstractEAIndividual tmpindy =  (AbstractEAIndividual)population.get(i);   		
+        	for (; cntIndies < population.size(); cntIndies++){
+        		AbstractEAIndividual tmpindy =  (AbstractEAIndividual)population.get(cntIndies);   		
         		tmpindy.resetConstraintViolation();
-        		EvalThread evalthread = new EvalThread(this,tmpindy,finished,population,sema);
+        		EvalThread evalthread = new EvalThread(this,tmpindy,population,sema);
         		pool.execute(evalthread);
-
         	}
         	try {
-        		sema.acquire(population.size());
+        		sema.acquire(cntIndies);
         	} catch (InterruptedException e) {
-        		// TODO Auto-generated catch block
         		e.printStackTrace();
+        		throw new RuntimeException("Threading error in AbstractOptimizationProblem: " + e.getMessage());
         	}
         	pool.shutdownNow();
         } else {
@@ -185,6 +184,21 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
      */
     public abstract void evaluate(AbstractEAIndividual individual);
 
+    
+	public static void defaultInitPopulation(Population population, AbstractEAIndividual template, InterfaceOptimizationProblem prob) {
+        AbstractEAIndividual tmpIndy;
+        population.clear();
+        
+        for (int i = 0; i < population.getTargetSize(); i++) {
+            tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)template).clone();
+            tmpIndy.init(prob);
+            population.add(tmpIndy);
+        }
+        // population init must be last
+        // it set's fitcalls and generation to zero
+        population.init();
+	}
+	
     /******************** Some output methods *******************************************/
 
     /** This method allows you to output a string that describes a found solution

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

@@ -55,31 +55,20 @@ public abstract class AbstractProblemBinary extends AbstractOptimizationProblem
 	 */
 	public abstract int getProblemDimension();
 
-	/**
+//	/**
-	 * Initialize a single individual with index k in the
+//	 * Initialize a single individual with index k in the
-	 * initPopulation cycle.
+//	 * initPopulation cycle.
-	 * @param k
+//	 * @param k
-	 * @param indy
+//	 * @param indy
-	 */
+//	 */
-    protected void initIndy(int k, AbstractEAIndividual indy) {
+//    protected void initIndy(int k, AbstractEAIndividual indy) {
-    	indy.init(this);
+//    	indy.init(this);
-    }
+//    }
 
 	@Override
 	public void initPopulation(Population population) {
-        AbstractEAIndividual tmpIndy;
-        population.clear();
-
         ((InterfaceDataTypeBinary)this.m_Template).setBinaryDataLength(this.getProblemDimension());
-
+        AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this);
-        for (int i = 0; i < population.getPopulationSize(); i++) {
-            tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
-            initIndy(i, tmpIndy);
-            population.add(tmpIndy);
-        }
-        // population init must be last
-        // it set's fitcalls and generation to zero
-        population.init();
 	}
 	
 	@Override

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

@@ -155,18 +155,8 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 	
 	@Override
 	public void initPopulation(Population population) {
-        AbstractEAIndividual tmpIndy;
-        population.clear();
         initTemplate();
-        
+        AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this);
-        for (int i = 0; i < population.getPopulationSize(); i++) {
-            tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
-            tmpIndy.init(this);
-            population.add(tmpIndy);
-        }
-        // population init must be last
-        // it set's fitcalls and generation to zero
-        population.init();
 	}
 	
 	/**

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

@@ -0,0 +1,99 @@
+package eva2.server.go.problems;
+
+/**
+ * Created by IntelliJ IDEA.
+ * User: streiche
+ * Date: 24.03.2003
+ * Time: 17:58:55
+ * To change this template use Options | File Templates.
+ */
+public abstract class AbstractProblemDoubleOffset extends AbstractProblemDouble implements Interface2DBorderProblem {
+
+//	protected AbstractEAIndividual      m_OverallBest       = null;
+    protected int                       m_ProblemDimension  = 10;
+    protected double                    m_XOffSet           = 0.0;
+    protected double                    m_YOffSet           = 0.0;
+//    protected boolean                   m_UseTestConstraint = false;
+
+    public AbstractProblemDoubleOffset() {
+    	super();
+    	setDefaultRange(10);
+    }
+    
+    public AbstractProblemDoubleOffset(AbstractProblemDoubleOffset b) {
+    	super();
+    	super.cloneObjects(b);
+    	this.m_ProblemDimension = b.m_ProblemDimension;
+    	this.m_XOffSet          = b.m_XOffSet;
+    	this.m_YOffSet          = b.m_YOffSet;
+//    	this.m_UseTestConstraint = b.m_UseTestConstraint;
+    }
+    
+    public AbstractProblemDoubleOffset(int dim) {
+    	this();
+    	setProblemDimension(dim);
+    }
+    
+    public AbstractProblemDoubleOffset(int dim, double defRange) {
+    	this(dim);
+    	setDefaultRange(defRange);
+    }
+
+    /** This method inits the Problem to log multiruns
+     */
+    public void initProblem() {
+//        this.m_OverallBest = null;
+    	initTemplate();
+    }
+
+/**********************************************************************************************************************
+ * These are for GUI
+ */
+    /** This method allows the CommonJavaObjectEditorPanel to read the
+     * name to the current object.
+     * @return The name.
+     */
+    public String getName() {
+        return "Double-valued-Problem+Offsets";
+    }
+
+    /** This method allows you to set/get an offset for decision variables.
+     * @param XOffSet     The offset for the decision variables.
+     */
+    public void setXOffSet(double XOffSet) {
+        this.m_XOffSet = XOffSet;
+    }
+    public double getXOffSet() {
+        return this.m_XOffSet;
+    }
+    public String xOffSetTipText() {
+        return "Choose an offset for the decision variable.";
+    }
+
+    /** This method allows you to set/get the offset for the
+     * objective value.
+     * @param YOffSet     The offset for the objective value.
+     */
+    public void setYOffSet(double YOffSet) {
+        this.m_YOffSet = YOffSet;
+    }
+    public double getYOffSet() {
+        return this.m_YOffSet;
+    }
+    public String yOffSetTipText() {
+        return "Choose an offset for the objective value.";
+    }
+    /** Length of the x vector at is to be optimized
+     * @param t Length of the x vector at is to be optimized
+     */
+    public void setProblemDimension(int t) {
+        this.m_ProblemDimension = t;
+    }
+    public int getProblemDimension() {
+        return this.m_ProblemDimension;
+    }
+    public String problemDimensionTipText() {
+        return "Length of the x vector to be optimized.";
+    }
+}
+

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

@@ -105,23 +105,12 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem implemen
      * @param population    The populations that is to be inited
      */
     public void initPopulation(Population population) {
-        AbstractEAIndividual tmpIndy;
-
         this.m_OverallBest = null;
 
-        population.clear();
-
         ((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.m_ProblemDimension);
         ((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(makeRange());
 
-        for (int i = 0; i < population.getPopulationSize(); i++) {
+        AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this);
-            tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
-            tmpIndy.init(this);
-            population.add(tmpIndy);
-        }
-        // population init must be last
-        // it set's fitcalls and generation to zero
-        population.init();
     }
     
     public double[][] makeRange() {

src/eva2/server/go/problems/F10Problem.java

@@ -9,7 +9,7 @@ import eva2.server.go.individuals.ESIndividualDoubleData;
  * Time: 14:33:07
  * To change this template use File | Settings | File Templates.
  */
-public class F10Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
+public class F10Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable {
 
     private double m_D          = 1.5;
     private double m_b          = 2.3;

src/eva2/server/go/problems/F11Problem.java

@@ -12,7 +12,7 @@ import eva2.server.go.populations.Population;
  * Time: 14:59:23
  * To change this template use File | Settings | File Templates.
  */
-public class F11Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
+public class F11Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable {
 
     private double m_D          = 4000;
 

src/eva2/server/go/problems/F12Problem.java

@@ -12,7 +12,7 @@ import eva2.server.go.populations.Population;
  * Time: 14:59:33
  * To change this template use File | Settings | File Templates.
  */
-public class F12Problem extends F1Problem implements java.io.Serializable {
+public class F12Problem extends AbstractProblemDoubleOffset implements java.io.Serializable {
 	private final static double f12range = 5.;
 
     public F12Problem() {

src/eva2/server/go/problems/F13Problem.java

@@ -6,7 +6,7 @@ import eva2.server.go.individuals.ESIndividualDoubleData;
  * Schwefels sine root function (1981) with a minimum at 420.9687^n of value 0.
  * Function f(x) = (418.9829 * n) - sum_n(x_i * sin(sqrt(abs(x_i)))) + (418.9829 * n);
  */
-public class F13Problem extends F1Problem implements InterfaceMultimodalProblem {
+public class F13Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem {
 
     public F13Problem() {
         this.m_Template         = new ESIndividualDoubleData();

src/eva2/server/go/problems/F14Problem.java

@@ -9,7 +9,7 @@ import eva2.server.go.individuals.ESIndividualDoubleData;
  * Time: 19:15:03
  * To change this template use File | Settings | File Templates.
  */
-public class F14Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
+public class F14Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable {
 	double rotation = 0.;
 	double rotationDX = 2;
 	

src/eva2/server/go/problems/F1Problem.java

@@ -1,7 +1,5 @@
 package eva2.server.go.problems;
 
-import eva2.server.go.individuals.AbstractEAIndividual;
-import eva2.server.go.operators.constraint.GenericConstraint;
 import eva2.server.go.strategies.InterfaceOptimizer;
 
