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Remove a huge chunk of globalInfo methods.

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refs #9
This commit is contained in:
Fabian Becker 2014-10-31 00:29:22 +01:00
parent 0b35a9e880
commit 4db79c69d2
127 changed files with 297 additions and 1554 deletions
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3
src/META-INF/MANIFEST.MF
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@ -1,3 +0,0 @@
Manifest-Version: 1.0
Main-Class: eva2.gui.Main

2
src/eva2/OptimizerFactory.java
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@ -769,7 +769,7 @@ public class OptimizerFactory {
opt.setPopulation(pop);
params.setOptimizer(opt);
params.setTerminator(term);
params.setSeed(seed);
params.setRandomSeed(seed);
return params;
}

2
src/eva2/examples/TestingCbnPostProc.java
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@ -18,7 +18,7 @@ public class TestingCbnPostProc {
OptimizationParameters esParams = OptimizerFactory.standardCbnES(fm0);
esParams.setTerminator(new EvaluationTerminator(2000));
esParams.setSeed(0);
esParams.setRandomSeed(0);
best = (AbstractEAIndividual) OptimizerFactory.optimizeToInd(esParams, null);
System.out.println(esParams.getTerminator().lastTerminationMessage() + "\nFound solution: "

2
src/eva2/examples/TestingGAB1.java
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@ -19,7 +19,7 @@ public class TestingGAB1 {
// add an evaluation terminator
gaParams.setTerminator(new EvaluationTerminator(1000));
// set a specific random seed
gaParams.setSeed(2342);
gaParams.setRandomSeed(2342);
// access the GA
GeneticAlgorithm ga = (GeneticAlgorithm) gaParams.getOptimizer();

2
src/eva2/examples/TestingPlusCmaEs.java
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@ -19,7 +19,7 @@ public class TestingPlusCmaEs {
OptimizationParameters esParams = OptimizerFactory.standardES(fm0);
esParams.setTerminator(new EvaluationTerminator(2000));
// set a random seed based on system time
esParams.setSeed(0);
esParams.setRandomSeed(0);
// set evolutionary operators and probabilities
AbstractEAIndividual.setOperators(

5
src/eva2/gui/PropertyPanel.java
View File

@ -68,9 +68,12 @@ public class PropertyPanel extends JPanel {
Rectangle box = new Rectangle(i.left, i.top,
getSize().width - i.left - i.right,
getSize().height - i.top - i.bottom);
g.clearRect(i.left, i.top,
Color back = g.getColor();
g.setColor(Color.WHITE);
g.fillRect(i.left, i.top,
getSize().width - i.right - i.left,
getSize().height - i.bottom - i.top);
g.setColor(back);
propertyEditor.paintValue(g, box);
}

4
src/eva2/optimization/go/InterfaceOptimizationParameters.java
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@ -27,9 +27,9 @@ public interface InterfaceOptimizationParameters {
*
* @param x Long seed.
*/
void setSeed(long x);
void setRandomSeed(long x);
long getSeed();
long getRandomSeed();
/**
* This method allows you to choose a termination criteria for the

8
src/eva2/optimization/go/StandaloneOptimization.java
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@ -78,7 +78,7 @@ public class StandaloneOptimization implements InterfaceStandaloneOptimization,
this.optimizationParameters = OptimizationParameters.getInstance();
this.experimentName = this.optimizationParameters.getOptimizer().getName() + "-" + this.performedRuns.size();
this.optimizationParameters.addPopulationChangedEventListener(this);
RNG.setRandomSeed(optimizationParameters.getSeed());
RNG.setRandomSeed(optimizationParameters.getRandomSeed());
}
/**
@ -348,7 +348,7 @@ public class StandaloneOptimization implements InterfaceStandaloneOptimization,
this.statusField.setText("Optimizing...");
}
RNG.setRandomSeed(optimizationParameters.getSeed());
RNG.setRandomSeed(optimizationParameters.getRandomSeed());
// opening output file...
if (!this.outputFileName.equalsIgnoreCase("none")) {
String name = "";
@ -646,10 +646,10 @@ public class StandaloneOptimization implements InterfaceStandaloneOptimization,
* @param seed The seed for the random number generator
*/
// MK: These methods have nothing to do with the seed parameter from the optimizationParameters object which is actually used, so I comment them out
// public void setSeed(long seed) {
// public void setRandomSeed(long seed) {
// RNG.setseed(seed);
// }
// public long getSeed() {
// public long getRandomSeed() {
// return RNG.getRandomSeed();
// }
// public String seedTipText() {

20
src/eva2/optimization/modules/AbstractOptimizationParameters.java
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@ -55,7 +55,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
* optimization e.g. with Hill Climbing.
*/
@Parameter(name = "Post Processing", description = "Parameters for the post processing step.")
protected InterfacePostProcessParams postProcessing = new PostProcessParams(false);
protected InterfacePostProcessParams postProcessParams = new PostProcessParams(false);
transient protected InterfacePopulationChangedEventListener populationChangedEventListener;
transient private List<InterfaceNotifyOnInformers> toInformAboutInformers = null;
@ -70,7 +70,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
this.terminator = optimizationParameters.terminator;
this.optimizer.setProblem(this.problem);
this.randomSeed = optimizationParameters.randomSeed;
this.postProcessing = optimizationParameters.postProcessing;
this.postProcessParams = optimizationParameters.postProcessParams;
}
public AbstractOptimizationParameters(InterfaceOptimizer opt, InterfaceOptimizationProblem prob, InterfaceTerminator term) {
@ -78,7 +78,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
optimizer = opt;
problem = prob;
terminator = term;
postProcessing = new PostProcessParams(false);
postProcessParams = new PostProcessParams(false);
opt.setProblem(prob);
}
@ -92,8 +92,8 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
setProblem(parameters.problem);
setTerminator(parameters.terminator);
this.optimizer.setProblem(this.problem);
setSeed(parameters.randomSeed);
setPostProcessParams(parameters.postProcessing);
setRandomSeed(parameters.randomSeed);
setPostProcessParams(parameters.postProcessParams);
}
/**
@ -236,7 +236,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
* @param x Long seed.
*/
@Override
public void setSeed(long x) {
public void setRandomSeed(long x) {
randomSeed = x;
}
@ -246,7 +246,7 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
* @return The current seed for the random number generator.
*/
@Override
public long getSeed() {
public long getRandomSeed() {
return randomSeed;
}
@ -268,16 +268,16 @@ public abstract class AbstractOptimizationParameters implements InterfaceOptimiz
@Override
public InterfacePostProcessParams getPostProcessParams() {
return postProcessing;
return postProcessParams;
}
@Override
public void setPostProcessParams(InterfacePostProcessParams ppp) {
postProcessing = ppp;
postProcessParams = ppp;
}
@Override
public void setDoPostProcessing(boolean doPP) {
postProcessing.setDoPostProcessing(doPP);
postProcessParams.setDoPostProcessing(doPP);
}
}

2
src/eva2/optimization/modules/Processor.java
View File

@ -212,7 +212,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
setOptimizationRunning(true);
}
RNG.setRandomSeed(optimizationParameters.getSeed());
RNG.setRandomSeed(optimizationParameters.getRandomSeed());
if (optimizationStateListener != null) {
if (wasRestarted) {

14
src/eva2/optimization/operator/archiving/ArchivingAllDominating.java
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@ -3,11 +3,13 @@ package eva2.optimization.operator.archiving;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.selection.SelectBestIndividuals;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* This simple strategy simply archives all Pareto optimal solutions. This method is
* very prone to OutOfMemory errors!
*/
@Description("This is a straightforward strategy, which selects all dominating individuals (very prone to generate OutOfMemory errors).")
public class ArchivingAllDominating extends AbstractArchiving implements java.io.Serializable {
@ -63,18 +65,6 @@ public class ArchivingAllDominating extends AbstractArchiving implements java.io
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a straightforward strategy, which selects all dominating individuals (very prone to generate OutOfMemory errors).";
}
/**
* This method will return a naming String
*

107
src/eva2/optimization/operator/archiving/ArchivingMaxiMin.java
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@ -7,17 +7,14 @@ import eva2.optimization.operator.selection.SelectBestIndividuals;
import eva2.optimization.population.Population;
import eva2.tools.chart2d.Chart2DDPointIconCross;
import eva2.tools.chart2d.DPointIcon;
import eva2.util.annotation.Description;
/**
* Another simple archiving strategy not based on dominance but on the MaxiMin
* criterion. Doesn't work well on non-convex Pareto fronts.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 09.08.2004
* Time: 17:59:26
* To change this template use File | Settings | File Templates.
*/
@Description("Maxi Min Archiving.")
public class ArchivingMaxiMin implements InterfaceArchiving, java.io.Serializable {
private MOSOMaxiMin maxiMin = new MOSOMaxiMin();
@ -85,18 +82,6 @@ public class ArchivingMaxiMin implements InterfaceArchiving, java.io.Serializabl
return new Chart2DDPointIconCross();
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Maxi Min Archiving.";
}
/**
* This method will return a naming String
*
@ -141,90 +126,4 @@ public class ArchivingMaxiMin implements InterfaceArchiving, java.io.Serializabl
public String obeyDebsConstViolationPrincipleToolTip() {
return "Toggle the use of Deb's coonstraint violation principle.";
}
}
//extends AbstractArchiving implements java.io.Serializable {
//
// private MOSOMaxiMin maxiMin = new MOSOMaxiMin();
// private InterfaceSelection selections = new SelectBestIndividuals();
//
// public ArchivingMaxiMin() {
// }
//
// public ArchivingMaxiMin(ArchivingMaxiMin a) {
// this.maxiMin = new MOSOMaxiMin();
// this.selections = (InterfaceSelection)a.selections.clone();
// }
//
// public Object clone() {
// return (Object) new ArchivingMaxiMin(this);
// }
//
// /** This method allows you to merge to populations into an archive.
// * This method will add elements from pop to the archive but will also
// * remove elements from the archive if the archive target size is exceeded.
// * @param pop The population that may add Individuals to the archive.
// */
// public void addElementsToArchive(Population pop) {
// Population archive;
// double[] tmpD;
// if (pop.getArchive() == null) pop.SetArchive(new Population());
//
// // First merge the current population and the archive
// Population tmpPop = new Population();
// tmpPop.addPopulation((Population)pop.getClone());
// tmpPop.addPopulation((Population)pop.getArchive().getClone());
// tmpPop.removeDoubleInstancesUsingFitness();
//
// // Now calculate the MaxiMin Criterium
// this.maxiMin.convertMultiObjective2SingleObjective(tmpPop);
// this.selections.prepareSelection(tmpPop);
// archive = this.selections.selectFrom(tmpPop, pop.getArchive().getPopulationSize());
// archive.setPopulationSize(pop.getArchive().getPopulationSize());
//
// // now unconvert from SO to MO
// for (int i = 0; i < archive.size(); i++) {
// tmpD = (double[])((AbstractEAIndividual)archive.get(i)).getData("MOFitness");
// ((AbstractEAIndividual)archive.get(i)).SetFitness(tmpD);
// }
//
// pop.SetArchive(archive);
// }
//
// /** This method allows you to determine an icon for a given individual
// * @param pop The population
// * @param index The identifier for the individual
// */
// public DPointIcon getIconFor(int index, Population pop) {
// return new Chart2DDPointIconCross();
// }
//
///**********************************************************************************************************************
// * These are for GUI
// */
// /** This method returns a global info string
// * @return description
// */
// public static String globalInfo() {
// return "Maxi Min Archiving.";
// }
// /** This method will return a naming String
// * @return The name of the algorithm
// */
// public String getName() {
// return "MaxiMin";
// }
//
// /** Since SelectMOMaxiMin relies on a MOSO conversion
// * a single criterion selection method can be used.
// * @param pop The selection method used.
// */
// public void setSelectionMethod(InterfaceSelection pop) {
// this.selections = pop;
// }
// public InterfaceSelection getSelectionMethod() {
// return this.selections;
// }
// public String selectionMethodTipText() {
// return "Choose the selection method (single-criteria ones please).";
// }
//}
}

