Rename AbstractEAIndividualComparator to EAIndividualComparator as it is not abstract.

src/eva2/optimization/individuals/EAIndividualComparator.java

@@ -16,11 +16,10 @@ import java.util.Comparator;
  * the comparison is based on those. This may be used to access alternative (e.g. older or
  * best-so-far fitness values) for individual comparison.
  *
- * @author mkron
+ * @see AbstractEAIndividual#isDominatingFitness(double[], double[])
- * @see #AbstractEAIndividual().isDominatingFitness(double[], double[])
  */
 @eva2.util.annotation.Description(value = "A comparator class for general EA individuals. Compares individuals based on their fitness in context of minimization.")
-public class AbstractEAIndividualComparator implements Comparator<Object>, Serializable {
+public class EAIndividualComparator implements Comparator<Object>, Serializable {
     // flag whether a data field should be used.
     private String indyDataKey = "";
     private int fitCriterion = -1;
@@ -31,7 +30,7 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
      * The default version calls compares based on dominance with priority of feasibility if there are constraints.
      * It assigns -1 if first is better, 1 if second is better, 0 if the two ind.s are not comparable.
      */
-    public AbstractEAIndividualComparator() {
+    public EAIndividualComparator() {
         this("", -1, true);
     }
 
@@ -42,9 +41,9 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
      * also regarded by default.
      * If indyDataKey is null, the default comparison is used.
      *
-     * @param indyDataKey
+     * @param indyDataKey Field of the individual to use for comparison
      */
-    public AbstractEAIndividualComparator(String indyDataKey) {
+    public EAIndividualComparator(String indyDataKey) {
         this(indyDataKey, -1, true);
     }
 
@@ -54,7 +53,7 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
      *
      * @param fitnessCriterion
      */
-    public AbstractEAIndividualComparator(int fitnessCriterion) {
+    public EAIndividualComparator(int fitnessCriterion) {
         this("", fitnessCriterion, true);
     }
 
@@ -66,14 +65,14 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
      * @param fitIndex
      * @param preferFeasible
      */
-    public AbstractEAIndividualComparator(int fitIndex, boolean preferFeasible) {
+    public EAIndividualComparator(int fitIndex, boolean preferFeasible) {
         this("", fitIndex, preferFeasible);
     }
 
     @Override
     public boolean equals(Object other) {
-        if (other instanceof AbstractEAIndividualComparator) {
+        if (other instanceof EAIndividualComparator) {
-            AbstractEAIndividualComparator o = (AbstractEAIndividualComparator) other;
+            EAIndividualComparator o = (EAIndividualComparator) other;
             if ((indyDataKey == o.indyDataKey) || (indyDataKey != null && (indyDataKey.equals(o.indyDataKey)))) {
                 if ((fitCriterion == o.fitCriterion) && (preferFeasible == o.preferFeasible)) {
                     return true;
@@ -91,19 +90,19 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
     /**
      * Generic constructor.
      *
-     * @param indyDataKey
+     * @param indyDataKey Field of the individual to use for comparison
      * @param fitnessCriterion
      * @param preferFeasible
-     * @see #AbstractEAIndividualComparator(int)
+     * @see #EAIndividualComparator(int)
-     * @see #AbstractEAIndividualComparator(String)
+     * @see #EAIndividualComparator(String)
      */
-    public AbstractEAIndividualComparator(String indyDataKey, int fitnessCriterion, boolean preferFeasible) {
+    public EAIndividualComparator(String indyDataKey, int fitnessCriterion, boolean preferFeasible) {
         this.indyDataKey = indyDataKey;
         this.fitCriterion = fitnessCriterion;
         this.preferFeasible = preferFeasible;
     }
 
-    public AbstractEAIndividualComparator(AbstractEAIndividualComparator other) {
+    public EAIndividualComparator(EAIndividualComparator other) {
         indyDataKey = other.indyDataKey;
         fitCriterion = other.fitCriterion;
         preferFeasible = other.preferFeasible;
@@ -111,7 +110,7 @@ public class AbstractEAIndividualComparator implements Comparator<Object>, Seria
 
     @Override
     public Object clone() {
-        return new AbstractEAIndividualComparator(this);
+        return new EAIndividualComparator(this);
     }
 