 /**
@@ -11,18 +9,7 @@ import eva2.server.go.strategies.InterfaceOptimizer;
  * Time: 17:58:55
  * To change this template use Options | File Templates.
  */
-public class F1Problem extends AbstractProblemDouble implements Interface2DBorderProblem, java.io.Serializable {
+public class F1Problem extends AbstractProblemDoubleOffset implements Interface2DBorderProblem, java.io.Serializable, InterfaceFirstOrderDerivableProblem {
-
-    /**
-	 * 
-	 */
-	private static final long serialVersionUID = 4870484001737601464L;
-//	protected AbstractEAIndividual      m_OverallBest       = null;
-    protected int                       m_ProblemDimension  = 10;
-    protected double                    m_XOffSet           = 0.0;
-    protected double                    m_YOffSet           = 0.0;
-//    protected boolean                   m_UseTestConstraint = false;
-
     public F1Problem() {
     	super();
     	setDefaultRange(10);
@@ -31,15 +18,10 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
     public F1Problem(F1Problem b) {
     	super();
     	super.cloneObjects(b);
-    	this.m_ProblemDimension = b.m_ProblemDimension;
-    	this.m_XOffSet          = b.m_XOffSet;
-    	this.m_YOffSet          = b.m_YOffSet;
-//    	this.m_UseTestConstraint = b.m_UseTestConstraint;
     }
     
     public F1Problem(int dim) {
-    	this();
+    	super(dim);
-    	setProblemDimension(dim);
     }
     
     public F1Problem(int dim, double defRange) {
@@ -54,13 +36,6 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
         return (Object) new F1Problem(this);
     }
 
-    /** This method inits the Problem to log multiruns
-     */
-    public void initProblem() {
-//        this.m_OverallBest = null;
-    	initTemplate();
-    }
-
     /** Ths method allows you to evaluate a simple bit string to determine the fitness
      * @param x     The n-dimensional input vector
      * @return  The m-dimensional output vector.
@@ -110,54 +85,12 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
         return "F1: multidimensional parabola problem";
     }
 
-    /** This method allows you to set/get an offset for decision variables.
+	public double[] getFirstOrderGradients(double[] x) {
-     * @param XOffSet     The offset for the decision variables.
+		// first order partial derivation in direction x_i is 2*x_i
-     */
+		double[] grads=new double[x.length];
-    public void setXOffSet(double XOffSet) {
+		for (int i=0; i<x.length; i++) {
-        this.m_XOffSet = XOffSet;
+			grads[i]=(2.*(x[i] - this.m_XOffSet));
-    }
+		}
-    public double getXOffSet() {
+		return grads;
-        return this.m_XOffSet;
+	}
-    }
-    public String xOffSetTipText() {
-        return "Choose an offset for the decision variable.";
-    }
-
-    /** This method allows you to set/get the offset for the
-     * objective value.
-     * @param YOffSet     The offset for the objective value.
-     */
-    public void setYOffSet(double YOffSet) {
-        this.m_YOffSet = YOffSet;
-    }
-    public double getYOffSet() {
-        return this.m_YOffSet;
-    }
-    public String yOffSetTipText() {
-        return "Choose an offset for the objective value.";
-    }
-    /** Length of the x vector at is to be optimized
-     * @param t Length of the x vector at is to be optimized
-     */
-    public void setProblemDimension(int t) {
-        this.m_ProblemDimension = t;
-    }
-    public int getProblemDimension() {
-        return this.m_ProblemDimension;
-    }
-    public String problemDimensionTipText() {
-        return "Length of the x vector to be optimized.";
-    }
-//    /** This method allows you to toggle the application of a simple test constraint.
-//     * @param b     The mode for the test constraint
-//     */
-//    public void setUseTestConstraint(boolean b) {
-//        this.m_UseTestConstraint = b;
-//    }
-//    public boolean getUseTestConstraint() {
-//        return this.m_UseTestConstraint;
-//    }
-//    public String useTestConstraintTipText() {
-//        return "Just a simple test constraint of x[0] >= 1.";
-//    }
 }

src/eva2/server/go/problems/F2Problem.java

@@ -9,7 +9,7 @@ import eva2.server.go.individuals.ESIndividualDoubleData;
  * Time: 19:03:09
  * To change this template use File | Settings | File Templates.
  */
-public class F2Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
+public class F2Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable, InterfaceFirstOrderDerivableProblem {
 
     public F2Problem() {
         this.m_Template         = new ESIndividualDoubleData();
@@ -45,6 +45,21 @@ public class F2Problem extends F1Problem implements InterfaceMultimodalProblem,
         return result;
     }
     
+	public double[] getFirstOrderGradients(double[] x) {
+        int dim = x.length;
+        double[] result = new double[dim];
+        double xi, xii;
+        
+        for (int i = 0; i < dim-1; i++) {
+        	xi=x[i]-m_XOffSet;
+        	xii=x[i+1]-m_XOffSet;
+        	
+            result[i] += (-200.*xii+200.*xi+2.*xi-2);
+            result[i+1] += (200.*xii-200*xi);
+        }
+        return result;
+	}
+	
     /** This method returns a string describing the optimization problem.
      * @return The description.
      */

src/eva2/server/go/problems/F3Problem.java

@@ -10,7 +10,7 @@ import eva2.server.go.individuals.ESIndividualDoubleData;
  * Time: 19:15:03
  * To change this template use File | Settings | File Templates.
  */
-public class F3Problem extends F1Problem implements java.io.Serializable {
+public class F3Problem extends AbstractProblemDoubleOffset implements java.io.Serializable {
 
     public F3Problem() {
         this.m_Template         = new ESIndividualDoubleData();

src/eva2/server/go/problems/F4Problem.java

@@ -13,7 +13,7 @@ import eva2.tools.math.RNG;
  * Time: 19:28:33
  * To change this template use File | Settings | File Templates.
  */
-public class F4Problem extends F1Problem implements java.io.Serializable {
+public class F4Problem extends AbstractProblemDoubleOffset implements java.io.Serializable {
 	final static double f4range = 1.28;
 	
     public F4Problem() {

src/eva2/server/go/problems/F5Problem.java

@@ -13,7 +13,7 @@ import eva2.tools.math.RNG;
  * Time: 19:30:52
  * To change this template use File | Settings | File Templates.
  */
-public class F5Problem extends F1Problem implements java.io.Serializable {
+public class F5Problem extends AbstractProblemDoubleOffset implements java.io.Serializable {
 	final static double f5range = 65.536;
 	
     public F5Problem() {

src/eva2/server/go/problems/F6Problem.java

@@ -11,7 +11,7 @@ import eva2.tools.math.Jama.Matrix;
  * Time: 13:09:36
  * To change this template use File | Settings | File Templates.
  */
-public class F6Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
+public class F6Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable {
 
 	private boolean 		doRotation = false;
     private double          m_A     = 10;

src/eva2/server/go/problems/F7Problem.java

@@ -15,7 +15,7 @@ import eva2.tools.math.RNG;
  * Time: 13:23:43
  * To change this template use File | Settings | File Templates.
  */
-public class F7Problem extends F1Problem implements java.io.Serializable {
+public class F7Problem extends AbstractProblemDoubleOffset implements java.io.Serializable {
 
     private double              m_t         = 250;
     private double              m_Change    = 4;

src/eva2/server/go/problems/F8Problem.java

@@ -8,7 +8,7 @@ package eva2.server.go.problems;
  * Time: 19:40:28
  * To change this template use File | Settings | File Templates.
  */
-public class F8Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
+public class F8Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable {
 
     private double      a = 20;
     private double      b = 0.2;

src/eva2/server/go/problems/F9Problem.java

@@ -3,7 +3,7 @@ package eva2.server.go.problems;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.ESIndividualDoubleData;
 
-public class F9Problem extends F1Problem implements java.io.Serializable {
+public class F9Problem extends AbstractProblemDoubleOffset implements java.io.Serializable {
 
     public F9Problem() {
         this.m_Template         = new ESIndividualDoubleData();

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

@@ -247,13 +247,8 @@ public class FLensProblem extends AbstractOptimizationProblem implements Interfa
 	 * @param population    The populations that is to be inited
 	 */
 	public void initPopulation(Population population) {
-		AbstractEAIndividual tmpIndy;
-
 		this.m_OverallBest = null;
-		population.clear();
-
 		((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.m_ProblemDimension);
-
         // set the range
         double[][] range = new double[this.m_ProblemDimension][2];
         for (int i = 0; i < range.length; i++) {
@@ -262,13 +257,8 @@ public class FLensProblem extends AbstractOptimizationProblem implements Interfa
         }
        ((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(range);
 
-		for (int i = 0; i < population.getPopulationSize(); i++) {
+		AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this);
-			tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
-			tmpIndy.init(this);
-			population.add(tmpIndy);
-		}
         if (this.m_Show) this.initProblemFrame();
-		population.init();
 	}
 
 	public void evaluatePopulationEnd(Population pop) {

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

@@ -1,18 +1,19 @@
 package eva2.server.go.problems;
 
 /**
- * <p>Title: EvA2</p>
+ * An interface for first-order derivable problems which can be used for gradient descent.
- * <p>Description: </p>
+ * 
- * <p>Copyright: Copyright (c) 2003</p>
+ * @author hplanatscher, mkron
- * <p>Company: </p>
+ *
- * @author not attributable
- * @version 1.0
  */
-
 public interface InterfaceFirstOrderDerivableProblem {
 
+	/**
+	 * Calculate the first order gradients of this problem.
+	 * @param x
+	 * @return the first order gradients of this problem
+	 */
   public double[] getFirstOrderGradients(double[] x);
-  public double getFirstOrderGradient(int paramindex,double[] x);
+//  public double getFirstOrderGradient(int paramindex,double[] x);
-
 