19
src/eva2/optimization/operator/archiving/ArchivingNSGAII.java
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@ -4,18 +4,15 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.tools.chart2d.Chart2DDPointIconCross;
import eva2.tools.chart2d.DPointIcon;
import eva2.util.annotation.Description;
import java.util.ArrayList;
/**
* The secon verison of the non dominace sorting GA.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 04.08.2003
* Time: 16:56:10
* To change this template use Options | File Templates.
*/
@Description("Non-dominating sorting GA revision 2.0.")
public class ArchivingNSGAII extends ArchivingNSGA implements java.io.Serializable {
public ArchivingNSGAII() {
@ -176,18 +173,6 @@ public class ArchivingNSGAII extends ArchivingNSGA implements java.io.Serializab
return new Chart2DDPointIconCross();
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Non-dominating sorting GA revision 2.0.";
}
/**
* This method will return a naming String
*

22
src/eva2/optimization/operator/archiving/ArchivingPESAII.java
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@ -3,18 +3,15 @@ package eva2.optimization.operator.archiving;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
/**
* The Pareto envelope sorting algorithm using a hybergrid and
* the so called squezze factor.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 05.04.2004
* Time: 14:24:54
* To change this template use File | Settings | File Templates.
* the so called squeeze factor.
*/
@Description("Pareto Envelope-based Selection Algorithm revision 2.0.")
public class ArchivingPESAII extends AbstractArchiving implements java.io.Serializable {
private int gridSize = 4;
@ -173,19 +170,6 @@ public class ArchivingPESAII extends AbstractArchiving implements java.io.Serial
return result;
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Pareto Envelope-based Selection Algorithm revision 2.0.";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/archiving/ArchivingSPEAII.java
View File

@ -10,11 +10,13 @@ import eva2.optimization.population.Population;
import eva2.tools.chart2d.Chart2DDPointIconCircle;
import eva2.tools.chart2d.Chart2DDPointIconText;
import eva2.tools.chart2d.DPoint;
import eva2.util.annotation.Description;
/**
* The strength Pareto EA in it's second version, which is based on
* dominance counts.
*/
@Description("Strength Pareto EA revision 2.0. The variable k to calculate the k-th distance is given by max(2, sqrt(archive.size())).")
public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serializable {
private InterfaceDistanceMetric metric = new ObjectiveSpaceMetric();
@ -379,18 +381,6 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial
return SPEAResult;
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Strength Pareto EA revision 2.0. The variable k to calculate the k-th distance is given by max(2, sqrt(archive.size())).";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/archiving/InformationRetrievalInserting.java
View File

@ -1,12 +1,14 @@
package eva2.optimization.operator.archiving;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* This information retrieval inserts the retrieved
* solutions, by removing random individuals from the
* population.
*/
@Description("This Information Retrieval will insert the archive into current population by replacing random individuals if necessary.")
public class InformationRetrievalInserting implements InterfaceInformationRetrieval, java.io.Serializable {
public InformationRetrievalInserting() {
@ -43,18 +45,6 @@ public class InformationRetrievalInserting implements InterfaceInformationRetrie
pop.addPopulation((Population) archive.getClone());
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This Information Retrieval will insert the archive into current population by replacing random individuals if necessary.";
}
/**
* This method will return a naming String
*

18
src/eva2/optimization/operator/archiving/InformationRetrievalNone.java
View File

@ -1,16 +1,13 @@
package eva2.optimization.operator.archiving;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* This class allows no information retrieval and thus no elitism
* for MOEAs.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 11.03.2004
* Time: 14:54:33
* To change this template use File | Settings | File Templates.
*/
@Description("This implements a deactivated Information Retrieval.")
public class InformationRetrievalNone implements InterfaceInformationRetrieval, java.io.Serializable {
public InformationRetrievalNone() {
@ -35,17 +32,6 @@ public class InformationRetrievalNone implements InterfaceInformationRetrieval,
// no InterfaceInformation Retrieval is performed
return;
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This implements a deactivated Information Retrieval.";
}
/**
* This method will return a naming String

14
src/eva2/optimization/operator/archiving/InformationRetrievalReplacing.java
View File

@ -2,11 +2,13 @@ package eva2.optimization.operator.archiving;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* This information retrieval method simply add the retrieved solutions
* to the current population.
*/
@Description("This Information Retrieval will replace the current population by the archive.")
public class InformationRetrievalReplacing implements InterfaceInformationRetrieval, java.io.Serializable {
public InformationRetrievalReplacing() {
@ -43,18 +45,6 @@ public class InformationRetrievalReplacing implements InterfaceInformationRetrie
pop.addPopulation(tmp);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This Information Retrieval will replace the current population by the archive.";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/classification/ClassificationSelfOrganizingMaps.java
View File

@ -2,6 +2,7 @@ package eva2.optimization.operator.classification;
import eva2.tools.chart2d.*;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import javax.swing.*;
import java.awt.*;
@ -13,6 +14,7 @@ import java.awt.event.WindowEvent;
* for classification. The Dikel flag is an undocumented extension,
* which seems to work but is not published.
*/
@Description("The Self-Organizing Maps, have been proposed by Kohonen (read this book on SOMs for further details).")
public class ClassificationSelfOrganizingMaps implements java.io.Serializable, InterfaceClassification {
/**
@ -489,9 +491,6 @@ public class ClassificationSelfOrganizingMaps implements java.io.Serializable, I
som.train(data, type);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -502,15 +501,6 @@ public class ClassificationSelfOrganizingMaps implements java.io.Serializable, I
return "Self-Organizing Maps";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The Self-Organizing Maps, have been proposed by Kohonen (read this book on SOMs for further details).";
}
/**
* This method allows you to set the number of neurons
* in x dimension

8
src/eva2/optimization/operator/cluster/ClusterAll.java
View File

@ -1,15 +1,15 @@
package eva2.optimization.operator.cluster;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
import java.io.Serializable;
import java.util.Arrays;
/**
* Dummy class which assigns all individuals to a single cluster only.
*
* @author mkron
*/
@Description("A dummy clustering implementation which assigns all elements to a single cluster.")
public class ClusterAll implements InterfaceClustering, Serializable {
private boolean assignLoners = false; // should loners be assigned?
@ -80,10 +80,6 @@ public class ClusterAll implements InterfaceClustering, Serializable {
return true;
}
public static String globalInfo() {
return "A dummy clustering implementation which assigns all elements to a single cluster.";
}
public String getName() {
return "Cluster-all";
}

16
src/eva2/optimization/operator/cluster/ClusteringDensityBased.java
View File

@ -5,14 +5,16 @@ import eva2.optimization.operator.distancemetric.InterfaceDistanceMetric;
import eva2.optimization.operator.distancemetric.PhenotypeMetric;
import eva2.optimization.population.Population;
import eva2.tools.Pair;
import eva2.util.annotation.Description;
import java.util.ArrayList;
/**
* The DBSCAN method. As far as I recall this is an hierachical
* The DBSCAN method. As far as I recall this is an hierarchical
* clustering method like the single-link method.
*/
@Description("A density-based clustering algorithm (DBSCAN).")
public class ClusteringDensityBased implements InterfaceClusteringDistanceParam, InterfaceClusteringMetricBased, java.io.Serializable {
private InterfaceDistanceMetric metric = new PhenotypeMetric();
@ -233,18 +235,6 @@ public class ClusteringDensityBased implements InterfaceClusteringDistanceParam,
return res;
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "A density-based clustering algorithm (DBSCAN).";
}
/**
* This method will return a naming String
*

16
src/eva2/optimization/operator/cluster/ClusteringKMeans.java
View File

@ -12,13 +12,15 @@ import eva2.problems.F1Problem;
import eva2.tools.chart2d.Chart2DDPointIconCircle;
import eva2.tools.chart2d.Chart2DDPointIconText;
import eva2.tools.chart2d.DPoint;
import eva2.util.annotation.Description;
import java.util.Arrays;
/**
* The k-mean clustering algorithms. I guess it is not a hierachical
* The k-mean clustering algorithms. I guess it is not a hierarchical
* clustering method.
*/
@Description("Oldy but goldy: K-Means clustering.")
public class ClusteringKMeans implements InterfaceClustering, java.io.Serializable {
private int k = 5;
@ -404,18 +406,6 @@ public class ClusteringKMeans implements InterfaceClustering, java.io.Serializab
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Oldy but goldy: K-Means clustering.";
}
/**
* This method will return a naming String
*

8
src/eva2/optimization/operator/cluster/ClusteringNearestBetter.java
View File

@ -8,6 +8,7 @@ import eva2.optimization.operator.distancemetric.PhenotypeMetric;
import eva2.optimization.operator.paramcontrol.ParamAdaption;
import eva2.optimization.operator.paramcontrol.ParameterControlManager;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
import java.io.Serializable;
import java.util.ArrayList;
@ -21,9 +22,8 @@ import java.util.Vector;
* A tree is produced by assigning each individual the closest individual with better fitness.
* Connections with a distance above a certain threshold are cut. After that, each interconnected subtree forms a cluster.
* In the paper, the threshold is deduced as 2*d_p for d_p: the mean distance in the population.
*
* @author mkron
*/
@Description("A tree is produced by assigning each individual the closest individual with better fitness. Connections with a distance above a certain threshold are cut. After that, each interconnected subtree forms a cluster.")
public class ClusteringNearestBetter implements InterfaceClustering, Serializable {
private static final long serialVersionUID = 1L;
private InterfaceDistanceMetric metric = new PhenotypeMetric();
@ -391,10 +391,6 @@ public class ClusteringNearestBetter implements InterfaceClustering, Serializabl
}
}
public static String globalInfo() {
return "A tree is produced by assigning each individual the closest individual with better fitness. Connections with a distance above a certain threshold are cut. After that, each interconnected subtree forms a cluster.";
}
public String metricTipText() {
return "The metric to use during clustering.";
}

19
src/eva2/optimization/operator/cluster/ClusteringXMeans.java
View File

@ -11,6 +11,7 @@ import eva2.tools.chart2d.Chart2DDPointIconCircle;
import eva2.tools.chart2d.Chart2DDPointIconText;
import eva2.tools.chart2d.DPoint;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.Arrays;
@ -18,12 +19,8 @@ import java.util.Arrays;
* The x-means clustering method should be able to determine a
* suiteable value for k automatically, simply by evaluating all
* alternatives.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 23.06.2005
* Time: 14:48:35
* To change this template use File | Settings | File Templates.
*/
@Description("Oldy but goldy: K-Means clustering.")
public class ClusteringXMeans implements InterfaceClustering, java.io.Serializable {
public int maxK = 5;
@ -365,18 +362,6 @@ public class ClusteringXMeans implements InterfaceClustering, java.io.Serializab
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Oldy but goldy: K-Means clustering.";
}
/**
* This method will return a naming String
*

18
src/eva2/optimization/operator/cluster/InterfaceClustering.java
View File

@ -7,11 +7,6 @@ import eva2.optimization.population.Population;
* is no true concept on how to calculate a possibly problem
* specific distance between two individuals, this is still to
* be considered as under construction.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 24.04.2003
* Time: 15:13:36
* To change this template use Options | File Templates.
*/
public interface InterfaceClustering {
@ -66,19 +61,6 @@ public interface InterfaceClustering {
*/
public String initClustering(Population pop);
/**
* This method decides if an unclustered individual belongs to an already established species.
* For some clustering methods this can only be decided in reference to the complete population.
*
* @param indy A unclustered individual.
* @param species A species.
* @param pop The complete population as a reference.
* @return True or False.
*/
//Removed since for some clustering methods its not feasible to associate loners sequentially. Instead, a whole set of
// lone individuals can now be associated to a given set of clusters
//public boolean belongsToSpecies(AbstractEAIndividual indy, Population species);
/**
* Try to associate a set of loners with a given set of species. Return a list
* of indices assigning loner i with species j for all loners. If no species can