     /**

src/eva2/optimization/operator/archiving/ArchivingNSGAIISMeasure.java

@@ -1,7 +1,7 @@
 package eva2.optimization.operator.archiving;
 
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.individuals.AbstractEAIndividualComparator;
+import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.population.Population;
 
 import java.util.Arrays;
@@ -65,7 +65,7 @@ public class ArchivingNSGAIISMeasure extends ArchivingNSGAII {
         }
 
 
-        Arrays.sort(frontArray, new AbstractEAIndividualComparator(0));
+        Arrays.sort(frontArray, new EAIndividualComparator(0));
 
 
         ((AbstractEAIndividual) frontArray[0]).putData("HyperCube", Double.MAX_VALUE); //die beiden aussen bekommen maximal wert als smeasure

src/eva2/optimization/operator/cluster/ClusteringDynPeakIdent.java

@@ -1,7 +1,7 @@
 package eva2.optimization.operator.cluster;
 
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.individuals.AbstractEAIndividualComparator;
+import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.individuals.IndividualDistanceComparator;
 import eva2.optimization.operator.distancemetric.EuclideanMetric;
 import eva2.optimization.operator.distancemetric.InterfaceDistanceMetric;
@@ -99,7 +99,7 @@ public class ClusteringDynPeakIdent implements InterfaceClustering, java.io.Seri
 
     @Override
     public Population[] cluster(Population pop, Population referenceSet) {
-        AbstractEAIndividualComparator eaComparator = new AbstractEAIndividualComparator(-1);
+        EAIndividualComparator eaComparator = new EAIndividualComparator(-1);
         Population sorted = pop.getSortedBestFirst(eaComparator);
         Population peaks = performDynPeakIdent(metric, sorted, numNiches, nicheRadius);
         Population[] clusters = new Population[peaks.size() + 1];

src/eva2/optimization/operator/cluster/ClusteringNearestBetter.java

@@ -2,7 +2,7 @@ package eva2.optimization.operator.cluster;
 
 import eva2.gui.editor.GenericObjectEditor;
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.individuals.AbstractEAIndividualComparator;
+import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.operator.distancemetric.InterfaceDistanceMetric;
 import eva2.optimization.operator.distancemetric.PhenotypeMetric;
 import eva2.optimization.operator.paramcontrol.ParamAdaption;
@@ -37,7 +37,7 @@ public class ClusteringNearestBetter implements InterfaceClustering, Serializabl
 
     private int[] uplink;
     private double[] uplinkDist;
-    private AbstractEAIndividualComparator comparator = new AbstractEAIndividualComparator();
+    private EAIndividualComparator comparator = new EAIndividualComparator();
     private Vector<Integer>[] children;
     private static final String initializedForKey = "initializedClustNearestBetterOnHash";
     private static final String initializedRefData = "initializedClustNearestBetterData";
@@ -52,7 +52,7 @@ public class ClusteringNearestBetter implements InterfaceClustering, Serializabl
         this.meanDistFactor = o.meanDistFactor;
         this.currentMeanDistance = o.currentMeanDistance;
         this.minimumGroupSize = o.minimumGroupSize;
-        this.comparator = (AbstractEAIndividualComparator) o.comparator.clone();
+        this.comparator = (EAIndividualComparator) o.comparator.clone();
         this.testConvergingSpeciesOnBestOnly = o.testConvergingSpeciesOnBestOnly;
     }
 
@@ -435,7 +435,7 @@ public class ClusteringNearestBetter implements InterfaceClustering, Serializabl
         return "Define the comparator by which the population is sorted before clustering.";
     }
 
-    public AbstractEAIndividualComparator getComparator() {
+    public EAIndividualComparator getComparator() {
         return comparator;
     }
 //	public void setComparator(AbstractEAIndividualComparator comparator) {

src/eva2/optimization/operator/mutation/MutateESRankMuCMA.java

@@ -3,7 +3,7 @@ package eva2.optimization.operator.mutation;
 import eva2.gui.editor.GenericObjectEditor;
 import eva2.optimization.enums.ESMutationInitialSigma;
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.individuals.AbstractEAIndividualComparator;
+import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.operator.distancemetric.EuclideanMetric;
 import eva2.optimization.population.Population;
@@ -112,7 +112,7 @@ public class MutateESRankMuCMA implements InterfaceAdaptOperatorGenerational, In
      */
     @Override
     public void adaptAfterSelection(Population oldGen, Population selectedP) {
-        Population selectedSorted = selectedP.getSortedBestFirst(new AbstractEAIndividualComparator(-1));
+        Population selectedSorted = selectedP.getSortedBestFirst(new EAIndividualComparator(-1));
 
         int mu, lambda;
         mu = selectedP.size();

src/eva2/optimization/operator/terminators/HistoryConvergenceTerminator.java

@@ -1,7 +1,7 @@
 package eva2.optimization.operator.terminators;
 