 }

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

@@ -9,7 +9,7 @@ import eva2.server.go.individuals.codings.gp.GPArea;
  * Time: 14:43:03
  * To change this template use Options | File Templates.
  */
-public interface InterfaceProgramProblem {
+public interface InterfaceProgramProblem extends InterfaceOptimizationProblem {
 
     /** This method allows a GP program to sense the environment, e.g.
      * input values, current time etc

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

@@ -437,19 +437,8 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf
 
 	@Override
 	public void initPopulation(Population population) {
-		AbstractEAIndividual tmpIndy;
-		population.clear();
 		initTemplate();
-
+		AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this);
-		for (int i = 0; i < population.getPopulationSize(); i++) {
-			tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
-			tmpIndy.init(this);
-//			System.err.println("initPopulation: " + AbstractEAIndividual.getDefaultDataString(tmpIndy) + " , " + tmpIndy.getStringRepresentation());
-			population.add(tmpIndy);
-		}
-		// population init must be last
-		// it set's fitcalls and generation to zero
-		population.init();
 	}
 
 	public String getStringRepresentationForProblem(InterfaceOptimizer opt) {

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

@@ -143,9 +143,8 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
      * @param population    The populations that is to be inited
      */
     public static void initPopulation(Population pop, InterfaceProgramProblem prob, boolean useInnerConsts, int numConsts) {
-        AbstractEAIndividual tmpIndy, template;
+        AbstractEAIndividual template;
 
-        pop.clear();
         template = ((AbstractOptimizationProblem)prob).getIndividualTemplate();
         GPArea tmpArea[]    = new GPArea[1];
         tmpArea[0]          = prob.getArea();
@@ -154,14 +153,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
         if ((template instanceof GAPIndividualProgramData) && useInnerConsts) {
             ((GAPIndividualProgramData)template).setDoubleDataLength(numConsts);
         }
-        for (int i = 0; i < pop.getPopulationSize(); i++) {
+        AbstractOptimizationProblem.defaultInitPopulation(pop, template, prob);
-            tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)template).clone();
-            tmpIndy.init((AbstractOptimizationProblem)prob);
-            pop.add(tmpIndy);
-        }
-        // population init must be last
-        // it set's fitcalls and generation to zero
-        pop.init();
     }
     
     /** This method init the enviroment panel if necessary.
@@ -238,6 +230,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements
         if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) {
             this.m_OverallBest = (AbstractEAIndividual)individual.clone();
             if (this.m_Show) {
+            	if (m_Plot==null) this.initEnvironmentPanel();
                 this.m_Plot.clearAll();
                 program     = ((InterfaceDataTypeProgram)this.m_OverallBest).getProgramData()[0];
                 for (double i = 0; i < this.m_NumberOfCheckPoints; i++) {

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

@@ -101,20 +101,8 @@ public class SimpleProblemWrapper extends AbstractOptimizationProblem {
 	
 	@Override
 	public void initPopulation(Population population) {
-        AbstractEAIndividual tmpIndy;
-        population.clear();
-
         initTemplate();
-
+        AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this);
-        for (int i = 0; i < population.getPopulationSize(); i++) {
-            tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
-            tmpIndy.init(this);
-            population.add(tmpIndy);
-        }
-        // population init must be last
-        // it set's fitcalls and generation to zero
-        population.init();
-		
 	}
 
 	@Override

src/eva2/server/go/problems/TF1Problem.java

@@ -90,23 +90,14 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem
      * @param population    The populations that is to be inited
      */
     public void initPopulation(Population population) {
-        AbstractEAIndividual tmpIndy;
         this.m_ParetoFront = new Population();
 
         double[][] newRange = makeRange();
 
-        population.clear();
-
         ((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.m_ProblemDimension);
         ((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(newRange);
-        for (int i = 0; i < population.getPopulationSize(); i++) {
+        
-            tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone();
+        AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this);
-            tmpIndy.init(this);
-            population.add(tmpIndy);
-        }
-        // population init must be last
-        // it set's fitcalls and generation to zero
-        population.init();
     }
     
 	protected double[][] makeRange() {

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

@@ -118,7 +118,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
         //System.out.println("Population:"+this.m_Population.getSolutionRepresentationFor());
         this.m_PopulSelectionOperator.prepareSelection(this.m_Population);
         this.m_RecombSelectionOperator.prepareSelection(this.m_Population);
-        parents     = this.m_PopulSelectionOperator.selectFrom(this.m_Population, this.m_Population.getPopulationSize());
+        parents     = this.m_PopulSelectionOperator.selectFrom(this.m_Population, this.m_Population.getTargetSize());
         //System.out.println("Parents:"+parents.getSolutionRepresentationFor());
 
         for (int i = 0; i < parents.size(); i++) {
@@ -169,7 +169,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
 
         this.m_Population.clear();
         this.m_Population.add(best);
-        for (int i = 1; i < this.m_Population.getPopulationSize(); i++) {
+        for (int i = 1; i < this.m_Population.getTargetSize(); i++) {
             mutant      = (InterfaceGAIndividual)best.clone();
             tmpBitSet   = mutant.getBGenotype();
             for (int j = 0; j < mutant.getGenotypeLength(); j++) {
@@ -207,7 +207,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
             nextGeneration.addPopulation(this.m_Population);
 //            this.m_NormationOperator.computeSelectionProbability(nextGeneration, "Fitness");
             this.m_PopulSelectionOperator.prepareSelection(this.m_Population);
-            tmp = this.m_PopulSelectionOperator.selectFrom(nextGeneration, this.m_Population.getPopulationSize());
+            tmp = this.m_PopulSelectionOperator.selectFrom(nextGeneration, this.m_Population.getTargetSize());
             nextGeneration.clear();
             nextGeneration.addPopulation(tmp);
             this.m_Population = nextGeneration;

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

@@ -111,7 +111,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         this.m_Optimizer.addPopulationChangedEventListener(this);
         this.m_Undifferentiated = new Population();
         this.m_Undifferentiated.setUseHistory(true);
-        this.m_Undifferentiated.setPopulationSize(this.m_PopulationSize);
+        this.m_Undifferentiated.setTargetSize(this.m_PopulationSize);
         this.m_Species = new ArrayList<Population>();
         this.m_Problem.initPopulation(this.m_Undifferentiated);
         this.evaluatePopulation(this.m_Undifferentiated);
@@ -132,7 +132,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         pop.setUseHistory(true);
         this.m_Undifferentiated = (Population)pop.clone();
         if (reset) this.m_Undifferentiated.init();
-        this.m_Undifferentiated.setPopulationSize(this.m_PopulationSize);
+        this.m_Undifferentiated.setTargetSize(this.m_PopulationSize);
         this.m_Species = new ArrayList<Population>();
         m_Archive						= new Population();
         convergedCnt = 0;
@@ -273,7 +273,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     private Population initializeIndividuals(int n) {
         Population result = new Population();
         result.setUseHistory(true);
-        result.setPopulationSize(n);
+        result.setTargetSize(n);
         //@todo: crossover between species is to be impelemented
         this.m_Problem.initPopulation(result);
         this.m_Problem.evaluate(result);
@@ -334,18 +334,18 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     		if (TRACE) System.out.println("mu: "+es.getMu() + " / lambda: " + es.getLambda());
     	}
     	if (TRACE) {
-    		System.out.println("Bef: spec size: " + species.size() + ", says its " + species.getPopulationSize());
+    		System.out.println("Bef: spec size: " + species.size() + ", target size " + species.getTargetSize());
     		System.out.println("Best bef: " + BeanInspector.toString(m_Optimizer.getPopulation().getBestFitness()));
     	}
     	this.m_Optimizer.optimize();
     	Population retPop =  m_Optimizer.getPopulation();
 		if (TRACE) {
-			System.out.println("Aft: spec size: " + retPop.size() + ", says its " + retPop.getPopulationSize());
+			System.out.println("Aft: spec size: " + retPop.size() + ", target size " + retPop.getTargetSize());
 			System.out.println("Best aft: " + BeanInspector.toString(retPop.getBestFitness()));
 		}
-    	if (retPop.size() != retPop.getPopulationSize()) {
+    	if (retPop.size() != retPop.getTargetSize()) {
-    		if (TRACE) System.out.println("correcting popsize after opt: " + retPop.getPopulationSize() + " to " + retPop.size());
+    		if (TRACE) System.out.println("correcting popsize after opt: " + retPop.getTargetSize() + " to " + retPop.size());
-    		retPop.setPopulationSize(retPop.size());
+    		retPop.synchSize();
     	}
     	
 //    	if (useDistraction) {
@@ -376,7 +376,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         }
 