9
src/eva2/optimization/operator/constraint/ConstraintCollection.java
View File

@ -1,11 +1,12 @@
package eva2.optimization.operator.constraint;
import eva2.util.annotation.Description;
/**
* To handle a set of constraints with a single parameter adaption mechanism.
* Single constraints are
*
* @author mkron
*/
@Description("A set of constraints with a single parameter adaption mechanism.")
public class ConstraintCollection extends AbstractConstraint {
private AbstractConstraint[] constraintArray = new AbstractConstraint[]{};
@ -64,8 +65,4 @@ public class ConstraintCollection extends AbstractConstraint {
public String getName() {
return constraintArray.length + " constr./" + getPenaltyFactor() + "/" + getHandlingMethod() + "/" + getPenaltyFactControl().getClass().getSimpleName();
}
public static String globalInfo() {
return "A set of constraints with a single parameter adaption mechanism.";
}
}

8
src/eva2/optimization/operator/constraint/DummyConstraint.java
View File

@ -1,12 +1,12 @@
package eva2.optimization.operator.constraint;
import eva2.gui.editor.GenericObjectEditor;
import eva2.util.annotation.Description;
/**
* This constraint is always satisfied.
*
* @author mkron
*/
@Description("This constraint is always fulfilled.")
public class DummyConstraint extends AbstractConstraint {
@Override
@ -23,8 +23,4 @@ public class DummyConstraint extends AbstractConstraint {
protected double getRawViolationValue(double[] indyX) {
return 0;
}
public static String globalInfo() {
return "This constraint is always fulfilled.";
}
}

10
src/eva2/optimization/operator/constraint/GenericConstraint.java
View File

@ -3,15 +3,15 @@ package eva2.optimization.operator.constraint;
import eva2.optimization.individuals.codings.gp.AbstractGPNode;
import eva2.problems.GPFunctionProblem;
import eva2.tools.EVAERROR;
import eva2.util.annotation.Description;
import java.io.Serializable;
/**
* A generic constraint is defined by a String describing a function of the x0...xn values of potential solutions.
* The function String is parsed to a GP function tree using AbstractGPNode and GPFunctionProblem.
*
* @author mkron
*/
@Description("A generic constraint which is parsed from a String; n is dimension, x0..xn are solution components. Use prefix notation as in \"+(-(sum(x),n),sqrt(*(pi,x0)))\".")
public class GenericConstraint extends AbstractConstraint implements InterfaceDoubleConstraint, Serializable {
private String constraintString = "+(x0,x1)";
private transient AbstractGPNode constraintProgram = null;
@ -20,7 +20,6 @@ public class GenericConstraint extends AbstractConstraint implements InterfaceDo
public GenericConstraint() {
super();
constraintProgram = null;
// compileConstraint(constraintString);
}
public GenericConstraint(String str) {
@ -110,9 +109,4 @@ public class GenericConstraint extends AbstractConstraint implements InterfaceDo
public String getName() {
return this.getClass().getSimpleName() + " " + constraintString;
}
public static String globalInfo() {
return "A generic constraint which is parsed from a String; n is dimension, x0..xn are solution components. Use prefix notation as in \"+(-(sum(x),n),sqrt(*(pi,x0)))\".";
}
}

9
src/eva2/optimization/operator/constraint/ImplicitConstraint.java
View File

@ -1,12 +1,13 @@
package eva2.optimization.operator.constraint;
import eva2.util.annotation.Description;
/**
* A constraint that is already calculated by the fitness function as an
* independent criterion. This class can be used to transform it into
* a fitness penalty.
*
* @author mkron
*/
@Description("Similar to a multi-objective translation into fitness, this class allows to interpret fitness criteria as constraints.")
public class ImplicitConstraint extends AbstractConstraint {
int index = 0;
@ -35,10 +36,6 @@ public class ImplicitConstraint extends AbstractConstraint {
return "ImplicitCnstr-" + index;
}
public static String globalInfo() {
return "Similar to a multi-objective translation into fitness, this class allows to interpret fitness criteria as constraints.";
}
public int getIndex() {
return index;
}

17
src/eva2/optimization/operator/distancemetric/DoubleIntegralMetric.java
View File

@ -3,6 +3,7 @@ package eva2.optimization.operator.distancemetric;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.tools.EVAERROR;
import eva2.tools.math.Mathematics;
import eva2.util.annotation.Description;
import java.io.Serializable;
@ -12,12 +13,11 @@ import java.io.Serializable;
* <p/>
* This can be used with any individual type that implements getDoublePositionShallow.
*
* @author mkron
* @see AbstractEAIndividual.getDoublePositionShallow(AbstractEAIndividual)
* @see AbstractEAIndividual#getDoublePositionShallow(AbstractEAIndividual)
*/
@Description("A simple integral metric for real-valued types.")
public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializable {
boolean oneNormed = true; // if true, the vectors are normed to unity sum before comparison.
// String indyDataKey=null;
public DoubleIntegralMetric() {
}
@ -34,13 +34,6 @@ public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializab
@Override
public double distance(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
double[] dIndy1 = null, dIndy2 = null;
// String indyDataKey = ParticleSwarmOptimization.partBestPosKey;
// if (indyDataKey!=null && (indyDataKey.length()>0)) {
// try {
// dIndy1 = (double[]) indy1.getData(indyDataKey);
// dIndy2 = (double[]) indy2.getData(indyDataKey);
// } catch (Exception e) {dIndy1=null;}
// } else dIndy1=null;
if (dIndy1 == null || dIndy2 == null) {
dIndy1 = AbstractEAIndividual.getDoublePositionShallow(indy1);
@ -76,10 +69,6 @@ public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializab
return "Real-valued integral metric";
}
public static String globalInfo() {
return "A simple integral metric for real-valued types.";
}
public boolean isOneNormed() {
return oneNormed;
}

10
src/eva2/optimization/operator/distancemetric/EuclideanMetric.java
View File

@ -2,6 +2,7 @@ package eva2.optimization.operator.distancemetric;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.individuals.InterfaceDataTypeDouble;
import eva2.util.annotation.Description;
import java.io.Serializable;
@ -10,8 +11,9 @@ import java.io.Serializable;
* as given by AbstractEAIndividual.getDoublePosition(AbstractEAIndividual).
*
* @author mkron
* @see AbstractEAIndividual.getDoublePosition(AbstractEAIndividual)
* @see AbstractEAIndividual#getDoublePosition(AbstractEAIndividual)
*/
@Description("Set to true to norm the distance by the double range - only possible with InterfaceDataTypeDouble individuals.")
public class EuclideanMetric implements InterfaceDistanceMetric, Serializable {
private boolean normedByDblRange = false;
@ -93,7 +95,7 @@ public class EuclideanMetric implements InterfaceDistanceMetric, Serializable {
* @return description
*/
public static String globalInfo() {
return "The euclidean metric calculates euclidian distances for individuals which have a real valued interpretation.";
return "The euclidean metric calculates euclidean distances for individuals which have a real valued interpretation.";
}
/**
@ -112,9 +114,5 @@ public class EuclideanMetric implements InterfaceDistanceMetric, Serializable {
public boolean isNormedByDblRange() {
return normedByDblRange;
}
public String normedByDblRangeTipText() {
return "Set to true to norm the distance by the double range - only possible with InterfaceDataTypeDouble individuals.";
}
}

18
src/eva2/optimization/operator/distancemetric/GenotypeMetricBitSet.java
View File

@ -3,17 +3,14 @@ package eva2.optimization.operator.distancemetric;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.individuals.InterfaceGAIndividual;
import eva2.util.annotation.Description;
import java.util.BitSet;
/**
* Distance based on a bit-set if any.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 27.04.2003
* Time: 18:21:01
* To change this template use Options | File Templates.
*/
@Description("This is a genotype based distance metric suited for binary data. The hamming distance is computed and normalized by chromosome length.")
public class GenotypeMetricBitSet implements InterfaceDistanceMetric, java.io.Serializable {
public GenotypeMetricBitSet() {
@ -63,17 +60,6 @@ public class GenotypeMetricBitSet implements InterfaceDistanceMetric, java.io.Se
}
return result / (double) length;
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a genotype based distance metric suited for binary data. The hamming distance is computed and normalized by chromosome length.";
}
/**
* This method will return a naming String

14
src/eva2/optimization/operator/distancemetric/IndividualDataMetric.java
View File

@ -3,6 +3,7 @@ package eva2.optimization.operator.distancemetric;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.strategies.ParticleSwarmOptimization;
import eva2.tools.EVAERROR;
import eva2.util.annotation.Description;
import java.io.Serializable;
@ -10,9 +11,8 @@ import java.io.Serializable;
* Define a metric on data stored within individuals, such as the personal best position
* in PSO. The metric tries to set the stored data as double position to an indy clone.
* On these clones, the base metric is applied.
*
* @author mkron
*/
@Description("Uses individual object data (so far only double[]) to calculate the distance.")
public class IndividualDataMetric implements InterfaceDistanceMetric, Serializable {
private String dataKey = ParticleSwarmOptimization.partBestPosKey;
// private boolean normedDistance = true; // flag whether to use normed distances (for InterfaceDataTypeDouble)
@ -89,16 +89,6 @@ public class IndividualDataMetric implements InterfaceDistanceMetric, Serializab
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 static String globalInfo() {
return "Uses individual object data (so far only double[]) to calculate the distance.";
}
public void setBaseMetric(InterfaceDistanceMetric baseMetric) {
this.baseMetric = baseMetric;

18
src/eva2/optimization/operator/distancemetric/ObjectiveSpaceMetric.java
View File

@ -1,15 +1,12 @@
package eva2.optimization.operator.distancemetric;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.util.annotation.Description;
/**
* Objective space metric suited for multi-objective optimization.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 04.08.2003
* Time: 14:39:47
* To change this template use Options | File Templates.
*/
@Description("The objective space metric calculates euclidian distances on the fitness vectors.")
public class ObjectiveSpaceMetric implements InterfaceDistanceMetric, java.io.Serializable {
public ObjectiveSpaceMetric() {
@ -45,17 +42,6 @@ public class ObjectiveSpaceMetric implements InterfaceDistanceMetric, java.io.Se
}
return Math.sqrt(result);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The objective space metric calculates euclidian distances on the fitness vectors.";
}
/**
* This method will return a naming String

17
src/eva2/optimization/operator/distancemetric/PhenotypeMetric.java
View File

@ -3,15 +3,14 @@ package eva2.optimization.operator.distancemetric;
import eva2.gui.BeanInspector;
import eva2.optimization.individuals.*;
import eva2.util.annotation.Description;
import java.util.BitSet;
/**
* A phenotype metric suited for the most common data types.
* <p/>
* User: streiche
* Date: 19.07.2005
*/
@Description("This is a phenotype based metric which can be applied to binary, integer, double, permutation, and program data types. For the latter two, the Levenshtein distance is computed. All distance values are normed.")
public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Serializable {
private static PhenotypeMetric pMetric = null;
private static GenotypeMetricBitSet bitMetric = null;
@ -223,18 +222,6 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
return Math.sqrt(result);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a phenotype based metric which can be applied to binary, integer, double, permutation, and program data types. For the latter two, the Levenshtein distance is computed. All distance values are normed.";
}
/**
* This method will return a naming String
*

18
src/eva2/optimization/operator/distancemetric/SigmaSingleMetricGlobalMutation.java
View File

@ -3,6 +3,7 @@ package eva2.optimization.operator.distancemetric;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.individuals.InterfaceDataTypeDouble;
import eva2.optimization.operator.mutation.MutateESGlobal;
import eva2.util.annotation.Description;
/**
* This method includes the sigma as distance element.
@ -10,12 +11,8 @@ import eva2.optimization.operator.mutation.MutateESGlobal;
* at least on real-valued search spaces together
* with the correct mutation operator. I guess this
* could be a paper, but I'm so lazy right now.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 23.04.2004
* Time: 12:48:26
* To change this template use File | Settings | File Templates.
*/
@Description("This is an experimental method for individuals using global ES mutation.")
public class SigmaSingleMetricGlobalMutation implements InterfaceDistanceMetric, java.io.Serializable {
public SigmaSingleMetricGlobalMutation() {
@ -66,17 +63,6 @@ public class SigmaSingleMetricGlobalMutation implements InterfaceDistanceMetric,
return 1.0;
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is an experimental method for individuals using global ES mutation.";
}
/**
* This method will return a naming String

14
src/eva2/optimization/operator/fitnessmodifier/FitnessAdaptiveClustering.java
View File

@ -4,11 +4,13 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.cluster.ClusteringDensityBased;
import eva2.optimization.operator.cluster.InterfaceClustering;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* The fitness modifier are defunct and are to be moved to
* the selection operators...
*/
@Description("This is a normation method based on Fitness Sharing. It adds a penalty for too similar individuals on the standard Normation method.")
public class FitnessAdaptiveClustering implements java.io.Serializable, InterfaceFitnessModifier {
private InterfaceClustering clusteringAlgorithm = new ClusteringDensityBased();
@ -61,18 +63,6 @@ public class FitnessAdaptiveClustering implements java.io.Serializable, Interfac
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a normation method based on Fitness Sharing. It adds a penalty for too similar individuals on the standard Normation method.";
}
/**
* This method allows you to set/get the clustering method on which the
* species convergence is based.

