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Adding niching methods and updated CBN-EA

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This commit is contained in:
Marcel Kronfeld 2011-05-03 12:32:54 +00:00
parent 315ef66d54
commit ae46d9a3d7
6 changed files with 1768 additions and 102 deletions
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224
src/eva2/server/go/strategies/CBNPSO.java Normal file
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@ -0,0 +1,224 @@
package eva2.server.go.strategies;
import java.io.Serializable;
import eva2.server.go.PopulationInterface;
import eva2.server.go.enums.PSOTopologyEnum;
import eva2.server.go.operators.cluster.ClusteringDensityBased;
import eva2.server.go.operators.distancemetric.IndividualDataMetric;
import eva2.server.go.operators.paramcontrol.CbpsoFitnessThresholdBasedAdaption;
import eva2.server.go.operators.paramcontrol.LinearParamAdaption;
import eva2.server.go.operators.paramcontrol.ParamAdaption;
import eva2.server.go.operators.paramcontrol.SinusoidalParamAdaption;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.AbstractProblemDouble;
import eva2.server.go.problems.InterfaceInterestingHistogram;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.server.go.problems.InterfaceProblemDouble;
import eva2.tools.EVAERROR;
import eva2.tools.ToolBox;
public class CBNPSO extends ClusterBasedNichingEA implements Serializable {
private boolean forceUpperClustDist = true;
/**
* Standard constructur with fixed frequency (no fitness threshold based frequency adaption)
*/
public CBNPSO() {
this(false);
}
public CBNPSO(boolean threshAdaption) {
this(10, 15, 0.001, 1e-10, 15, 100, threshAdaption);
// super();
// setDifferentiationCA(new ClusteringDensityBased(getClusterDiffDist(), 10, new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey)));
// setMergingCA(new ClusteringDensityBased(0.001, 3, new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey)));
// setEpsilonBound(1e-10);
// setHaltingWindow(15);
// setMaxSpeciesSize(15);
// setOptimizer(new ParticleSwarmOptimization(100, 2.05, 2.05, PSOTopologyEnum.grid, 2));
// ParamAdaption[] defAdpt = new ParamAdaption[]{getDefaultSinusoidalAdaption()};
// setParameterControl(defAdpt);
// if (threshAdaption) addParameterControl(getDefaultThreshAdaption());
// setPopulationSize(100);
}
public CBNPSO(int minSpecSize, int maxSpecSize, double sigmaMerge, double epsilonConv, int haltingWindow, int popSize, boolean threshAdaption) {
super();
setDifferentiationCA(new ClusteringDensityBased(getClusterDiffDist(), minSpecSize, new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey)));
setMergingCA(new ClusteringDensityBased(sigmaMerge, 3, new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey)));
setEpsilonBound(epsilonConv);
setHaltingWindow(haltingWindow);
setMaxSpeciesSize(maxSpecSize);
setOptimizer(new ParticleSwarmOptimization(popSize, 2.05, 2.05, PSOTopologyEnum.grid, 2));
ParamAdaption[] defAdpt = new ParamAdaption[]{getDefaultSinusoidalAdaption()};
setParameterControl(defAdpt);
if (threshAdaption) addParameterControl(getDefaultThreshAdaption());
setPopulationSize(popSize);
}
private ParamAdaption getDefaultSinusoidalAdaption() {
return new SinusoidalParamAdaption(0.1, 1., 10000, 0, "clusterDiffDist");
}
private ParamAdaption getDefaultThreshAdaption() {
return new CbpsoFitnessThresholdBasedAdaption();
}
@Override
public void SetProblem(InterfaceOptimizationProblem problem) {
super.SetProblem(problem);
if (problem instanceof AbstractProblemDouble) {
AbstractProblemDouble dblProb = ((AbstractProblemDouble)problem);
adaptMinMaxSwarmSizeByDim(dblProb);
}
}
/**
* Return the period of the sinusoidal sigma adaption or -1 if not applicable.