         // optimize D_0
-        this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.size());
+        this.m_Undifferentiated.synchSize();
         if (isActive(m_Undifferentiated)) {
             this.capMutationRate(this.m_Undifferentiated, 0); // MK TODO this sets mutation rate to 0! why?
             m_Undifferentiated = optimizeSpecies(m_Undifferentiated);
@@ -398,7 +398,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
             if (TRACE) System.out.println("-Deme " + i + " size: " + ((Population)this.m_Species.get(i)).size());
             curSpecies = ((Population)this.m_Species.get(i));
             curSpecies.SetFunctionCalls(0);
-            curSpecies.setPopulationSize(curSpecies.size());
+            curSpecies.synchSize();
             if (isActive(curSpecies)) {
                 if ((haltingWindow > 0) && (this.testSpeciesForConvergence(curSpecies))) {
 ///////////////////////////////////////////// Halting Window /////////////////////////////////////////////////
@@ -431,7 +431,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                     // reinit the surplus individuals and add these new individuals to undifferentiated
                     m_Undifferentiated.addPopulation(this.initializeIndividuals(reinitCount));
                     m_Undifferentiated.incrFunctionCallsBy(reinitCount);
-                    m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()+reinitCount);
+                    m_Undifferentiated.setTargetSize(this.m_Undifferentiated.getTargetSize()+reinitCount);
 //                    if (this.m_Debug) {
 //                        System.out.println("Undiff.Size: " + this.m_Undifferentiated.size() +"/"+this.m_Undifferentiated.getPopulationSize());
 //                        System.out.println("Diff.Size  : " + ((Population)this.m_Species.get(i)).size() +"/"+((Population)this.m_Species.get(i)).getPopulationSize());
@@ -492,9 +492,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                         ClusterResult = this.m_CAForSpeciesDifferentation.cluster(curSpecies);
                         this.m_Undifferentiated.addPopulation(ClusterResult[0]);
 //                        ClusterResult[0].setUseHistory(true);
-                        this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize() + ClusterResult[0].size());
+                        this.m_Undifferentiated.setTargetSize(this.m_Undifferentiated.getTargetSize() + ClusterResult[0].size());
                         for (int j = 1; j < ClusterResult.length; j++) { // set up new species
-                            ClusterResult[j].setPopulationSize(ClusterResult[j].size());
+                            ClusterResult[j].synchSize();
                         	ClusterResult[j].setUseHistory(true);
 //                            if (ClusterResult.length > 2) ClusterResult[j].m_History = new ArrayList(); // mk: why min 3? Ill copy the history from the original pop for any j...
                             ClusterResult[j].m_History = (ArrayList<AbstractEAIndividual>) curSpecies.m_History.clone();
@@ -537,7 +537,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                             this.m_Undifferentiated.remove(i);
                             if (isActive(curSpecies)) {
                                 curSpecies.add(tmpIndy);
-                                this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()-1);
+                                this.m_Undifferentiated.setTargetSize(this.m_Undifferentiated.getTargetSize()-1);
                                 i--; // needs to be reduced because D0 size has decreased
                             } else {
                                 // the species is inactive and seen as converged, so reinitialize the individual
@@ -578,7 +578,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
                                 // reinitialized individuals and add them to undifferentiated
                                 this.m_Undifferentiated.addPopulation(this.initializeIndividuals(reinitCount));
 //                                m_Undifferentiated.incrFunctionCallsby(reinitCount);
-                                this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()+reinitCount);
+                                this.m_Undifferentiated.setTargetSize(this.m_Undifferentiated.getTargetSize()+reinitCount);
                             }
                         }
                     }
@@ -639,7 +639,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
      * @param spec
      */
     protected void deactivateSpecies(Population spec, AbstractEAIndividual survivor) {
-    	spec.setPopulationSize(1);
+    	spec.setTargetSize(1);
     	spec.clear();
     	spec.add(survivor);   	
     }
@@ -736,7 +736,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
         this.m_Population = (Population)m_Undifferentiated.clone();
 //        m_Population.addPopulation(this.m_Undifferentiated);
         for (int i = 0; i < this.m_Species.size(); i++) this.m_Population.addPopulation((Population)this.m_Species.get(i));
-        m_Population.setPopulationSize(m_Population.size()); // set it to true value
+        m_Population.synchSize(); // set target size to current size
         return this.m_Population;
     }
     
@@ -753,7 +753,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
     	// return inactive species
     	Population sols = (Population)m_Archive.clone();
     	sols.addPopulation(getPopulation());
-    	sols.setPopulationSize(sols.size());
+    	sols.synchSize();
     	return new SolutionSet(getPopulation(), sols);
     }
     

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

@@ -4,6 +4,7 @@ import java.io.Serializable;
 
 import eva2.gui.GenericObjectEditor;
 import eva2.server.go.InterfacePopulationChangedEventListener;
+import eva2.server.go.PopulationInterface;
 import eva2.server.go.enums.PostProcessMethod;
 import eva2.server.go.operators.mutation.MutateESFixedStepSize;
 import eva2.server.go.operators.postprocess.PostProcess;
@@ -12,6 +13,7 @@ import eva2.server.go.populations.Population;
 import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.AbstractOptimizationProblem;
 import eva2.server.go.problems.F1Problem;
+import eva2.server.go.problems.InterfaceAdditionalPopulationInformer;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
 import eva2.tools.Pair;
 
@@ -31,7 +33,7 @@ import eva2.tools.Pair;
  * @author mkron
  *
  */
-public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener, InterfaceOptimizer, Serializable {
+public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener, InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
     transient private InterfacePopulationChangedEventListener   m_Listener;
     public static final boolean TRACE = false;
     
@@ -52,6 +54,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
    	private double							reduceFactor		= 0.2;
    	private MutateESFixedStepSize				mutator = new MutateESFixedStepSize(0.1);
 	private PostProcessMethod localSearchMethod = PostProcessMethod.nelderMead;
+	private boolean doReinitialization = true;
 
 	public ClusteringHillClimbing() {
 		hideHideable();
@@ -83,6 +86,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 	 */
 	public void hideHideable() {
 		GenericObjectEditor.setHideProperty(getClass(), "population", true);
+		setDoReinitialization(isDoReinitialization());
+		setLocalSearchMethod(getLocalSearchMethod());
 	}
 	
      public void SetIdentifier(String name) {
@@ -117,7 +122,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
        	mutator = new MutateESFixedStepSize(initialStepSize);
        	archive = new Population();
        	hideHideable();
-       	m_Population.setPopulationSize(initialPopSize);
+       	m_Population.setTargetSize(initialPopSize);
         this.m_Problem.initPopulation(this.m_Population);
         m_Population.addPopulationChangedEventListener(null); // noone will be notified directly on pop changes
         this.m_Problem.evaluate(this.m_Population);
@@ -152,16 +157,26 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 		m_Population.addPopulationChangedEventListener(this);
 		m_Population.setNotifyEvalInterval(notifyGuiEvery);
 		Pair<Population, Double> popD;
-		if (TRACE) System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + (2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle)));
+		int funCallsBefore=m_Population.getFunctionCalls();
-		popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, 2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5, mutator);
+		int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle); 
-//		(m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5);
+		if (lastOverhead>0) evalsNow = (2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle));
+		else evalsNow = hcEvalCycle;
+		do {
+			if (TRACE) System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + evalsNow);
+			popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, evalsNow, 0.5, mutator);
+			//		(m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5);
+			if (popD.head().getFunctionCalls()==funCallsBefore) {
+				System.err.println("Bad case, increasing allowed evaluations!");
+				evalsNow=Math.max(evalsNow++, (int)(evalsNow*1.2));
+			}
+		} while (popD.head().getFunctionCalls()==funCallsBefore);
 		improvement = popD.tail();
 		m_Population = popD.head();
 		if (TRACE) System.out.println("num inds after clusterLS: " + m_Population.size());
 
 		popD.head().setGenerationTo(m_Population.getGeneration()+1);
 		
-		if (improvement < minImprovement) {
+		if (doReinitialization  && (improvement < minImprovement)) {
 			if (TRACE) System.out.println("improvement below " + minImprovement);
 			if ((localSearchMethod != PostProcessMethod.hillClimber) || (mutator.getSigma() < stepSizeThreshold)) { // reinit!
 				// is performed for nm and cma, and if hc has too low sigma
@@ -175,10 +190,10 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 				
 				Population tmpPop = new Population();
 				tmpPop.addPopulationChangedEventListener(null);
-				tmpPop.setPopulationSize(initialPopSize);
+				tmpPop.setTargetSize(initialPopSize);
 				this.m_Problem.initPopulation(tmpPop);
 				tmpPop.setSameParams(m_Population);
-				tmpPop.setPopulationSize(initialPopSize);
+				tmpPop.setTargetSize(initialPopSize);
 				this.m_Problem.evaluate(tmpPop);
 				
 				// reset population while keeping function calls etc.
@@ -245,7 +260,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
     public String getStringRepresentation() {
         StringBuffer sbuf = new StringBuffer("Clustering Hill Climbing");
         sbuf.append(", initial pop size: ");
-        sbuf.append(getPopulation().getPopulationSize());
+        sbuf.append(getPopulation().getTargetSize());
         sbuf.append("Optimization Problem: ");
         sbuf.append(this.m_Problem.getStringRepresentationForProblem(this));
         sbuf.append(this.m_Population.getStringRepresentation());
@@ -259,7 +274,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 	}
 
 	public String globalInfo() {
-		return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency.";
+		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.";
 	}
 	
 	/**
@@ -394,23 +410,32 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi
 
 	public void setLocalSearchMethod(PostProcessMethod localSearchMethod) {
 		this.localSearchMethod = localSearchMethod;
+		GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeInitial", localSearchMethod==PostProcessMethod.hillClimber);
+		GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeThreshold", localSearchMethod==PostProcessMethod.hillClimber);
 	}
 	
 	public String localSearchMethodTipText() {
-		return "Set the method to be used for local search";
+		return "Set the method to be used for the hill climbing as local search";
 	}
 
-//	/**
+	public String getAdditionalFileStringHeader(PopulationInterface pop) {
-//	 * @return the mutator
+		return "#Indies";
-//	 */
+	}
-//	public InterfaceMutation getMutator() {
+
-//		return mutator;
+	public String getAdditionalFileStringValue(PopulationInterface pop) {
-//	}
+		return ""+m_Population.size();
-//
+	}
-//	/**
+
-//	 * @param mutator the mutator to set
+	public boolean isDoReinitialization() {
-//	 */
+		return doReinitialization;
-//	public void setMutator(InterfaceMutation mutator) {
+	}
-//		this.mutator = mutator;
+
-//	}
+	public void setDoReinitialization(boolean doReinitialization) {
+		this.doReinitialization = doReinitialization;
+		GenericObjectEditor.setShowProperty(this.getClass(), "minImprovement", doReinitialization); 
+	}
+
+	public String doReinitializationTipText() {
+		return "Activate reinitialization if no improvement was achieved.";
+	}
 }