19
src/eva2/optimization/operator/fitnessmodifier/FitnessModifierNone.java
View File

@ -1,16 +1,13 @@
package eva2.optimization.operator.fitnessmodifier;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* The fitness modifier are defunct and are to be moved to
* the selection operators...
* Created by IntelliJ IDEA.
* User: streiche
* Date: 30.03.2004
* Time: 17:43:49
* To change this template use File | Settings | File Templates.
*/
@Description("With this method the fitness remains unaltered.")
public class FitnessModifierNone implements java.io.Serializable, InterfaceFitnessModifier {
/**
@ -24,9 +21,6 @@ public class FitnessModifierNone implements java.io.Serializable, InterfaceFitne
// as the name might suggest this guy is pretty lazy
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -36,14 +30,5 @@ public class FitnessModifierNone implements java.io.Serializable, InterfaceFitne
public String getName() {
return "No Fitness Modification";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "With this method the fitness remains unaltered.";
}
}

14
src/eva2/optimization/operator/fitnessmodifier/FitnessSharing.java
View File

@ -4,11 +4,13 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.distancemetric.InterfaceDistanceMetric;
import eva2.optimization.operator.distancemetric.PhenotypeMetric;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* The fitness modifier are defunct and are to be moved to
* the selection operators...
*/
@Description("This is a normation method based on Fitness Sharing. It adds a penalty for too similar individuals on the standard Normation method.")
public class FitnessSharing implements java.io.Serializable, InterfaceFitnessModifier {
private double sharingDistance = 0.05;
@ -64,18 +66,6 @@ public class FitnessSharing implements java.io.Serializable, InterfaceFitnessMod
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a normation method based on Fitness Sharing. It adds a penalty for too similar individuals on the standard Normation method.";
}
/**
* These methods allows you to set/get the Sharing Distance
*

9
src/eva2/optimization/operator/initialization/DefaultInitialization.java
View File

@ -2,14 +2,13 @@ package eva2.optimization.operator.initialization;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.util.annotation.Description;
/**
* A dummy initialization method which only calls the default initialize method of the individual.
*
* @author mkron
*/
@Description("Uses the standard initialization of the individual implementation")
public class DefaultInitialization implements InterfaceInitialization, java.io.Serializable {
private static final long serialVersionUID = 1L;
public DefaultInitialization() {
}
@ -27,8 +26,4 @@ public class DefaultInitialization implements InterfaceInitialization, java.io.S
public String getName() {
return "DefaultInitialization";
}
public String globalInfo() {
return "Uses the standard initialization of the individual implementation";
}
}

21
src/eva2/optimization/operator/initialization/GAGIInitializeSegmentwise.java
View File

@ -6,6 +6,7 @@ import eva2.optimization.individuals.InterfaceGIIndividual;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.EVAERROR;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.BitSet;
@ -18,7 +19,7 @@ import java.util.BitSet;
* integer individuals, it allows to control the number of occurrences of a certain integer
* per segment. It may also be used to initialize with subsets of integers (by setting 0
* elements to a certain type and all to a subset of the range).
* <p/>
* </p><p>
* The initialization may be parameterized in two ways, where each takes a fixed
* segment length s. Firstly, a fixed number of bits (k<=s) is set per segment,
* so each segment has equal cardinality.
@ -26,9 +27,9 @@ import java.util.BitSet;
* each segment i, so different segments may have different cardinality. The array
* must comply to the binary genotype length of the problem. The array definition
* has strict priority over the fixed cardinality definition.
*
* @author mkron
*/
@Description("A method which initializes with a fixed number of occurences per segment, which is a fixed-length" +
" substring of equal length. In the binary case, thus the cardinality of each segment can be predefined.")
public class GAGIInitializeSegmentwise implements InterfaceInitialization, java.io.Serializable {
private static final long serialVersionUID = 1L;
protected int[] bitsPerSegmentArray = new int[0];
@ -93,14 +94,6 @@ public class GAGIInitializeSegmentwise implements InterfaceInitialization, java.
public InterfaceInitialization clone() {
return new GAGIInitializeSegmentwise(this);
}
//
// public static void main(String[] args) {
// GAGIInitializeSegmentwise initialize = new GAGIInitializeSegmentwise(5, new int[]{5,0}, 0.5);
// GIIndividualIntegerData indy = new GIIndividualIntegerData();
// indy.setInitOperator(initialize);
// indy.initialize(null);
// System.out.println(indy.getStringRepresentation());
// }
@Override
public void initialize(AbstractEAIndividual indy,
@ -287,16 +280,10 @@ public class GAGIInitializeSegmentwise implements InterfaceInitialization, java.
return "The fixed length of a segment, which is a substring of the binary genotype";
}
////////
public String getName() {
return "GA-GI segment-wise initialize";
}
public static String globalInfo() {
return "A method which initializes with a fixed number of occurences per segment, which is a fixed-length" +
" substring of equal length. In the binary case, thus the cardinality of each segment can be predefined.";
}
public int getTargetElement() {
return targetElement;
}

18
src/eva2/optimization/operator/mutation/MutateDefault.java
View File

@ -4,11 +4,13 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.individuals.IndividualInterface;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.util.annotation.Description;
/**
* Mutate individuals using the default operator implemented by the individuals themselves.
*/
@Description("The default mutation just uses the default method implemented in the individual.")
public class MutateDefault implements InterfaceMutation, java.io.Serializable {
/**
@ -51,9 +53,7 @@ public class MutateDefault implements InterfaceMutation, java.io.Serializable {
*/
@Override
public void mutate(AbstractEAIndividual individual) {
if (individual instanceof IndividualInterface) {
individual.defaultMutate();
}
individual.defaultMutate();
}
/**
@ -79,9 +79,6 @@ public class MutateDefault implements InterfaceMutation, java.io.Serializable {
return "Default mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -91,13 +88,4 @@ public class MutateDefault implements InterfaceMutation, java.io.Serializable {
public String getName() {
return "Default mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The default mutation just uses the default method implemented in the individual.";
}
}

14
src/eva2/optimization/operator/mutation/MutateEAMixer.java
View File

@ -5,6 +5,7 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
@ -12,6 +13,7 @@ import java.util.ArrayList;
/**
*
*/
@Description("This meta-mutation operator allows you to combine multiple alternative mutation operators.")
public class MutateEAMixer implements InterfaceMutation, java.io.Serializable {
private PropertyMutationMixer mutationMixer;
@ -180,9 +182,6 @@ public class MutateEAMixer implements InterfaceMutation, java.io.Serializable {
return "EA mutation mixer";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -193,15 +192,6 @@ public class MutateEAMixer implements InterfaceMutation, java.io.Serializable {
return "EA mutation mixer";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This meta-mutation operator allows you to combine multiple alternative mutation operators.";
}
/**
* Choose the set of mutators.
*

16
src/eva2/optimization/operator/mutation/MutateESCorrVector.java
View File

@ -6,6 +6,7 @@ import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.Mathematics;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
@ -15,12 +16,13 @@ import java.util.ArrayList;
* updates the velocity by rotation and scaling, and then mutates the individual
* by adding the velocity. This was used for a particle filter localization problem
* and is less useful in general.
* <p/>
* </p><p>
* Rotation vectors are normal distributed with mean zero, scaling factors are
* log-normally distributed around mean 1. This means that the averaged expected change
* of the mutation vector is zero. The smaller the deviations, the higher the correlations
* between successive mutation steps.
*/
@Description("The correlated vector mutation stores a specific mutation vector per individual.")
public class MutateESCorrVector implements InterfaceMutation, java.io.Serializable {
protected double scalingDev = 0.05;
protected double initialVelocity = 0.02;
@ -208,9 +210,6 @@ public class MutateESCorrVector implements InterfaceMutation, java.io.Serializab
return "ES global mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -221,15 +220,6 @@ public class MutateESCorrVector implements InterfaceMutation, java.io.Serializab
return "ES correlated vector mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The correlated vector mutation stores a specific mutation vector per individual.";
}
/**
* Set the initial mutation step size with this method.
*

19
src/eva2/optimization/operator/mutation/MutateESCorrolated.java
View File

@ -5,14 +5,11 @@ import eva2.optimization.individuals.InterfaceESIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 02.04.2003
* Time: 17:58:30
* To change this template use Options | File Templates.
*/
@Description("The local correlated mutation stores n sigmas for each double attribute and n(n-1) alphas.")
public class MutateESCorrolated implements InterfaceMutation, java.io.Serializable {
protected double mutationStepSize = 0.2;
protected double tau1 = 0.15;
@ -228,9 +225,6 @@ public class MutateESCorrolated implements InterfaceMutation, java.io.Serializab
return "ES local correlated mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -241,15 +235,6 @@ public class MutateESCorrolated implements InterfaceMutation, java.io.Serializab
return "ES local correlated mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The local correlated mutation stores n sigmas for each double attribute and n(n-1) alphas.";
}
/**
* Set the value for tau2 with this method.
*

16
src/eva2/optimization/operator/mutation/MutateESCovarianceMatrixAdaption.java
View File

@ -8,10 +8,11 @@ import eva2.tools.math.Jama.EigenvalueDecomposition;
import eva2.tools.math.Jama.Matrix;
import eva2.tools.math.Mathematics;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
*/
@Description("This is the most sophisticated CMA mutation.")
public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java.io.Serializable {
protected int D;
@ -307,9 +308,7 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
public String getStringRepresentation() {
return "CMA mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -320,15 +319,6 @@ public class MutateESCovarianceMatrixAdaption implements InterfaceMutation, java
return "CMA mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is the most sophisticated CMA mutation.";
}
/**
* Use only positive numbers this limits the freedom of effect.
*

11
src/eva2/optimization/operator/mutation/MutateESCovarianceMatrixAdaptionPlus.java
View File

@ -5,7 +5,9 @@ import eva2.optimization.individuals.InterfaceESIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.Jama.Matrix;
import eva2.util.annotation.Description;
@Description("This is the CMA mutation according to Igel,Hansen,Roth 2007")
public class MutateESCovarianceMatrixAdaptionPlus extends
MutateESCovarianceMatrixAdaption implements InterfaceMutation,
InterfaceAdaptOperatorGenerational {
@ -215,15 +217,6 @@ public class MutateESCovarianceMatrixAdaptionPlus extends
return "CMA mutation for plus Strategies";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is the CMA mutation according to Igel,Hansen,Roth 2007";
}
@Override
public String getStringRepresentation() {
// TODO Auto-generated method stub

