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Rename AbstractEAIndividualComparator to EAIndividualComparator as it is not abstract.

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This commit is contained in:
Fabian Becker 2014-10-26 19:40:55 +01:00
parent a8320cad76
commit adc0d74bf7
10 changed files with 45 additions and 46 deletions
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31
src/eva2/optimization/individuals/AbstractEAIndividualComparator.java → src/eva2/optimization/individuals/EAIndividualComparator.java
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@ -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) {
AbstractEAIndividualComparator o = (AbstractEAIndividualComparator) other;
if (other instanceof EAIndividualComparator) {
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 #AbstractEAIndividualComparator(String)
* @see #EAIndividualComparator(int)
* @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);
}
/**

4
src/eva2/optimization/operator/archiving/ArchivingNSGAIISMeasure.java
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@ -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

4
src/eva2/optimization/operator/cluster/ClusteringDynPeakIdent.java
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@ -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];

8
src/eva2/optimization/operator/cluster/ClusteringNearestBetter.java
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@ -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) {

4
src/eva2/optimization/operator/mutation/MutateESRankMuCMA.java
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@ -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();

4
src/eva2/optimization/operator/terminators/HistoryConvergenceTerminator.java
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@ -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

12
src/eva2/optimization/population/Population.java
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@ -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));
}
/**

12
src/eva2/optimization/strategies/ClusterBasedNichingEA.java
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@ -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 AbstractEAIndividualComparator histComparator = new AbstractEAIndividualComparator("", -1, true);
private EAIndividualComparator reduceSizeComparator = new EAIndividualComparator();
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() {

4
src/eva2/optimization/strategies/MultiObjectiveEA.java
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@ -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)));
}
/**

8
src/eva2/optimization/strategies/ParticleSwarmOptimization.java
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@ -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());