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

@@ -52,7 +52,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
     public static final String esMuParam = "EvolutionStrategyMuParameter";
 
     public EvolutionStrategies() {
-        this.m_Population.setPopulationSize(this.m_Lambda);
+        this.m_Population.setTargetSize(this.m_Lambda);
     }
     
     public EvolutionStrategies(int mu, int lambda, boolean usePlus) {
@@ -111,7 +111,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
      * @param reset     If true the population is reset.
      */
     public void initByPopulation(Population pop, boolean reset) {
-    	origPopSize = pop.getPopulationSize();
+    	origPopSize = pop.getTargetSize();
 //    	System.out.println("ES: orig popsize is " + origPopSize);
         this.m_Population = (Population)pop.clone();
         if (reset) {
@@ -368,9 +368,9 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
         	System.err.println("Invalid mu/lambda ratio! Setting mu=lambda="+m_Mu);
         	this.m_Lambda = this.m_Mu;
         }
-        if (this.m_UsePlusStrategy) this.m_Population.setPopulationSize(this.m_Mu + this.m_Lambda);
+        if (this.m_UsePlusStrategy) this.m_Population.setTargetSize(this.m_Mu + this.m_Lambda);
-        else this.m_Population.setPopulationSize(this.m_Lambda);
+        else this.m_Population.setTargetSize(this.m_Lambda);
-        origPopSize=m_Population.getPopulationSize();
+        origPopSize=m_Population.getTargetSize();
     }
 
     /** This method allows you to set an identifier for the algorithm

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

@@ -39,7 +39,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
 
     public FloodAlgorithm() {
         this.m_Population = new Population();
-        this.m_Population.setPopulationSize(10);
+        this.m_Population.setTargetSize(10);
     }
 
     public FloodAlgorithm(FloodAlgorithm a) {

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

@@ -110,7 +110,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
             //System.out.println("Population:"+this.m_Population.getSolutionRepresentationFor());
             this.m_ParentSelection.prepareSelection(this.m_Population);
             this.m_PartnerSelection.prepareSelection(this.m_Population);
-            parents     = this.m_ParentSelection.selectFrom(this.m_Population, this.m_Population.getPopulationSize());
+            parents     = this.m_ParentSelection.selectFrom(this.m_Population, this.m_Population.getTargetSize());
             //System.out.println("Parents:"+parents.getSolutionRepresentationFor());
 
             if (parents.getEAIndividual(0).getMutationOperator() instanceof InterfaceMutationGenerational) {
@@ -153,7 +153,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
             }
             if (this.m_Plague > 0) {
                 for (int i = 0; i < this.m_Plague; i++) if (this.m_Population.size() > 2) this.m_Population.remove(this.m_Population.getWorstEAIndividual());
-                this.m_Population.setPopulationSize(this.m_Population.size());
+                this.m_Population.setTargetSize(this.m_Population.size());
             }
             //System.out.println("Population size: " + this.m_Population.size());
 //        if (this.m_Population.getArchive() != null) {
@@ -203,7 +203,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
             String result = "";
             result += "Genetic Algorithm:\n";
             result += "Using:\n";
-            result += " Population Size    = " + this.m_Population.getPopulationSize() + "/" + this.m_Population.size() + "\n";
+            result += " Population Size    = " + this.m_Population.getTargetSize() + "/" + this.m_Population.size() + "\n";
             result += " Parent Selection   = " + this.m_ParentSelection.getClass().toString() + "\n";
             result += " Partner Selection  = " + this.m_PartnerSelection.getClass().toString() + "\n";
             result += " Number of Partners = " + this.m_NumberOfPartners + "\n";

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

@@ -1,7 +1,5 @@
 package eva2.server.go.strategies;
 
-import java.util.*;
-
 import eva2.server.go.InterfacePopulationChangedEventListener;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.InterfaceDataTypeDouble;
@@ -11,45 +9,53 @@ import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.F1Problem;
 import eva2.server.go.problems.InterfaceFirstOrderDerivableProblem;
 import eva2.server.go.problems.InterfaceOptimizationProblem;
-
+import eva2.tools.EVAERROR;
-
 
 /** 
  * A gradient descent algorithm by hannes planatscher don't expect any
  * descriptions here... *big sigh*
  * 
+ * mkron added some!
+ * 
  * @author not attributable
  * @version 1.0
  */
 
 public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Serializable {
 
-  private InterfaceOptimizationProblem m_Problem;
+	private InterfaceOptimizationProblem m_Problem;
 
-  InterfaceDataTypeDouble m_Best, m_Test;
+	InterfaceDataTypeDouble m_Best, m_Test;
-  private int           iterations = 1;
+	private int           iterations = 1;
-  private double        localnminus = 0.5;
+	private double        wDecreaseStepSize = 0.5;
-  private double        localnplus = 1.1;
+	private double        wIncreaseStepSize = 1.1;
-  boolean               recovery = false;
+	boolean               recovery = false;
-  private int           recoverylocksteps = 5;
+	private int           recoverylocksteps = 5;
-  private double        recoverythreshold = 100000;
+	private double        recoverythreshold = 100000;
-  boolean               localstepsizeadaption = true;
+	boolean               localStepSizeAdaption = true;
-  boolean               globalstepsizeadaption = true;
+	boolean               globalStepSizeAdaption = false;
-  private double        globalinitstepsize = 1;
+	private double        globalinitstepsize = 1;
-  double                globalmaxstepsize = 3.0;
+	double                globalmaxstepsize = 3.0;
-  double                globalminstepsize = 0.1;
+	double                globalminstepsize = 0.0001;
-  boolean               manhattan = false;
+	boolean               manhattan = false;
-  double                localmaxstepsize = 10;
+	double                localmaxstepsize = 10;
-  double                localminstepsize = 0.1;
+	double                localminstepsize = 0.0001;
-  private boolean       momentumterm = false;
+	private boolean       momentumterm = false;
-  transient private InterfacePopulationChangedEventListener m_Listener;
+	transient private InterfacePopulationChangedEventListener m_Listener;
-  public double         maximumabsolutechange = 0.2;
+	public double         maximumabsolutechange = 0.2;
-  Hashtable             indyhash;
+	//  Hashtable             indyhash;
 
-  // These variables are necessary for the more complex LectureGUI enviroment
+	// These variables are necessary for the more complex LectureGUI enviroment
-  transient private String m_Identifier = "";
+	transient private String m_Identifier = "";
-  private Population m_Population;
+	private Population m_Population;
-  private InterfaceDataTypeDouble InterfaceDataTypeDouble;
+
+	private static final String lockKey = "gdaLockDataKey";
+	private static final String lastFitnessKey = "gdaLastFitDataKey";
+	private static final String stepSizeKey = "gdaStepSizeDataKey";
+	private static final String wStepSizeKey = "gdaWStepSizeDataKey";
+	private static final String gradientKey = "gdaGradientDataKey";
+	private static final String changesKey = "gdaChangesDataKey";
+	private static final String oldParamsKey = "gdaOldParamsDataKey";
 
   public void initByPopulation(Population pop, boolean reset) {
     this.setPopulation((Population) pop.clone());
@@ -59,16 +65,15 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
         this.firePropertyChangedEvent(Population.nextGenerationPerformed);
     }
     //System.out.println("initByPopulation() called");
-    indyhash = new Hashtable();
+//    indyhash = new Hashtable();
   }
 
   public GradientDescentAlgorithm() {
-    indyhash = new Hashtable();
+//    indyhash = new Hashtable();
     this.m_Population = new Population();
-    this.m_Population.setPopulationSize(1);
+    this.m_Population.setTargetSize(1);
   }
 
-
   public Object clone() {
     /**@todo Implement InterfaceOptimizer method*/
     throw new java.lang.UnsupportedOperationException("Method clone() not yet implemented.");
@@ -78,15 +83,13 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
     return "GradientDescentAlgorithm";
   }
 
-
   public void init() {
     //System.out.println("init() called ");
-    indyhash = new Hashtable();
+//    indyhash = new Hashtable();
     this.m_Problem.initPopulation(this.m_Population);
     this.m_Problem.evaluate(this.m_Population);
   }
 
-
   public double signum(double val) {
     return (val < 0) ? -1 : 1;
   }
@@ -94,23 +97,23 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
   public void optimize() {
    //  System.out.println("opt. called");
     AbstractEAIndividual indy;
-      if ((this.indyhash == null) || (this.indyhash.size() <1)) init();
+//      if ((this.indyhash == null) || (this.indyhash.size() <1)) init();
 
     for (int i = 0; i < this.m_Population.size(); i++) {
       indy = ((AbstractEAIndividual)this.m_Population.get(i));
-      if (!indyhash.containsKey(indy)) {
+      if (!indy.hasData(gradientKey)) {
         //System.out.println("new indy to hash");
-        Hashtable history = new Hashtable();
+//        Hashtable history = new Hashtable();
         int[] lock = new int[((InterfaceDataTypeDouble) indy).getDoubleData().length];
         double[] wstepsize = new double[((InterfaceDataTypeDouble) indy).getDoubleData().length];
         for (int li = 0; li < lock.length; li++) lock[li] = 0;
         for (int li = 0; li < lock.length; li++) wstepsize[li] = 1.0;
         double fitness = 0;
-        history.put("lock", lock);
+        indy.putData(lockKey, lock);
-        history.put("lastfitness", new Double(fitness));
+        indy.putData(lastFitnessKey, new Double(fitness));
-        history.put("stepsize", new Double(globalinitstepsize));
+        indy.putData(stepSizeKey, new Double(globalinitstepsize));
-        history.put("wstepsize", wstepsize);
+        indy.putData(wStepSizeKey, wstepsize);
-        indyhash.put(indy, history);
+//        indyhash.put(indy, history);
       } else {
         //System.out.println("indy already in hash");
       }
@@ -121,48 +124,49 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
       indy = ((AbstractEAIndividual)this.m_Population.get(i));
       double[][] range = ((InterfaceDataTypeDouble) indy).getDoubleRange();
       double[] params = ((InterfaceDataTypeDouble) indy).getDoubleData();
+      indy.putData(oldParamsKey , params);
 