19
src/eva2/optimization/operator/mutation/MutateESFixedStepSize.java
View File

@ -5,14 +5,11 @@ import eva2.optimization.individuals.InterfaceESIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 08.09.2004
* Time: 17:05:23
* To change this template use File | Settings | File Templates.
*/
@Description("The fixed step size mutation alters all elements with a fixed mutation step size.")
public class MutateESFixedStepSize implements InterfaceMutation, java.io.Serializable {
protected double sigma = 0.005;
@ -114,9 +111,6 @@ public class MutateESFixedStepSize implements InterfaceMutation, java.io.Seriali
return "ES fixed step size mutation " + getSigma();
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -127,15 +121,6 @@ public class MutateESFixedStepSize implements InterfaceMutation, java.io.Seriali
return "ES fixed step size mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The fixed step size mutation alters all elements with a fixed mutation step size.";
}
/**
* Set the value for Sigma with this method.
*

14
src/eva2/optimization/operator/mutation/MutateESGlobal.java
View File

@ -8,6 +8,7 @@ import eva2.optimization.population.PopulationInterface;
import eva2.problems.InterfaceAdditionalPopulationInformer;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
@ -15,6 +16,7 @@ import java.util.ArrayList;
/**
*
*/
@Description("The global mutation stores only one sigma for all double attributes.")
public class MutateESGlobal implements InterfaceMutation, java.io.Serializable, InterfaceAdditionalPopulationInformer {
protected double mutationStepSize = 0.2;
protected double tau1 = 0.15;
@ -179,9 +181,6 @@ public class MutateESGlobal implements InterfaceMutation, java.io.Serializable,
return "ES global mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -192,15 +191,6 @@ public class MutateESGlobal implements InterfaceMutation, java.io.Serializable,
return "ES global mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The global mutation stores only one sigma for all double attributes.";
}
/**
* Set the initial mutation step size with this method.
*

14
src/eva2/optimization/operator/mutation/MutateESLocal.java
View File

@ -10,12 +10,14 @@ import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.SelectedTag;
import eva2.tools.Tag;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
/**
*
*/
@Description("The local mutation stores n sigmas for each double attribute.")
public class MutateESLocal implements InterfaceMutation, InterfaceAdditionalPopulationInformer, java.io.Serializable {
protected double mutationStepSize = 0.2;
@ -217,9 +219,6 @@ public class MutateESLocal implements InterfaceMutation, InterfaceAdditionalPopu
return "ES local mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -230,15 +229,6 @@ public class MutateESLocal implements InterfaceMutation, InterfaceAdditionalPopu
return "ES local mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The local mutation stores n sigmas for each double attribute.";
}
/**
* Set the initial mutation step size with this method.
*

15
src/eva2/optimization/operator/mutation/MutateESMainVectorAdaption.java
View File

@ -6,10 +6,12 @@ import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.Mathematics;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
*
*/
@Description("This is the most sophisticated MVA mutation.")
public class MutateESMainVectorAdaption implements InterfaceMutation, java.io.Serializable {
private boolean checkConstraints = true;
@ -242,9 +244,7 @@ public class MutateESMainVectorAdaption implements InterfaceMutation, java.io.Se
public String getStringRepresentation() {
return "MVA mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -255,15 +255,6 @@ public class MutateESMainVectorAdaption implements InterfaceMutation, java.io.Se
return "MVA mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is the most sophisticated MVA mutation.";
}
/**
* Use only positive numbers this limits the freedom of effect.
*

11
src/eva2/optimization/operator/mutation/MutateESMutativeStepSizeControl.java
View File

@ -5,10 +5,12 @@ import eva2.optimization.individuals.InterfaceESIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
*
*/
@Description("The mutative step size control mutation randomly increases/decreases the current step size.")
public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.io.Serializable {
protected double mutationStepSize = 0.2;
protected double alpha = 1.2;
@ -161,15 +163,6 @@ public class MutateESMutativeStepSizeControl implements InterfaceMutation, java.
return "MSR";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The mutative step size control mutation randomly increases/decreases the current step size.";
}
/**
* Set the initial mutation step size with this method.
*

28
src/eva2/optimization/operator/mutation/MutateESPathLengthAdaption.java
View File

@ -6,6 +6,7 @@ import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.Mathematics;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* ES mutation with path length control. The step size (single sigma) is
@ -13,6 +14,7 @@ import eva2.tools.math.RNG;
* to the expected path length in for uncorrelated single steps.
* See Hansen&Ostermeier 2001, Eqs. 16,17.
*/
@Description("The single step size is controlled using the evolution path.")
public class MutateESPathLengthAdaption implements InterfaceMutation, java.io.Serializable {
private int dim;
@ -217,9 +219,7 @@ public class MutateESPathLengthAdaption implements InterfaceMutation, java.io.Se
public String getStringRepresentation() {
return "Mutation/Path-Length-Control";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -230,28 +230,6 @@ public class MutateESPathLengthAdaption implements InterfaceMutation, java.io.Se
return "Mutation/Path-Length-Control";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The single step size is controlled using the evolution path.";
}
// /** Use only positive numbers this limits the freedom of effect.
// * @param bit The new representation for the inner constants.
// */
// public void setUsePath(boolean bit) {
// this.usePath = bit;
// }
// public boolean getUsePath() {
// return this.usePath;
// }
// public String usePathTipText() {
// return "Use path.";
// }
/**
* This method allows you to set the initial sigma value.
*

14
src/eva2/optimization/operator/mutation/MutateESPolynomial.java
View File

@ -5,10 +5,12 @@ import eva2.optimization.individuals.InterfaceESIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
*
*/
@Description("The polynomial mutation alters all elements according to a polynomial distribution")
public class MutateESPolynomial implements InterfaceMutation, java.io.Serializable {
private double eta = 0.2;
@ -119,9 +121,6 @@ public class MutateESPolynomial implements InterfaceMutation, java.io.Serializab
return "ES fixed step size mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -132,15 +131,6 @@ public class MutateESPolynomial implements InterfaceMutation, java.io.Serializab
return "ES polynomial mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public String globalInfo() {
return "The polynomial mutation alters all elements according to a polynomial distribution";
}
/**
* This method allows you to set the number of crossovers that occur in the
* genotype.

8
src/eva2/optimization/operator/mutation/MutateESRankMuCMA.java
View File

@ -14,6 +14,7 @@ import eva2.tools.Pair;
import eva2.tools.math.Jama.EigenvalueDecomposition;
import eva2.tools.math.Mathematics;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.io.Serializable;
@ -28,9 +29,8 @@ import java.io.Serializable;
* in the populations, so that in principle, multi-modal optimization with several populations becomes possible.
* This of course requires proper handling of the generational cycle, i.e., new generations should be cloned from
* the former ones (without individuals is ok) so that the parameters are taken over.
*
* @author mkron
*/
@Description("The CMA mutator scheme with static cov. matrix, rank-mu update and weighted recombination.")
public class MutateESRankMuCMA implements InterfaceAdaptOperatorGenerational, InterfaceMutation, Serializable {
// int dim;
private double c_c, expRandStepLen;
@ -518,10 +518,6 @@ public class MutateESRankMuCMA implements InterfaceAdaptOperatorGenerational, In
return "Rank-Mu-CMA-Mutator";
}
public static String globalInfo() {
return "The CMA mutator scheme with static cov. matrix, rank-mu update and weighted recombination.";
}
@Override
public void init(AbstractEAIndividual individual,
InterfaceOptimizationProblem opt) {

17
src/eva2/optimization/operator/mutation/MutateESStandard.java
View File

@ -10,12 +10,8 @@ package eva2.optimization.operator.mutation;
//import eva2.tools.math.RNG;
//
///**
// * Created by IntelliJ IDEA.
// * User: streiche
// * Date: 15.05.2003
// * Time: 17:04:24
// * To change this template use Options | File Templates.
// */
//@Description("The standard mutation alters all elements of the double attributes with a fixed mutation step size.")
//public class MutateESStandard implements InterfaceMutation, java.io.Serializable {
// protected double mutationStepSize = 0.1;
// public MutateESStandard() {
@ -88,10 +84,7 @@ package eva2.optimization.operator.mutation;
// public String getStringRepresentation() {
// return "ES standard mutation";
// }
//
///**********************************************************************************************************************
// * These are for GUI
// */
// /** This method allows the CommonJavaObjectEditorPanel to read the
// * name to the current object.
// * @return The name.
@ -99,12 +92,6 @@ package eva2.optimization.operator.mutation;
// public String getName() {
// return "ES standard mutation ";
// }
// /** This method returns a global info string
// * @return description
// */
// public static String globalInfo() {
// return "The standard mutation alters all elements of the double attributes with a fixed mutation step size.";
// }
//
// /** This method allows you to set the fixed mutation step size
// * @param step The new mutation step size

11
src/eva2/optimization/operator/mutation/MutateESSuccessRule.java
View File

@ -2,10 +2,12 @@ package eva2.optimization.operator.mutation;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* Success rule implementation.
*/
@Description("The 1/5 success rule works only together with an ES optimizer.")
public class MutateESSuccessRule extends MutateESFixedStepSize implements InterfaceMutation, InterfaceAdaptOperatorGenerational, java.io.Serializable {
protected double successRate = 0.2;
protected double alpha = 1.2;
@ -90,15 +92,6 @@ public class MutateESSuccessRule extends MutateESFixedStepSize implements Interf
return "ES 1/5 Success Rule mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The 1/5 success rule works only together with an ES optimizer.";
}
public String mutationStepSizeTipText() {
return "Choose the initial mutation step size.";
}

14
src/eva2/optimization/operator/mutation/MutateGAAdaptive.java
View File

@ -6,6 +6,7 @@ import eva2.optimization.individuals.InterfaceGAIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.BitSet;
@ -13,6 +14,7 @@ import java.util.BitSet;
* The mutation probability is adapted using a parameter tau and stored in the individual.
* Better mutation probabilities are selected indirectly as they produce better offspring.
*/
@Description("The standard mutation switches n bits of the GA genotype.")
public class MutateGAAdaptive implements InterfaceMutation, java.io.Serializable {
protected double mutationStep = 1;
@ -120,9 +122,6 @@ public class MutateGAAdaptive implements InterfaceMutation, java.io.Serializable
return "GA adaptive mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -133,15 +132,6 @@ public class MutateGAAdaptive implements InterfaceMutation, java.io.Serializable
return "GA adaptive mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The standard mutation switches n bits of the GA genotype.";
}
/**
* Set the initial mutation step size with this method.
*

18
src/eva2/optimization/operator/mutation/MutateGAGISwapBits.java
View File

@ -6,6 +6,7 @@ import eva2.optimization.individuals.InterfaceGIIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.BitSet;
@ -15,12 +16,9 @@ import java.util.BitSet;
* individual length).
* A range of number of par mutations can be given from which the actual number of pairs
* is drawn in a uniform way.
* <p/>
* User: streiche, mkron
* Date: 05.08.2004
* Time: 17:45:36
* To change this template use File | Settings | File Templates.
*/
@Description("This mutation operator swaps n random position pairs (bits or integers). The number of mutations is" +
" chosen uniformly in a given interval.")
public class MutateGAGISwapBits implements InterfaceMutation, java.io.Serializable {
private int minNumMutations = 1;
private int maxNumMutations = 3;
@ -221,16 +219,6 @@ public class MutateGAGISwapBits implements InterfaceMutation, java.io.Serializab
return "GA-GI swap bits mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This mutation operator swaps n random position pairs (bits or integers). The number of mutations is" +
" chosen uniformly in a given interval.";
}
/**
* This method allows you to set the number of mutations that occur in the
* genotype.