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.individuals.AbstractEAIndividualComparator;
+import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.operator.distancemetric.ObjectiveSpaceMetric;
 import eva2.optimization.population.InterfaceSolutionSet;
 import eva2.optimization.population.Population;
@@ -22,7 +22,7 @@ public class HistoryConvergenceTerminator implements InterfaceTerminator, Serial
     int fitCrit = 0;
     double convergenceThreshold;
     boolean stdDevInsteadOfImprovement;
-    AbstractEAIndividualComparator indyImprovementComparator = new AbstractEAIndividualComparator("", -1, true);
+    EAIndividualComparator indyImprovementComparator = new EAIndividualComparator("", -1, true);
     String msg;
 
     public static final boolean hideFromGOE = true; // hide from GUI

src/eva2/optimization/population/Population.java

@@ -1039,7 +1039,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         }
     }
 
-    public int getIndexOfBestEAIndividual(AbstractEAIndividualComparator comparator) {
+    public int getIndexOfBestEAIndividual(EAIndividualComparator comparator) {
         return getIndexOfBestOrWorstIndividual(true, comparator);
     }
 
@@ -1059,10 +1059,10 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @param fitIndex
      * @return
      * @see #getIndexOfBestOrWorstIndividual(boolean, Comparator)
-     * @see AbstractEAIndividualComparator
+     * @see eva2.optimization.individuals.EAIndividualComparator
      */
     public int getIndexOfBestOrWorstIndy(boolean bBest, boolean checkConstraints, int fitIndex) {
-        return getIndexOfBestOrWorstIndividual(bBest, new AbstractEAIndividualComparator(fitIndex, checkConstraints));
+        return getIndexOfBestOrWorstIndividual(bBest, new EAIndividualComparator(fitIndex, checkConstraints));
     }
 
     /**
@@ -1162,7 +1162,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
             n = super.size();
         }
         Population pop = new Population(n);
-        getSortedNIndividuals(n, true, pop, new AbstractEAIndividualComparator(fitIndex));
+        getSortedNIndividuals(n, true, pop, new EAIndividualComparator(fitIndex));
         return pop;
     }
 
@@ -1179,7 +1179,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      */
     public Population getWorstNIndividuals(int n, int fitIndex) {
         Population pop = new Population(n);
-        getSortedNIndividuals(n, false, pop, new AbstractEAIndividualComparator(fitIndex));
+        getSortedNIndividuals(n, false, pop, new EAIndividualComparator(fitIndex));
         return pop;
     }
 
@@ -1272,7 +1272,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @param fitIndex
      */
     public void setSortingFitnessCriterion(int fitIndex) {
-        getSorted(new AbstractEAIndividualComparator(fitIndex));
+        getSorted(new EAIndividualComparator(fitIndex));
     }
 
     /**

src/eva2/optimization/strategies/ClusterBasedNichingEA.java

@@ -7,7 +7,7 @@ import eva2.gui.plot.Plot;
 import eva2.gui.plot.TopoPlot;
 import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.individuals.AbstractEAIndividualComparator;
+import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.operator.cluster.ClusteringDensityBased;
 import eva2.optimization.operator.cluster.InterfaceClustering;
@@ -72,8 +72,8 @@ public class ClusterBasedNichingEA extends AbstractOptimizer implements Interfac
     private double muLambdaRatio = 0.5;
     private int sleepTime = 0;
     private int maxSpeciesSize = 15;
-    private AbstractEAIndividualComparator reduceSizeComparator = new AbstractEAIndividualComparator();
+    private EAIndividualComparator reduceSizeComparator = new EAIndividualComparator();
-    private AbstractEAIndividualComparator histComparator = new AbstractEAIndividualComparator("", -1, true);
+    private EAIndividualComparator histComparator = new EAIndividualComparator("", -1, true);
     protected ParameterControlManager paramControl = new ParameterControlManager();
     private double avgDistForConvergence = 0.1; // Upper bound for average indy distance in a species in the test for convergence
 