-      int[] lock = (int[]) ((Hashtable) indyhash.get(indy)).get("lock");
+      int[] lock = (int[]) indy.getData(lockKey);
-      double indystepsize = ((Double) ((Hashtable) indyhash.get(indy)).get("stepsize")).doubleValue();
+      double indystepsize = ((Double) indy.getData(stepSizeKey)).doubleValue();
    //   System.out.println("indystepsize" + indystepsize);
 
       if ((this.m_Problem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) {
-        Hashtable history = (Hashtable) indyhash.get(indy);
+//        Hashtable history = (Hashtable) indyhash.get(indy);
         for (int iterations = 0; iterations < this.iterations; iterations++) {
 
-          double[] oldgradient = (double[]) history.get("gradient");
+          double[] oldgradient = indy.hasData(gradientKey) ? (double[]) indy.getData(gradientKey) : null;
-          double[] wstepsize = (double[]) history.get("wstepsize");
+          double[] wstepsize = (double[]) indy.getData(wStepSizeKey);
           double[] oldchange = null;
 
           double[] gradient = ((InterfaceFirstOrderDerivableProblem) m_Problem).getFirstOrderGradients(params);
-          if ((oldgradient != null) && (wstepsize != null)) {
+          if ((oldgradient != null) && (wstepsize != null)) { // LOCAL adaption
             for (int li = 0; li < wstepsize.length; li++) {
               double prod = gradient[li] * oldgradient[li];
               if (prod < 0) {
-                wstepsize[li] = localnminus * wstepsize[li];
+                wstepsize[li] = wDecreaseStepSize * wstepsize[li];
               } else if (prod > 0) {
-                wstepsize[li] = localnplus * wstepsize[li];
+                wstepsize[li] = wIncreaseStepSize * wstepsize[li];
               }
               wstepsize[li] = (wstepsize[li] < localminstepsize) ? localminstepsize : wstepsize[li];
               wstepsize[li] = (wstepsize[li] > localmaxstepsize) ? localmaxstepsize : wstepsize[li];
 
-              //System.out.println("wstepsize "+ li + " " + wstepsize[li]);
+//              System.out.println("wstepsize "+ li + " " + wstepsize[li]);
             }
 
           }
           double[] newparams = new double[params.length];
-          history.put("gradient", gradient);
+          indy.putData(gradientKey, gradient);
           double[] change = new double[params.length];
-          if (history.containsKey("changes")) {
+          if (indy.hasData(changesKey)) {
-            oldchange =(double[]) history.get("changes");
+            oldchange =(double[]) indy.getData(changesKey);
           }
           boolean dograddesc = (this.momentumterm) && (oldchange != null);
 
           for (int j = 0; j < newparams.length; j++) {
             if (lock[j] == 0) {
               double tempstepsize = 1;
-              if (this.localstepsizeadaption) tempstepsize = tempstepsize *wstepsize[j];
+              if (this.localStepSizeAdaption) tempstepsize = tempstepsize * wstepsize[j];
-              if (this.globalstepsizeadaption) tempstepsize = tempstepsize *indystepsize;
+              if (this.globalStepSizeAdaption) tempstepsize = tempstepsize * indystepsize;
               double wchange = signum(tempstepsize * gradient[j]) * Math.min(maximumabsolutechange,Math.abs(tempstepsize * gradient[j])); //indystepsize * gradient[j];
               if (this.manhattan) wchange = this.signum(wchange) * tempstepsize;
               if (dograddesc)  {
@@ -181,26 +185,31 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
           }
           params = newparams;
 
-          history.put("changes", change);
+          indy.putData(changesKey, change);
 
-        }
+        } // end loop iterations
 
         ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(params);
 
+      } // end if ((this.m_Problem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) {
+      else {
+    	  String msg="Warning, problem of type InterfaceFirstOrderDerivableProblem and template of type InterfaceDataTypeDouble is required for " + this.getClass();
+    	  EVAERROR.errorMsgOnce(msg);
+    	  throw new RuntimeException(msg);
       }
-    }
+    } // for loop population size
 
     this.m_Problem.evaluate(this.m_Population);
 
     if (this.recovery) {
       for (int i = 0; i < this.m_Population.size(); i++) {
         indy = ((AbstractEAIndividual)this.m_Population.get(i));
-        Hashtable history = (Hashtable) indyhash.get(indy);
+//        Hashtable history = (Hashtable) indyhash.get(indy);
         if (indy.getFitness()[0] > recoverythreshold) {
           System.out.println("Gradient Descent: Fitness critical:" + indy.getFitness()[0]);
-          ((InterfaceDataTypeDouble) indy).SetDoublePhenotype((double[]) history.get("params"));
+          ((InterfaceDataTypeDouble) indy).SetDoublePhenotype((double[]) indy.getData(oldParamsKey));
-          double[] changes = (double[]) history.get("changes");
+          double[] changes = (double[]) indy.getData(changesKey);
-          int[] lock = (int[]) history.get("lock");
+          int[] lock = (int[]) indy.getData(lockKey);
 
           int indexmaxchange = 0;
           double maxchangeval = Double.NEGATIVE_INFINITY;
@@ -211,37 +220,37 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
             }
           }
           lock[indexmaxchange] = recoverylocksteps;
-          history.put("lock", lock);
+          indy.putData(lockKey, lock);
         } else {
         }
       }
       this.m_Problem.evaluate(this.m_Population);
     }
 
-    if (this.globalstepsizeadaption) {
+    if (this.globalStepSizeAdaption) {
 
       //System.out.println("gsa main");
       for (int i = 0; i < this.m_Population.size(); i++) {
         indy = ((AbstractEAIndividual)this.m_Population.get(i));
-        Hashtable history = (Hashtable) indyhash.get(indy);
+//        Hashtable history = (Hashtable) indyhash.get(indy);
-        if (history == null) break;
+//        if (history == null) break;
-        if (history.get("lastfitness") != null) {
+        if (indy.getData(lastFitnessKey) != null) {
-          double lastfit = ((Double) history.get("lastfitness")).doubleValue();
+          double lastfit = ((Double) indy.getData(lastFitnessKey)).doubleValue();
-          double indystepsize = ((Double) history.get("stepsize")).doubleValue();
+          double indystepsize = ((Double) indy.getData(stepSizeKey)).doubleValue();
 
-            if (lastfit < indy.getFitness()[0]) {
+            if (lastfit < indy.getFitness()[0]) { // GLOBAL adaption
-              indystepsize *= 0.5;
+              indystepsize *= wDecreaseStepSize;
             } else {
-              indystepsize *= 1.1;
+              indystepsize *= wIncreaseStepSize;
             }
 //System.out.println("newstepsize" + indystepsize);
           indystepsize = (indystepsize > globalmaxstepsize) ? globalmaxstepsize : indystepsize;
           indystepsize = (indystepsize < globalminstepsize) ? globalminstepsize : indystepsize;
-          history.put("stepsize", new Double(indystepsize));
+          indy.putData(stepSizeKey, new Double(indystepsize));
         }
 
 //System.out.println("newstepsize in bounds" + indystepsize);
-        history.put("lastfitness", new Double(indy.getFitness()[0]));
+        indy.putData(lastFitnessKey, new Double(indy.getFitness()[0]));
       }
 
     }
@@ -265,11 +274,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
   }
 
   public void setPopulation(Population pop) {
-    Hashtable newindyhash = new Hashtable();
+//    Hashtable newindyhash = new Hashtable();
-    for (int i = 0; i < pop.size(); i++) {
+//    for (int i = 0; i < pop.size(); i++) {
-      if (indyhash.contains(pop.get(i))) newindyhash.put(pop.get(i), indyhash.get(pop.get(i)));
+//      if (indyhash.contains(pop.get(i))) newindyhash.put(pop.get(i), indyhash.get(pop.get(i)));
-    }
+//    }
-    indyhash = newindyhash;
+//    indyhash = newindyhash;
     this.m_Population = pop;
   }
 
@@ -298,7 +307,6 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
     return "GradientDescentAlgorithm";
   }
 
-
   public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
     this.m_Listener = ea;
   }
@@ -324,143 +332,163 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
     }
   }
 
+  public void freeWilly() {  }
 
-
+  public String globalInfo() {
-  public void freeWilly() {
+	  return "Gradient Descent can be applied to derivable functions ("+InterfaceFirstOrderDerivableProblem.class.getSimpleName()+").";
-
+  }
+//////////////// for global adaption
+  public boolean isAdaptStepSizeGlobally() {
+    return globalStepSizeAdaption;
+  }
+  public void setAdaptStepSizeGlobally(boolean globalstepsizeadaption) {
+    this.globalStepSizeAdaption = globalstepsizeadaption;
+    if (globalstepsizeadaption && localStepSizeAdaption) setAdaptStepSizeLocally(false);
+  }
+  public String adaptStepSizeGloballyTipText() {
+	  return "Use a single step size per individual - (priority over local step size).";
   }
   