10
src/eva2/optimization/operator/mutation/MutateGAGISwapBitsSegmentwise.java
View File

@ -1,15 +1,16 @@
package eva2.optimization.operator.mutation;
import eva2.tools.EVAERROR;
import eva2.util.annotation.Description;
import java.io.Serializable;
/**
* This implementation restricts the swap positions of the standard swapping mutation
* to swaps within subsequences (segments) of the genotype. The segments have a fixed length.
*
* @author mkron
*/
@Description("Segment-wise swapping of elements - the mutation pairs are selected within the same" +
" sub-sequence of the genotype.")
public class MutateGAGISwapBitsSegmentwise extends MutateGAGISwapBits implements Serializable {
private int segmentLength = 8;
@ -37,11 +38,6 @@ public class MutateGAGISwapBitsSegmentwise extends MutateGAGISwapBits implements
return new MutateGAGISwapBitsSegmentwise(this);
}
public static String globalInfo() {
return "Segment-wise swapping of elements - the mutation pairs are selected within the same" +
" subsequence of the genotype.";
}
@Override
public String getName() {
return "GA-GI swap bits segment-wise mutation";

11
src/eva2/optimization/operator/mutation/MutateGAInvertBits.java
View File

@ -5,12 +5,14 @@ import eva2.optimization.individuals.InterfaceGAIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.BitSet;
/**
*
*/
@Description("This mutation operator inverts n successive bits.")
public class MutateGAInvertBits implements InterfaceMutation, java.io.Serializable {
private int numberOfMutations = 1;
@ -126,15 +128,6 @@ public class MutateGAInvertBits implements InterfaceMutation, java.io.Serializab
return "GA invert n bits mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This mutation operator inverts n successive bits.";
}
/**
* This method allows you to set the number of mutations that occur in the
* genotype.

15
src/eva2/optimization/operator/mutation/MutateGANBit.java
View File

@ -5,12 +5,14 @@ import eva2.optimization.individuals.InterfaceGAIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.BitSet;
/**
*
*/
@Description("Switch n bits of the GA genotype.")
public class MutateGANBit implements InterfaceMutation, java.io.Serializable {
private int numberOfMutations = 1;
@ -104,10 +106,6 @@ public class MutateGANBit implements InterfaceMutation, java.io.Serializable {
return "GA n-Bit mutation (n=" + this.numberOfMutations + ")";
}
/**
* ********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the name to
* the current object.
@ -118,15 +116,6 @@ public class MutateGANBit implements InterfaceMutation, java.io.Serializable {
return "GA n-Bit mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Switch n bits of the GA genotype.";
}
/**
* This method allows you to set the number of mutations that occur in the
* genotype.

16
src/eva2/optimization/operator/mutation/MutateGAShiftSubstring.java
View File

@ -6,15 +6,15 @@ import eva2.optimization.individuals.InterfaceGAIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.BitSet;
/**
* Shift a certain substring within the individual. The length and shift distance
* can be predefined or will be selected at random. The shift is performed cyclic.
*
* @author mkron
*/
@Description("This mutation operator shifts n successive along the genotype (cyclic).")
public class MutateGAShiftSubstring implements InterfaceMutation, java.io.Serializable {
private int subStringLength = 0;
@ -145,9 +145,6 @@ public class MutateGAShiftSubstring implements InterfaceMutation, java.io.Serial
return "GA inversion mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -158,15 +155,6 @@ public class MutateGAShiftSubstring implements InterfaceMutation, java.io.Serial
return "GA shift bitstring mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This mutation operator shifts n successive along the genotype (cyclic).";
}
public int getSubStringLength() {
return subStringLength;
}

25
src/eva2/optimization/operator/mutation/MutateGASwapBitsSegmentwise.java
View File

@ -7,6 +7,7 @@ import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.EVAERROR;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.BitSet;
@ -19,12 +20,9 @@ import java.util.BitSet;
* that further mutations are performed recursively with p_mut. Thus, the probability
* to perform k mutations per segment is (p_mut)^k. However, more than s mutations per segment will
* never be performed.
* <p/>
* User: mkron
* Date: 05.08.2004
* Time: 17:45:36
* To change this template use File | Settings | File Templates.
*/
@Description("This mutation operator swaps bits in sub-segments of the genotype. Each segment is mutated" +
" with a certain probability. Depending on the setting, multiple mutations per segment may occur.")
public class MutateGASwapBitsSegmentwise implements InterfaceMutation, java.io.Serializable {
private double mutationProbPerSegment = 0.7;
private boolean multiplesPerSegment = false;
@ -151,9 +149,9 @@ public class MutateGASwapBitsSegmentwise implements InterfaceMutation, java.io.S
/**
* Swap one pair of bits within an indicated segment.
*
* @param tmpBitSet
* @param bs
* @param i
* @param segmentLength2
* @param segLen
*/
private void swapBitsInSegmentAt(BitSet bs, int i, int segLen) {
int indexOne = getRandomIndex(bs, i, segLen, true); // may prefer true bits
@ -193,9 +191,6 @@ public class MutateGASwapBitsSegmentwise implements InterfaceMutation, java.io.S
return "GA swap bits segment-wise mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -206,16 +201,6 @@ public class MutateGASwapBitsSegmentwise implements InterfaceMutation, java.io.S
return "GA swap bits segment-wise mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This mutation operator swaps bits in subsegments of the genotype. Each segment is mutated" +
" with a certain probability. Depending on the setting, multiple mutations per segment may occur.";
}
public void setPreferTrueChange(boolean preferPairs) {
this.preferPairs = preferPairs;
}

8
src/eva2/optimization/operator/mutation/MutateGAUniform.java
View File

@ -7,14 +7,14 @@ import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.EVAERROR;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.io.Serializable;
/**
* Uniform mutation mutates every GA bit with a fixed probability.
*
* @author mkron
*/
@Description("Uniform mutation mutates every GA bit with a fixed probability.")
public class MutateGAUniform implements InterfaceMutation, Serializable {
double bitwiseProb = 0.1;
private boolean useInvertedLength = true;
@ -108,8 +108,4 @@ public class MutateGAUniform implements InterfaceMutation, Serializable {
public String getName() {
return "Uniform-GA-Mutation";
}
public static String globalInfo() {
return "Uniform mutation mutates every GA bit with a fixed probability.";
}
}

19
src/eva2/optimization/operator/mutation/MutateGIInsertDelete.java
View File

@ -5,14 +5,11 @@ import eva2.optimization.individuals.InterfaceGIIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 30.05.2005
* Time: 17:33:46
* To change this template use File | Settings | File Templates.
*/
@Description("This mutation operator inserts or adds elements to the array.")
public class MutateGIInsertDelete implements InterfaceMutation, java.io.Serializable {
int maxLengthOfInsDel = 2;
@ -153,9 +150,6 @@ public class MutateGIInsertDelete implements InterfaceMutation, java.io.Serializ
return "GI insert/delete mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -166,15 +160,6 @@ public class MutateGIInsertDelete implements InterfaceMutation, java.io.Serializ
return "GI insert/delete mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This mutation operator inserts or adds elements to the array.";
}
/**
* This method allows you to set the max length of the insert or deletion.
*

19
src/eva2/optimization/operator/mutation/MutateGIInvert.java
View File

@ -5,14 +5,11 @@ import eva2.optimization.individuals.InterfaceGIIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 19.05.2005
* Time: 16:15:20
* To change this template use File | Settings | File Templates.
*/
@Description("The invert mutation inverts a segment of the int[].")
public class MutateGIInvert implements InterfaceMutation, java.io.Serializable {
int maxLengthOfInvert = 2;
@ -123,9 +120,6 @@ public class MutateGIInvert implements InterfaceMutation, java.io.Serializable {
return "GI invert mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -136,15 +130,6 @@ public class MutateGIInvert implements InterfaceMutation, java.io.Serializable {
return "GI invert mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The invert mutation inverts a segment of the int[].";
}
/**
* This method allows you to set the max length of invert.
*

19
src/eva2/optimization/operator/mutation/MutateGINominal.java
View File

@ -5,14 +5,11 @@ import eva2.optimization.individuals.InterfaceGIIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 19.05.2005
* Time: 16:02:32
* To change this template use File | Settings | File Templates.
*/
@Description("The nominal mutation alters n element of the int attributes completely at random.")
public class MutateGINominal implements InterfaceMutation, java.io.Serializable {
int numberOfMutations = 2;
@ -109,9 +106,6 @@ public class MutateGINominal implements InterfaceMutation, java.io.Serializable
return "GI nominal mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -122,15 +116,6 @@ public class MutateGINominal implements InterfaceMutation, java.io.Serializable
return "GI nominal mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The nominal mutation alters n element of the int attributes completely at random.";
}
/**
* This method allows you to set the number of mutations to occur.
*

19
src/eva2/optimization/operator/mutation/MutateGIOrdinal.java
View File

@ -5,14 +5,11 @@ import eva2.optimization.individuals.InterfaceGIIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 19.05.2005
* Time: 15:53:03
* To change this template use File | Settings | File Templates.
*/
@Description("The ordinal mutation alters n element of the int attributes based on an ordinal ordering.")
public class MutateGIOrdinal implements InterfaceMutation, java.io.Serializable {
double stepSize = 0.1;
@ -128,9 +125,6 @@ public class MutateGIOrdinal implements InterfaceMutation, java.io.Serializable
return "GI ordinal mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -141,15 +135,6 @@ public class MutateGIOrdinal implements InterfaceMutation, java.io.Serializable
return "GI ordinal mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The ordinal mutation alters n element of the int attributes based on an ordinal ordering.";
}
/**
* This method allows you to set the mean step size.
*

16
src/eva2/optimization/operator/mutation/MutateGISubset.java
View File

@ -7,14 +7,18 @@ import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.EVAERROR;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.io.Serializable;
/**
* An integer mutation operator which switches elements within a given subset only.
*
* @author mkron
*/
@Description("A mutation operator which switches positions within a given subset only. A random " +
"position is chosen but mutated only if its allele is contained" +
" in the mutable set. The new allele is chosen from this set as well." +
" In case the random positions do not contain a mutable allele, the switching is skipped. " +
"This means that fewer switches may occur than expected from the minimal bound.")
public class MutateGISubset implements InterfaceMutation, Serializable {
private int[] mutableSet = new int[]{0, 1};
private int minNumMutations = 1;
@ -124,14 +128,6 @@ public class MutateGISubset implements InterfaceMutation, Serializable {
return false;
}
public static String globalInfo() {
return "A mutation operator which switches positions within a given subset only. A random " +
"position is chosen but mutated only if its allele is contained" +
" in the mutable set. The new allele is chosen from this set as well." +
" In case the random positions do not contain a mutable allele, the switching is skipped. " +
"This means that fewer switches may occur than expected from the minimal bound.";
}
public int[] getMutableSet() {
return mutableSet;
}

16
src/eva2/optimization/operator/mutation/MutateGITranslocate.java
View File

@ -5,15 +5,15 @@ import eva2.optimization.individuals.InterfaceGIIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Mutate an integer individual by shifting a connected subsequence within the genotype. The sequence
* length is chosen uniformly randomly up to an upper limit. The destination position may either be
* fully randomly or also limited to a maximal distance.
* <p/>
* User: mkron, streiche
*/
@Description("This mutation translocates a segment of the int[].")
public class MutateGITranslocate implements InterfaceMutation, java.io.Serializable {
int maxLengthOfTranslocate = 4;
@ -159,9 +159,6 @@ public class MutateGITranslocate implements InterfaceMutation, java.io.Serializa
return "GI translocation mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -172,15 +169,6 @@ public class MutateGITranslocate implements InterfaceMutation, java.io.Serializa
return "GI translocation mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This mutation translocates a segment of the int[].";
}
/**
* This method allows you to set the max length of invert.
*

19
src/eva2/optimization/operator/mutation/MutateGPAdaptive.java
View File

@ -6,14 +6,11 @@ import eva2.optimization.individuals.InterfaceGPIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 11.06.2003
* Time: 16:52:48
* To change this template use Options | File Templates.
*/
@Description("This mutation replaces a random node with a new random subtree.")
public class MutateGPAdaptive implements InterfaceMutation, java.io.Serializable {
protected double mutationStep = 1;
protected double tau1 = 0.15;
@ -124,9 +121,6 @@ public class MutateGPAdaptive implements InterfaceMutation, java.io.Serializable
return "GP adaptive mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -137,15 +131,6 @@ public class MutateGPAdaptive implements InterfaceMutation, java.io.Serializable
return "GP adaptive mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This mutation replaces a random node with a new random subtree.";
}
/**
* Set the initial mutation step size with this method.
*