@@ -1137,12 +1137,12 @@ public class ClusterBasedNichingEA extends AbstractOptimizer implements Interfac
         return "Set the comparator used to define the 'worst' individuals when reducing species size.";
     }
 
-    public AbstractEAIndividualComparator getReduceSizeComparator() {
+    public EAIndividualComparator getReduceSizeComparator() {
         return reduceSizeComparator;
     }
 
     public void setReduceSizeComparator(
-            AbstractEAIndividualComparator reduceSizeComparator) {
+            EAIndividualComparator reduceSizeComparator) {
         this.reduceSizeComparator = reduceSizeComparator;
     }
 
@@ -1153,7 +1153,7 @@ public class ClusterBasedNichingEA extends AbstractOptimizer implements Interfac
     //	public void setHistComparator(AbstractEAIndividualComparator histComparator) {
 //		this.histComparator = histComparator;
 //	}
-    public AbstractEAIndividualComparator getHistComparator() {
+    public EAIndividualComparator getHistComparator() {
         return histComparator;
     }
 //	public String histComparatorTipText() {

src/eva2/optimization/strategies/MultiObjectiveEA.java

@@ -2,7 +2,7 @@ package eva2.optimization.strategies;
 
 import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.individuals.AbstractEAIndividualComparator;
+import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.operator.archiving.ArchivingNSGAII;
 import eva2.optimization.operator.archiving.InformationRetrievalInserting;
 import eva2.optimization.operator.archiving.InterfaceArchiving;
@@ -248,7 +248,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
 
     @Override
     public InterfaceSolutionSet getAllSolutions() {
-        return new SolutionSet(getPopulation(), ArchivingNSGAII.getNonDominatedSortedFront(getPopulation().getArchive()).getSortedPop(new AbstractEAIndividualComparator(0)));
+        return new SolutionSet(getPopulation(), ArchivingNSGAII.getNonDominatedSortedFront(getPopulation().getArchive()).getSortedPop(new EAIndividualComparator(0)));
     }
 
     /**

src/eva2/optimization/strategies/ParticleSwarmOptimization.java

@@ -7,7 +7,7 @@ import eva2.gui.plot.TopoPlot;
 import eva2.optimization.enums.PSOTopology;
 import eva2.optimization.go.InterfacePopulationChangedEventListener;
 import eva2.optimization.individuals.AbstractEAIndividual;
-import eva2.optimization.individuals.AbstractEAIndividualComparator;
+import eva2.optimization.individuals.EAIndividualComparator;
 import eva2.optimization.individuals.InterfaceDataTypeDouble;
 import eva2.optimization.operator.distancemetric.PhenotypeMetric;
 import eva2.optimization.operator.paramcontrol.ParamAdaption;
@@ -1316,9 +1316,9 @@ public class ParticleSwarmOptimization extends AbstractOptimizer implements java
         if ((topology == PSOTopology.multiSwarm) || (topology == PSOTopology.tree)) {
             sortedPop = pop.toArray();
             if ((topology == PSOTopology.multiSwarm) || (treeStruct >= 2)) {
-                Arrays.sort(sortedPop, new AbstractEAIndividualComparator());
+                Arrays.sort(sortedPop, new EAIndividualComparator());
             } else {
-                Arrays.sort(sortedPop, new AbstractEAIndividualComparator(partBestFitKey));
+                Arrays.sort(sortedPop, new EAIndividualComparator(partBestFitKey));
             }
             addSortedIndicesTo(sortedPop, pop);
         }
@@ -1379,7 +1379,7 @@ public class ParticleSwarmOptimization extends AbstractOptimizer implements java
         if (topology == PSOTopology.hpso) { // HPSO sorting the population
             int parentIndex;
             AbstractEAIndividual indy;
-            AbstractEAIndividualComparator comp = new AbstractEAIndividualComparator(partBestFitKey);
+            EAIndividualComparator comp = new EAIndividualComparator(partBestFitKey);
             for (int i = 0; i < pop.size(); i++) {
                 // loop over the part of the tree which is complete (full degree in each level)
                 parentIndex = getParentIndex(topologyRange, i, pop.size());