-
+  public double getGlobalMaxStepSize() {
-  public double getGlobalMaxstepsize() {
     return globalmaxstepsize;
   }
-
+  public void setGlobalMaxStepSize(double p) {
-  public void setGlobalMaxstepsize(double p) {
     globalmaxstepsize = p;
   }
+  public String globalMaxStepSizeTipText() {
+	  return "Maximum step size for global adaption.";
+  }
 
-
+  public double getGlobalMinStepSize() {
-  public double getGlobalMinstepsize() {
     return globalminstepsize;
   }
-
+  public void setGlobalMinStepSize(double p) {
-  public void setGlobalMinstepsize(double p) {
     globalminstepsize = p;
   }
-
+  public String globalMindStepSizeTipText() {
-  public int getRecoveryLocksteps() {
+	  return "Minimum step size for global adaption.";
-    return recoverylocksteps;
   }
   
-  public void setRecoveryLocksteps(int locksteps) {
+  public double getGlobalInitStepSize() {
-    this.recoverylocksteps = locksteps;
-  }
-
-  public double getGlobalInitstepsize() {
     return globalinitstepsize;
   }
-
+  public void setGlobalInitStepSize(double initstepsize) {
-  public void setGlobalInitstepsize(double initstepsize) {
     this.globalinitstepsize = initstepsize;
   }
-  public boolean isLocalStepsizeadaption() {
+  public String globalInitStepSizeTipText() {
-    return localstepsizeadaption;
+	  return "Initial step size for global adaption.";
   }
-  public void setLocalStepsizeadaption(boolean stepsizeadaption) {
+
-    this.localstepsizeadaption = stepsizeadaption;
+  //////////////// for local adaption
+  public boolean isAdaptStepSizeLocally() {
+	  return localStepSizeAdaption;
   }
+  public void setAdaptStepSizeLocally(boolean stepsizeadaption) {
+	  this.localStepSizeAdaption = stepsizeadaption;
+	  if (globalStepSizeAdaption && localStepSizeAdaption) setAdaptStepSizeGlobally(false);
+  }
+  public String adaptStepSizeLocallyTipText() {
+	  return "Use a step size parameter in any dimension.";
+  }
+
+  public double getLocalMinStepSize() {
+	  return localminstepsize;
+  }
+  public void setLocalMinStepSize(double localminstepsize) {
+	  this.localminstepsize = localminstepsize;
+  }
+  
+  public double getLocalMaxStepSize() {
+	  return localmaxstepsize;
+  }
+  public void setLocalMaxStepSize(double localmaxstepsize) {
+	  this.localmaxstepsize = localmaxstepsize;
+  }
+  
+  public void setStepSizeIncreaseFact(double nplus) {
+	  this.wIncreaseStepSize = nplus;
+  }
+  public double getStepSizeIncreaseFact() {
+	  return wIncreaseStepSize;
+  } 
+  public String stepSizeIncreaseFactTipText() {
+	  return "Factor for increasing the step size in adaption.";
+  }
+  
+  public void setStepSizeDecreaseFact(double nminus) {
+	  this.wDecreaseStepSize = nminus;
+  }
+  public double getStepSizeDecreaseFact() {
+	  return wDecreaseStepSize;
+  }
+  public String stepSizeDecreaseFactTipText() {
+	  return "Factor for decreasing the step size in adaption.";
+  }
+  
+//////////////// concerning recovery
   public boolean isRecovery() {
-    return recovery;
+	  return recovery;
   }
   public void setRecovery(boolean recovery) {
-    this.recovery = recovery;
+	  this.recovery = recovery;
   }
-  public double getLocalNplus() {
+  public int getRecoveryLocksteps() {
-    return localnplus;
+	  return recoverylocksteps;
   }
-  public double getLocalNminus() {
+  public void setRecoveryLocksteps(int locksteps) {
-    return localnminus;
+	  this.recoverylocksteps = locksteps;
   }
-  public void setLocalNplus(double nplus) {
+  public double getRecoveryThreshold() {
-    this.localnplus = nplus;
+	  return recoverythreshold;
   }
-  public void setLocalNminus(double nminus) {
+  public void setRecoveryThreshold(double recoverythreshold) {
-    this.localnminus = nminus;
+	  this.recoverythreshold = recoverythreshold;
   }
+  public String recoveryThresholdTipText() {
+	  return "If the fitness exceeds this threshold, an unstable area is assumed and one step recovered.";
+  }
+
+  
   public int getIterations() {
     return iterations;
   }
   public void setIterations(int iterations) {
     this.iterations = iterations;
   }
-  public boolean isGlobalstepsizeadaption() {
+  public String iterationsTipText() {
-    return globalstepsizeadaption;
+	  return "The number of GD-iterations per generation.";
-  }
-  public void setGlobalstepsizeadaption(boolean globalstepsizeadaption) {
-    this.globalstepsizeadaption = globalstepsizeadaption;
-  }
-  public double getLocalminstepsize() {
-    return localminstepsize;
-  }
-  public double getLocalmaxstepsize() {
-    return localmaxstepsize;
-  }
-  public void setLocalminstepsize(double localminstepsize) {
-    this.localminstepsize = localminstepsize;
-  }
-  public void setLocalmaxstepsize(double localmaxstepsize) {
-    this.localmaxstepsize = localmaxstepsize;
   }
+
+  
   public boolean isManhattan() {
     return manhattan;
   }
-
-
-
-  public boolean isMomentumTerm() {
-    return momentumterm;
-  }
-
-  public double getMomentumweigth() {
-    return momentumweigth;
-  }
-
   public void setManhattan(boolean manhattan) {
     this.manhattan = manhattan;
   }
 
-
+  public boolean isMomentumTerm() {
-
+    return momentumterm;
+  }
   public void setMomentumTerm(boolean momentum) {
     this.momentumterm = momentum;
   }
   
-  public void setMomentumweigth(double momentumweigth) {
+  public double getMomentumWeigth() {
+    return momentumweigth;
+  }
+  public void setMomentumWeigth(double momentumweigth) {
     this.momentumweigth = momentumweigth;
   }
 
-  public void setPopulationSize(int p) {
+  public double getMaximumAbsoluteChange() {
-    this.getPopulation().setPopulationSize(p);
-  }
-
-  public int GetPopulationSize() {
-    return this.getPopulation().getPopulationSize();
-  }
-
-  public double getRecoverythreshold() {
-    return recoverythreshold;
-  }
-  public void setRecoverythreshold(double recoverythreshold) {
-    this.recoverythreshold = recoverythreshold;
-  }
-
-  public double getMaximumabsolutechange() {
     return maximumabsolutechange;
   }
-
+  public void setMaximumAbsoluteChange(double maximumabsolutechange) {
-
-  public void setMaximumabsolutechange(double maximumabsolutechange) {
     this.maximumabsolutechange = maximumabsolutechange;
   }
+  public String maximumAbsoluteChangeTipText() {
+	  return "The maximum change along a coordinate in one step.";
+  }
 
 }

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

@@ -38,7 +38,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
 
     public HillClimbing() {
         this.m_Population = new Population();
-        this.m_Population.setPopulationSize(10);
+        this.m_Population.setTargetSize(10);
     }
     
     public HillClimbing(HillClimbing a) {

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

@@ -336,7 +336,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
         if (false) {
             imea.m_Optimizer    = new MultiObjectiveEA();
             ((MultiObjectiveEA)imea.m_Optimizer).setArchiveSize(25);
-            ((MultiObjectiveEA)imea.m_Optimizer).getPopulation().setPopulationSize(50);
+            ((MultiObjectiveEA)imea.m_Optimizer).getPopulation().setTargetSize(50);
             imea.m_Problem      = new TF1Problem();
             ((TF1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());
 //            ((TF1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData());

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

@@ -42,7 +42,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
 
     public MonteCarloSearch() {
         this.m_Population = new Population();
-        this.m_Population.setPopulationSize(50);
+        this.m_Population.setTargetSize(50);
     }
 
     public MonteCarloSearch(MonteCarloSearch a) {

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

@@ -48,7 +48,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
     transient private InterfacePopulationChangedEventListener m_Listener;
 
     public MultiObjectiveEA() {
-        this.m_Optimizer.getPopulation().setPopulationSize(100);
+        this.m_Optimizer.getPopulation().setTargetSize(100);
         ((GeneticAlgorithm)this.m_Optimizer).setParentSelection(new SelectMONonDominated());
         ((GeneticAlgorithm)this.m_Optimizer).setPartnerSelection(new SelectMONonDominated());
     }
@@ -297,7 +297,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
             archive = new Population();
             this.m_Optimizer.getPopulation().SetArchive(archive);
         }
-        return archive.getPopulationSize();
+        return archive.getTargetSize();
     }
     public void setArchiveSize(int b){
         Population archive = this.m_Optimizer.getPopulation().getArchive();
@@ -305,7 +305,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
             archive = new Population();
             this.m_Optimizer.getPopulation().SetArchive(archive);
         }
-        archive.setPopulationSize(b);
+        archive.setTargetSize(b);
     }
     public String archiveSizeTipText() {
         return "Choose the size of the archive.";

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

@@ -312,8 +312,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 	public void setPopulationSize(int populationSize) {
 		this.populationSize = populationSize;
 		if (m_Population!=null) {
-			m_Population.setPopulationSize(populationSize);
+			m_Population.setTargetSize(populationSize);
-			m_Population.setNotifyEvalInterval(m_Population.getPopulationSize());
+			m_Population.setNotifyEvalInterval(m_Population.getTargetSize());
 		}
 	}
 	