19
src/eva2/optimization/operator/mutation/MutateGPSingleNode.java
View File

@ -8,16 +8,13 @@ import eva2.optimization.individuals.codings.gp.GPArea;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 09.09.2004
* Time: 17:08:37
* To change this template use File | Settings | File Templates.
*/
@Description("The node mutation replaces a random node but keeps the descendants.")
public class MutateGPSingleNode implements InterfaceMutation, java.io.Serializable {
/**
@ -115,9 +112,6 @@ public class MutateGPSingleNode implements InterfaceMutation, java.io.Serializab
return "GP node mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -127,13 +121,4 @@ public class MutateGPSingleNode implements InterfaceMutation, java.io.Serializab
public String getName() {
return "GP node mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The node mutation replaces a random node but keeps the descendants.";
}
}

25
src/eva2/optimization/operator/mutation/MutateOBGAFlip.java
View File

@ -5,21 +5,12 @@ import eva2.optimization.individuals.InterfaceOBGAIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* <p>Title: EvA2</p>
* <p/>
* <p>Description: </p>
* <p/>
* <p>Copyright: Copyright (c) 2003</p>
* <p/>
* <p>Company: </p>
* Mutates a permutation by flipping edges a given nubmer of times.
*
* @author planatsc
* @version 1.0
*/
@Description("This mutation operators flips edges of the OBGA.")
public class MutateOBGAFlip implements InterfaceMutation, java.io.Serializable {
/**
@ -91,9 +82,6 @@ public class MutateOBGAFlip implements InterfaceMutation, java.io.Serializable {
return "OBGA flip mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -104,15 +92,6 @@ public class MutateOBGAFlip implements InterfaceMutation, java.io.Serializable {
return "OBGA flip mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This mutation operators flips edges of the OBGA.";
}
public int getTimes() {
return times;
}

24
src/eva2/optimization/operator/mutation/MutateOBGAInversion.java
View File

@ -6,16 +6,10 @@ import eva2.optimization.individuals.InterfaceOBGAIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* <p>Title: EvA2</p>
* <p>Description: </p>
* <p>Copyright: Copyright (c) 2003</p>
* <p>Company: </p>
*
* @author planatsc
* @version 1.0
* <p/>
* Mutates a permutation by inversion a part of the permutation.
* <br><br>
@ -25,8 +19,7 @@ import eva2.tools.math.RNG;
* 1 2 | 5 4 3 | 6 7 8 9
* </pre>
*/
@Description("This mutation operators inverts a certain section of the OBGA.")
public class MutateOBGAInversion implements java.io.Serializable, InterfaceMutation {
public MutateOBGAInversion() {
@ -84,10 +77,8 @@ public class MutateOBGAInversion implements java.io.Serializable, InterfaceMutat
return "OBGA inversion mutation";
}
/**********************************************************************************************************************
* These are for GUI
*/
/** This method allows the CommonJavaObjectEditorPanel to read the
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
* @return The name.
*/
@ -95,11 +86,4 @@ public class MutateOBGAInversion implements java.io.Serializable, InterfaceMutat
return "OBGA inversion mutation";
}
/** This method returns a global info string
* @return description
*/
public static String globalInfo() {
return "This mutation operators inverts a certain section of the OBGA.";
}
}

16
src/eva2/optimization/operator/mutation/NoMutation.java
View File

@ -4,14 +4,11 @@ package eva2.optimization.operator.mutation;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.problems.InterfaceOptimizationProblem;
import eva2.util.annotation.Description;
/**
* Created by IntelliJ IDEA.
* User: streiche
* Date: 25.03.2003
* Time: 11:42:42
* To change this template use Options | File Templates.
*/
@Description("This thing does nothing, really!")
public class NoMutation implements InterfaceMutation, java.io.Serializable {
/**
@ -87,13 +84,4 @@ public class NoMutation implements InterfaceMutation, java.io.Serializable {
public String getName() {
return "No mutation";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This thing does nothing, really!";
}
}

9
src/eva2/optimization/operator/postprocess/PostProcessParams.java
View File

@ -2,10 +2,12 @@ package eva2.optimization.operator.postprocess;
import eva2.gui.editor.GenericObjectEditor;
import eva2.optimization.enums.PostProcessMethod;
import eva2.util.annotation.Description;
import java.io.Serializable;
@Description("Combined clustering and local search post-processing of solutions. Additionally, accuracy checks can be performed on the " +
"returned solutions with different thresholds.")
public class PostProcessParams implements InterfacePostProcessParams, Serializable {
protected int postProcessSteps = 5000;
@ -150,11 +152,6 @@ public class PostProcessParams implements InterfacePostProcessParams, Serializab
return "PostProcessing " + (postProcess ? (postProcessSteps + "/" + postProcessClusterSigma) : "off");
}
public static String globalInfo() {
return "Combined clustering and local search post-processing of solutions. Additionally, accuracy checks can be performed on the " +
"returned solutions with different thresholds.";
}
@Override
public PostProcessMethod getPPMethod() {
return method;

16
src/eva2/optimization/operator/selection/SelectAll.java
View File

@ -2,12 +2,14 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* Simple method to select all.
* In case of multiple fitness values the selection
* criteria is selected randomly for each selection event. pff
* criteria is selected randomly for each selection event.
*/
@Description("This method selects all individuals.")
public class SelectAll implements InterfaceSelection, java.io.Serializable {
private boolean obeyDebsConstViolationPrinciple = true;
@ -86,9 +88,6 @@ public class SelectAll implements InterfaceSelection, java.io.Serializable {
return this.selectFrom(availablePartners, size);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -99,15 +98,6 @@ public class SelectAll implements InterfaceSelection, java.io.Serializable {
return "All Selection";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This method selects all individuals.";
}
/**
* Toggle the use of obeying the constraint violation principle
* of Deb

13
src/eva2/optimization/operator/selection/SelectBestIndividuals.java
View File

@ -4,6 +4,7 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
@ -12,6 +13,8 @@ import java.util.ArrayList;
* In case of multiple fitness values the selection
* criteria is selected randomly for each selection event.
*/
@Description("This selection method will select the n-Best individuals." +
"This is a single objective selecting method, it will select in respect to a random criterion.")
public class SelectBestIndividuals implements InterfaceSelection, java.io.Serializable {
private boolean obeyDebsConstViolationPrinciple = true;
@ -169,16 +172,6 @@ public class SelectBestIndividuals implements InterfaceSelection, java.io.Serial
return this.selectFrom(availablePartners, size);
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This selection method will select the n-Best individuals." +
"This is a single objective selecting method, it will select in respect to a random criterion.";
}
/**
* This method will return a naming String
*

17
src/eva2/optimization/operator/selection/SelectBestSingle.java
View File

@ -3,13 +3,16 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Select best individual multiple times if necessary.
* In case of multiple fitness values the selection
* critria is selected randomly for each selection event.
* criteria is selected randomly for each selection event.
*/
@Description("This selection method will select the single Best individual (n-times if necessary)." +
"This is a single objective selecting method, it will select in respect to a random criterion.")
public class SelectBestSingle implements InterfaceSelection, java.io.Serializable {
private boolean obeyDebsConstViolationPrinciple = true;
@ -126,18 +129,6 @@ public class SelectBestSingle implements InterfaceSelection, java.io.Serializabl
return this.selectFrom(availablePartners, size);
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This selection method will select the single Best individual (n-times if necessary)." +
"This is a single objective selecting method, it will select in respect to a random criterion.";
}
/**
* This method will return a naming String

17
src/eva2/optimization/operator/selection/SelectEPTournaments.java
View File

@ -4,6 +4,7 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
@ -11,8 +12,11 @@ import java.util.ArrayList;
* This method implements the multiple tournament scheme
* for EP.
* In case of multiple fitness values the selection
* critria is selected randomly for each selection event.
* criteria is selected randomly for each selection event.
*/
@Description("The EP tournament selection performs a number of tournaments per individual, the winner is assigned a point." +
" The individuals with the most points are selected." +
" This is a single objective selecting method, it will select in respect to a random criterion.")
public class SelectEPTournaments implements InterfaceSelection, java.io.Serializable {
private int tournamentSize = 4;
@ -180,17 +184,6 @@ public class SelectEPTournaments implements InterfaceSelection, java.io.Serializ
return "EP Tournament Selection";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The EP tournament selection performs a number of tournaments per individual, the winner is assigned a point." +
" The individuals with the most points are selected." +
" This is a single objective selecting method, it will select in respect to a random criterion.";
}
/**
* You can choose the tournament size.
*/

16
src/eva2/optimization/operator/selection/SelectHomologousMate.java
View File

@ -4,11 +4,14 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.distancemetric.InterfaceDistanceMetric;
import eva2.optimization.operator.distancemetric.ObjectiveSpaceMetric;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* An experimental implementation for mating restriction.
* Possibly defunct.
*/
@Description("This selection will select n mates from all individuals within the mating distance (extends Tournament Selection)." +
"This is a single objective selecting method, it will select in respect to a random criterion.")
public class SelectHomologousMate extends SelectTournament implements java.io.Serializable {
private double matingRadius = 0.1;
@ -67,9 +70,6 @@ public class SelectHomologousMate extends SelectTournament implements java.io.Se
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -81,16 +81,6 @@ public class SelectHomologousMate extends SelectTournament implements java.io.Se
return "Homologous Mating Selection";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This selection will select n mates from all individuals within the mating distance (extends Tournament Selection)." +
"This is a single objective selecting method, it will select in respect to a random criterion.";
}
/**
* This method allows you to set/get the mating radius.
*

14
src/eva2/optimization/operator/selection/SelectMOMAIIDominanceCounter.java
View File

@ -2,12 +2,14 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* Experimental selection mechanism for MOMA II where
* a single individual is a whole set of Pareto optimal
* solution. Currently defunct.
*/
@Description("This selection method only works for MOMA-II, it selects an individual depending on the number of non-dominated solutions.")
public class SelectMOMAIIDominanceCounter implements InterfaceSelection, java.io.Serializable {
private InterfaceSelection selection = new SelectBestIndividuals();
@ -114,18 +116,6 @@ public class SelectMOMAIIDominanceCounter implements InterfaceSelection, java.io
return this.selectFrom(availablePartners, size);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This selection method only works for MOMA-II, it selects an individual depending on the number of non-dominated solutions.";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/selection/SelectMOMaxiMin.java
View File

@ -3,11 +3,13 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.moso.MOSOMaxiMin;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* A multi-objective selection criterion based on the maximin
* method.
*/
@Description("This selection method will use the MaxiMin criteria to select individuals (use SelectBestIndividuals).")
public class SelectMOMaxiMin implements InterfaceSelection, java.io.Serializable {
private MOSOMaxiMin maxiMin = new MOSOMaxiMin();
@ -82,18 +84,6 @@ public class SelectMOMaxiMin implements InterfaceSelection, java.io.Serializable
return this.selectFrom(availablePartners, size);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This selection method will use the MaxiMin criteria to select individuals (use SelectBestIndividuals).";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/selection/SelectMONSGAIICrowedTournament.java
View File

@ -4,11 +4,13 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.archiving.ArchivingNSGAII;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* The infamous NSGA-II selection scheme for multi-objective
* optimization based on Pareto ranks and hybergrids.
*/
@Description("The Crowded Tournament Selection first chooses the better Pareto Front and then the smaller Crowding Distance.")
public class SelectMONSGAIICrowedTournament implements InterfaceSelection, java.io.Serializable {
private int tournamentSize = 4;
@ -152,9 +154,6 @@ public class SelectMONSGAIICrowedTournament implements InterfaceSelection, java.
return this.selectFrom(availablePartners, size);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method allows the CommonJavaObjectEditorPanel to read the
* name to the current object.
@ -165,15 +164,6 @@ public class SelectMONSGAIICrowedTournament implements InterfaceSelection, java.
return "MO Crowded Tournament Selection";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The Crowded Tournament Selection first chooses the better Pareto Front and then the smaller Crowding Distance.";
}
/**
* You can choose the tournament size.
*/

14
src/eva2/optimization/operator/selection/SelectMONonDominated.java
View File

@ -3,12 +3,14 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* This multi-objective selection method preferrs non-dominated
* individuals over dominated ones. Actually, this fails in case
* all individuals are Pareto optimal.
*/
@Description("This selection method will select all non-dominated individuals. Therefore the target size of the selection may be exceeded.")
public class SelectMONonDominated implements InterfaceSelection, java.io.Serializable {
private boolean obeyDebsConstViolationPrinciple = true;
@ -131,18 +133,6 @@ public class SelectMONonDominated implements InterfaceSelection, java.io.Seriali
return true;
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This selection method will select all non-dominated individuals. Therefore the target size of the selection may be exceeded.";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/selection/SelectMOPESA.java
View File

@ -4,10 +4,12 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.archiving.ArchivingPESAII;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* The multi-objective PESA selection method.
*/
@Description("Performs a binary tournament selection, preferring the individual with the smaller squeezing factor.")
public class SelectMOPESA implements InterfaceSelection, java.io.Serializable {
ArchivingPESAII PESAII = new ArchivingPESAII();
@ -99,18 +101,6 @@ public class SelectMOPESA implements InterfaceSelection, java.io.Serializable {
return this.selectFrom(availablePartners, size);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Performs a binary tournament selection, preferring the individual with the smaller squeezing factor.";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/selection/SelectMOPESAII.java
View File

@ -5,6 +5,7 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.archiving.ArchivingPESAII;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
import java.util.ArrayList;
import java.util.Enumeration;
@ -14,6 +15,7 @@ import java.util.Hashtable;
* The multi-objective PESA II selection criteria based on an n-dimensional
* grid using a squeezing factor.
*/
@Description("Performs a binary tournament selection, preferring the gridbox of smaller squeezing factor and selecting a random individual from the winner box.")
public class SelectMOPESAII implements InterfaceSelection, java.io.Serializable {
ArchivingPESAII PESAII = new ArchivingPESAII();
@ -135,18 +137,6 @@ public class SelectMOPESAII implements InterfaceSelection, java.io.Serializable
return this.selectFrom(availablePartners, size);
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "Performs a binary tournament selection, preferring the gridbox of smaller squeezing factor and selecting a random individual from the winner box.";
}
/**
* This method will return a naming String
*

11
src/eva2/optimization/operator/selection/SelectMOSPEAII.java
View File

@ -8,11 +8,13 @@ import eva2.optimization.population.Population;
import eva2.tools.chart2d.Chart2DDPointIconCircle;
import eva2.tools.chart2d.Chart2DDPointIconText;
import eva2.tools.chart2d.DPoint;
import eva2.util.annotation.Description;
/**
* The SPEA II selection criteria using strength and raw fitness to determine
* good individuals.
*/
@Description("This selection method calucates the strength and selects using the strength.")
public class SelectMOSPEAII implements InterfaceSelection, java.io.Serializable {
private InterfaceSelection environmentSelection = new SelectTournament();
@ -135,15 +137,6 @@ public class SelectMOSPEAII implements InterfaceSelection, java.io.Serializable
return "MO SPEAII selection";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This selection method calucates the strength and selects using the strength.";
}
/**
* This method will set the selection method that is to be used
*

17
src/eva2/optimization/operator/selection/SelectParticleWheel.java
View File

@ -6,13 +6,16 @@ import eva2.optimization.operator.selection.probability.SelProbStandard;
import eva2.optimization.operator.selection.probability.SelProbStandardScaling;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* This method implements the roulette wheel selection for
* a partical filter. In case of multiple fitness values the selection
* critria should be selected randomly for each selection event.
* a particle filter. In case of multiple fitness values the selection
* criteria should be selected randomly for each selection event.
*/
@Description("This method chooses individuals similar to the static roulette wheel. The chance for each individual to be selected depends on the selection probability. The selection probability is 1 for all Individuals with a fitness that is bigger than the midean fitness." +
"This is a single objective selecting method, it will select in respect to a random criterion.")
public class SelectParticleWheel implements InterfaceSelection, java.io.Serializable {
private boolean obeyDebsConstViolationPrinciple = true;
@ -155,16 +158,6 @@ public class SelectParticleWheel implements InterfaceSelection, java.io.Serializ
return "Particle Wheel Selection";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This method chooses individuals similar to the static roulette wheel. The chance for each individual to be selected depends on the selection probability. The selection probability is 1 for all Individuals with a fitness that is bigger than the midean fitness." +
"This is a single objective selecting method, it will select in respect to a random criterion.";
}
/**
* Toggle the use of obeying the constraint violation principle
* of Deb

11
src/eva2/optimization/operator/selection/SelectRandom.java
View File

@ -3,11 +3,13 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Random selection typically used for ES as mating selection.
*/
@Description("This method selects randomly. Therefore, it even works fine on Multiobjective fitness cases.")
public class SelectRandom implements InterfaceSelection, java.io.Serializable {
private boolean obeyDebsConstViolationPrinciple = false;
@ -115,15 +117,6 @@ public class SelectRandom implements InterfaceSelection, java.io.Serializable {
return "Random Selection";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This method selects randomly. Therefore, it even works fine on Multiobjective fitness cases.";
}
/**
* Toggle the use of obeying the constraint violation principle
* of Deb

15
src/eva2/optimization/operator/selection/SelectTournament.java
View File

@ -3,13 +3,16 @@ package eva2.optimization.operator.selection;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.tools.math.RNG;
import eva2.util.annotation.Description;
/**
* Tournament selection within a given tournament group size,
* also scaling invariant.
* In case of multiple fitness values the selection
* critria is selected randomly for each selection event.
* criteria is selected randomly for each selection event.
*/
@Description("The tournament selection compares the raw fitness of n individuals and takes the best." +
"This is a single-objective method, it selects with respect to the first criterion in the multi-objective case.")
public class SelectTournament implements InterfaceSelection, java.io.Serializable {
private int tournamentSize = 4;
@ -126,16 +129,6 @@ public class SelectTournament implements InterfaceSelection, java.io.Serializabl
return "Tournament Selection";
}
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "The tournament selection compares the raw fitness of n individuals and takes the best." +
"This is a single-objective method, it selects with respect to the first criterion in the multi-objective case.";
}
/**
* You can choose the tournament size.
*/

14
src/eva2/optimization/operator/selection/probability/SelProbBoltzman.java
View File

@ -2,12 +2,14 @@ package eva2.optimization.operator.selection.probability;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* Boltzman selection, actually it is no a selection method
* but a scaling method, but it is very good, because it is
* invariant to any linear transition function.
*/
@Description("This is the Boltzman Normation.")
public class SelProbBoltzman extends AbstractSelProb implements java.io.Serializable {
private double q = 1.0;
@ -225,18 +227,6 @@ public class SelProbBoltzman extends AbstractSelProb implements java.io.Serializ
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is the Boltzman Normation.";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/selection/probability/SelProbFitnessSharing.java
View File

@ -4,10 +4,12 @@ import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.operator.distancemetric.InterfaceDistanceMetric;
import eva2.optimization.operator.distancemetric.PhenotypeMetric;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* Here we have the infamous fitness sharing method.
*/
@Description("This is a fitness sharing based normation method.")
public class SelProbFitnessSharing extends AbstractSelProb implements java.io.Serializable {
private InterfaceSelectionProbability basicNormationMethod = new SelProbStandard();
@ -69,18 +71,6 @@ public class SelProbFitnessSharing extends AbstractSelProb implements java.io.Se
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a fitness sharing based normation method.";
}
/**
* This method will return a naming String
*

17
src/eva2/optimization/operator/selection/probability/SelProbInvertByMax.java
View File

@ -2,16 +2,15 @@ package eva2.optimization.operator.selection.probability;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* Scale the fitness of a maximization problem by inverting it by the maximum fitness,
* then normalize by fitness sum. This way, maximally bad individuals will receive a
* selection probability of zero.
*
* @author mkron
* <p/>
* May 2, 2007
*/
@Description("This is a standard normation method inverted by maximum fitness.")
public class SelProbInvertByMax extends AbstractSelProb {
private double maxFit = 1.;
@ -94,18 +93,6 @@ public class SelProbInvertByMax extends AbstractSelProb {
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a standard normation method inverted by maximum fitness.";
}
/**
* This method will return a naming String
*

18
src/eva2/optimization/operator/selection/probability/SelProbLinearRanking.java
View File

@ -2,15 +2,12 @@ package eva2.optimization.operator.selection.probability;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* A linear ranking method with offsets.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 11.06.2004
* Time: 10:30:47
* To change this template use File | Settings | File Templates.
*/
@Description("This is linear ranking normation.")
public class SelProbLinearRanking extends AbstractSelProb implements java.io.Serializable {
private double nappaPlus = 1.1;
@ -171,17 +168,6 @@ public class SelProbLinearRanking extends AbstractSelProb implements java.io.Ser
}
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is linear ranking normation.";
}
/**
* This method will return a naming String

13
src/eva2/optimization/operator/selection/probability/SelProbNonLinearRanking.java
View File

@ -2,11 +2,13 @@ package eva2.optimization.operator.selection.probability;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* A non-linear ranking, which is difficult to tune to
* the given optimization problem i guess.
*/
@Description("This is non-linear ranking normation.")
public class SelProbNonLinearRanking extends AbstractSelProb implements java.io.Serializable {
private double c = 0.04;
@ -182,17 +184,6 @@ public class SelProbNonLinearRanking extends AbstractSelProb implements java.io.
}
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is non-linear ranking normation.";
}
/**
* This method will return a naming String

18
src/eva2/optimization/operator/selection/probability/SelProbRanking.java
View File

@ -2,16 +2,13 @@ package eva2.optimization.operator.selection.probability;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* Ranking for calculating the selection probability.
* This truly scaling invariant.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 30.03.2004
* Time: 16:58:44
* To change this template use File | Settings | File Templates.
*/
@Description("This is ranking normation.")
public class SelProbRanking extends AbstractSelProb implements java.io.Serializable {
public SelProbRanking() {
@ -104,17 +101,6 @@ public class SelProbRanking extends AbstractSelProb implements java.io.Serializa
}
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is ranking normation.";
}
/**
* This method will return a naming String

14
src/eva2/optimization/operator/selection/probability/SelProbStandard.java
View File

@ -2,12 +2,14 @@ package eva2.optimization.operator.selection.probability;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* A simple sum to calculate the selection probability.
* <p/>
* p(i is selected) = exp(-fitness(i))/sum_j(exp(-fitness(j)))
*/
@Description("This is a standard normation method using the exp function.")
public class SelProbStandard extends AbstractSelProb implements java.io.Serializable {
public SelProbStandard() {
@ -96,18 +98,6 @@ public class SelProbStandard extends AbstractSelProb implements java.io.Serializ
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a standard normation method using the exp function.";
}
/**
* This method will return a naming String
*

14
src/eva2/optimization/operator/selection/probability/SelProbStandardScaling.java
View File

@ -2,10 +2,12 @@ package eva2.optimization.operator.selection.probability;
import eva2.optimization.individuals.AbstractEAIndividual;
import eva2.optimization.population.Population;
import eva2.util.annotation.Description;
/**
* A simple sum with a scaling factor.
*/
@Description("This is a standard normation method with scaling.")
public class SelProbStandardScaling extends AbstractSelProb implements java.io.Serializable {
private double Q = 0;
@ -156,18 +158,6 @@ public class SelProbStandardScaling extends AbstractSelProb implements java.io.S
}
}
/**********************************************************************************************************************
* These are for GUI
*/
/**
* This method returns a global info string
*
* @return description
*/
public static String globalInfo() {
return "This is a standard normation method with scaling.";
}
/**
* This method will return a naming String
*

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