@@ -430,7 +430,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
 			indy.resetConstraintViolation();
 			initialPop.add((AbstractEAIndividual)indy.clone());
 		}
-		initialPop.setPopulationSize(initialPop.size());
+		initialPop.synchSize();
 	}
 
 	/**

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

@@ -100,7 +100,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
     	} else {
     		(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESCorrVector(mutationSigma, initialVelocity, rotationDeg));
     	}
-    	m_Population.setPopSize(popSize);
+    	m_Population.setTargetSize(popSize);
         this.m_Problem.initPopulation(this.m_Population);
         
         setWithShow(withShow);
@@ -144,13 +144,13 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
         
         // Generate a Population of Parents with Parantselectionmethod.
         // DONT forget cloning -> selection does only shallow copies!
-        int targetSize = this.m_Population.getPopulationSize();
+        int targetSize = this.m_Population.getTargetSize();
         if (randomImmigrationQuota>0) {
         	if (randomImmigrationQuota>1.) System.err.println("Error, invalid immigration quota!");
         	else {
-        		targetSize = (int)(this.m_Population.getPopulationSize() * (1.-randomImmigrationQuota));
+        		targetSize = (int)(this.m_Population.getTargetSize() * (1.-randomImmigrationQuota));
         		targetSize = Math.max(1, targetSize); // guarantee at least one to be selected 
-        		if (targetSize < this.m_Population.getPopulationSize()) doImmigr=true;
+        		if (targetSize < this.m_Population.getTargetSize()) doImmigr=true;
         	}
         }
 
@@ -160,7 +160,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
         	// add immigrants
     		AbstractEAIndividual immi;
     		int i;
-        	for (i=0; (i+parents.getPopulationSize())<pop.getPopulationSize(); i++) {
+        	for (i=0; (i+parents.getTargetSize())<pop.getTargetSize(); i++) {
         		immi = (AbstractEAIndividual)pop.getEAIndividual(0).clone();
         		immi.init(getProblem());
         		parents.add(immi);
@@ -181,7 +181,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
     	if (withShow) {
     		drawPop(pop, 0, false);
     	}
-        for (int i = 0; i < pop.getPopulationSize(); i++) {
+        for (int i = 0; i < pop.getTargetSize(); i++) {
         	applyMotionModel((AbstractEAIndividual)((AbstractEAIndividual)pop.get(i)), 0.);
         	indCount++;
         }
@@ -454,6 +454,6 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
 
 	public void setPopSize(int popSize) {
 		this.popSize = popSize;
-		m_Population.setPopSize(popSize);
+		m_Population.setTargetSize(popSize);
 	}
 }

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

@@ -1569,17 +1569,12 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		return "PSO-"+getTopology()+getTopologyRange()+"_"+getPhi1()+"_"+getPhi2();
 	}
 
-	/** Assuming that all optimizer will store thier data in a population
-	 * we will allow acess to this population to query to current state
-	 * of the optimizer.
-	 * @return The population of current solutions to a given problem.
-	 */
 	public Population getPopulation() {
 		return this.m_Population;
 	}
 	public void setPopulation(Population pop){
 		this.m_Population = pop;
-		if (pop.size() != pop.getPopulationSize()) { // new particle count!
+		if (pop.size() != pop.getTargetSize()) { // new particle count!
 			tracedVelocity = null;
 			initByPopulation(null, false);
 		} else for (int i=0; i<pop.size(); i++) {

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

@@ -37,7 +37,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
 
     public SimulatedAnnealing() {
         this.m_Population = new Population();
-        this.m_Population.setPopulationSize(10);
+        this.m_Population.setTargetSize(10);
     }
     
     public SimulatedAnnealing(SimulatedAnnealing a) {

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

@@ -35,7 +35,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
 
     public ThresholdAlgorithm() {
         this.m_Population = new Population();
-        this.m_Population.setPopulationSize(10);
+        this.m_Population.setTargetSize(10);
     }
 
     public ThresholdAlgorithm(ThresholdAlgorithm a) {

src/eva2/server/go/strategies/tribes/TribesSwarm.java

@@ -65,7 +65,7 @@ public class TribesSwarm implements java.io.Serializable{
         	for (int i=0; i<tribes[n].explorerNb; i++) pop.add(tribes[n].explorer[i]);
         }
         pop.add(getBestMemory().asDummyExplorer(range, masterTribe.getObjectiveFirstDim()));
-        pop.setPopulationSize(pop.size());
+        pop.synchSize();
     	return pop;
     }
 

src/eva2/server/modules/MOEAParameters.java

@@ -133,7 +133,7 @@ public class MOEAParameters extends AbstractGOParameters implements InterfaceGOP
             archive = new Population();
             ((MultiObjectiveEA)this.m_Optimizer).getPopulation().SetArchive(archive);
         }
-        ((MultiObjectiveEA)this.m_Optimizer).getPopulation().getArchive().setPopulationSize(b);
+        ((MultiObjectiveEA)this.m_Optimizer).getPopulation().getArchive().setTargetSize(b);
     }
     public String archiveSizeTipText() {
         return "Choose the size of the archive.";

src/eva2/server/modules/Processor.java

@@ -2,6 +2,8 @@ package eva2.server.modules;
 
 import java.util.Vector;
 
+import javax.swing.JOptionPane;
+
 import eva2.gui.BeanInspector;
 import eva2.server.go.InterfaceGOParameters;
 import eva2.server.go.InterfacePopulationChangedEventListener;
@@ -25,6 +27,7 @@ import eva2.server.stat.StatisticsWithGUI;
 import eva2.tools.EVAERROR;
 import eva2.tools.EVAHELP;
 import eva2.tools.Pair;
+import eva2.tools.StringTools;
 import eva2.tools.jproxy.RemoteStateListener;
 import eva2.tools.math.RNG;
 
@@ -165,15 +168,19 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
     			setPriority(1);
     		}
     	} catch (Exception e) {
-    		System.err.println("Caught exception in Processor: "+e.getMessage());
+    		String errMsg = e.getMessage();
+    		if ((errMsg == null) || (errMsg.length() == 0)) errMsg="check console output for error messages.";
+    		errMsg = "Exception in Processor: "+errMsg;
+    		System.err.println(errMsg);
     		e.printStackTrace();
+    		try {
+    			JOptionPane.showMessageDialog(null, StringTools.wrapLine(errMsg, 60, 0.2), "Error in Optimization", JOptionPane.ERROR_MESSAGE);
+    		} catch (Exception ex) {} catch(Error er) {};
         	//m_Statistics.stopOptPerformed(false);
         	setOptRunning(false); // normal finish
         	if (m_ListenerModule!=null) {
         		m_ListenerModule.performedStop(); // is only needed in client server mode
-        		String errMsg = e.getMessage();
+            	m_ListenerModule.updateProgress(0, errMsg);    		
-        		if ((errMsg == null) || (errMsg.length() == 0)) errMsg="check console output for error messages.";
-            	m_ListenerModule.updateProgress(0, "Error in optimization: " + errMsg);    		
         	}
     	}
     	return resPop;
@@ -428,7 +435,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
 	    	}
 	    	if (!resultPop.contains(m_Statistics.getBestSolution())) {
 	    		resultPop.add(m_Statistics.getBestSolution()); // this is a minor cheat but guarantees that the best solution ever found is contained in the final results
-	    		resultPop.setPopulationSize(resultPop.size());
+	    		resultPop.synchSize();
 	    	}
 	    	resultPop = PostProcess.postProcess(ppp, resultPop, (AbstractOptimizationProblem)goParams.getProblem(), listener);
 	    	resPop = resultPop;

src/eva2/tools/StringTools.java

@@ -130,4 +130,52 @@ public class StringTools {
 			return null;
 		}
 	}
+
+	/**
+	 * Rewrap a given string to lines of approx. length len.
+	 * 
+	 * @param str
+	 * @param len
+	 * @return
+	 */
+	public static String wrapLine(String str,  int len, double tolerancePerCent) {
+		return wrapLine(str, new char[]{' ', '-', ',', '.'}, len, tolerancePerCent);
+	}
+	
+	/**
+	 * Rewrap a given string to lines of approx. length len.
+	 * 
+	 * @param str
+	 * @param len
+	 * @return
+	 */
+	public static String wrapLine(String str, char[] breakChars, int len, double tolerancePerCent) {
+		StringBuffer res=new StringBuffer(); 
+		String rest=str;
+		int minLen = (int)((1.-tolerancePerCent)*(double)len);
+		int maxLen = (int)((1.+tolerancePerCent)*(double)len);
+		int nextBreak=-1;
+		while (rest.length()>0) {
+			if (rest.length()<=maxLen) {
+				nextBreak = rest.length()-1;
+			} else {
+				nextBreak = getNextBreak(minLen, maxLen, breakChars, rest); // search for a break character in a certain interval
+				if (nextBreak<0) nextBreak = len; // if none found force the break at the intended length
+			}
+			if (res.length()>0) res.append("\n"); // insert newline
+			res.append(rest.substring(0, nextBreak+1));
+			rest = rest.substring(nextBreak+1);
+		}
+		return res.toString();
+	}
+	
+	public static int getNextBreak(int startIndex, int endIndex, char[] brkChars, String str) {
+		int index;
+		for (int i=0; i<brkChars.length; i++) {
+			//indices[i] = str.indexOf(""+brkChars[i], startIndex);
+			index =str.indexOf(""+brkChars[i], startIndex);
+			if (index>=0 && (index <= endIndex)) return index;
+		}
+		return -1;
+	}
 }