*
* @param p
*/
public int getSigmaAdaptionPeriod() {
ParamAdaption[] prmAd = getParameterControl();
for (int i=0; i<prmAd.length; i++) {
if (prmAd[i] instanceof SinusoidalParamAdaption) {
if (prmAd[i].getControlledParam().equals("clusterDiffDist")) return ((SinusoidalParamAdaption)prmAd[i]).getIterationPeriod();
}
}
return -1;
}
/**
* Set the period of the sinusoidal sigma adaption, if a fitting ParamAdaption
* instance is found. Otherwise, nothing happens.
* @param p
*/
public void setSigmaAdaptionPeriod(int p) {
ParamAdaption[] prmAd = getParameterControl();
for (int i=0; i<prmAd.length; i++) {
if (prmAd[i] instanceof SinusoidalParamAdaption) {
if (prmAd[i].getControlledParam().equals("clusterDiffDist")) {
((SinusoidalParamAdaption)prmAd[i]).setIterationPeriod(p);
return;
}
}
}
System.err.println("Error: unable to set adaption period " + p + ", no sinusoidal adaption instance found.");
}
public void setSigmaAdaptionShift(int s) {
ParamAdaption[] prmAd = getParameterControl();
for (int i=0; i<prmAd.length; i++) {
if (prmAd[i] instanceof SinusoidalParamAdaption) {
if (prmAd[i].getControlledParam().equals("clusterDiffDist")) {
((SinusoidalParamAdaption)prmAd[i]).setInitialShift(s);
return;
}
}
}
System.err.println("Error: unable to set adaption shift " + s + ", no sinusoidal adaption instance found.");
}
private void adaptMinMaxSwarmSizeByDim(AbstractProblemDouble dblProb) {
// TODO Auto-generated method stub
}
@Override
public void init() {
super.init();
if (getProblem() instanceof InterfaceProblemDouble) {
if (isForceUpperClustDist()) setUpperBoundClustDiff((InterfaceProblemDouble)getProblem());
} else System.err.println("Can set upper bound of clustering parameter only for AbstractProblemDouble types, not for " + getProblem().getClass().getName());
}
@Override
public String getName() {
if (getParameterControl().length>0) {
String addInfo="adpt";
if (getParameterControl()[0] instanceof SinusoidalParamAdaption) addInfo="SinT"+((SinusoidalParamAdaption)getParameterControl()[0]).getIterationPeriod();
else if (getParameterControl()[0] instanceof LinearParamAdaption) addInfo="Lin"+((LinearParamAdaption)getParameterControl()[0]).getStartV()+"-"+((LinearParamAdaption)getParameterControl()[0]).getEndV();
return "CBN-PSO-"+addInfo;
} else return "CBN-PSO";
}
public static String globalInfo() {
return "A CBN-EA variant employing PSO and dynamic variation of the clustering parameter by default.";
}
public void setForceUpperClustDist(boolean forceUpperClustDist) {
this.forceUpperClustDist = forceUpperClustDist;
}
public boolean isForceUpperClustDist() {
return forceUpperClustDist;
}
public String forceUpperClustDistTipText() {
return "Activate to force cluster distance to be maximal corresponding to the CBN-PSO settings.";
}
/**
* Return the ratio of interesting solutions per archived solutions, which
* is in [0,1] if any solutions have been identified, or -1 if the archive
* is empty.
*
* @param cbpso
* @param pop
* @param iteration
* @param maxIteration
* @return
*/
public double getInterestingSolutionRatio() {
InterfaceOptimizationProblem prob = getProblem();
double fitThres = 100;
if (prob instanceof InterfaceInterestingHistogram) {
fitThres = ((InterfaceInterestingHistogram)prob).getHistogram().getUpperBound();
} else EVAERROR.errorMsgOnce("Warning, problem does not define a fitness threshold!");
SolutionSet solSet = getAllSolutions();
Population archived = solSet.getSolutions();
Population interesting = archived.filterByFitness(fitThres, 0);
// Population archived = getArchivedSolutions();
// Population interesting = archived.filterByFitness(fitThres, 0);
if (archived.size()>0) {
return ((double)interesting.size())/((double)archived.size());
} else return -1;
}
@Override
public String[] getAdditionalDataHeader() {
String[] addVals = {"interestingRatio"};
if (getCurrentPeriod()>=0) addVals = new String[]{"interestingRatio", "adaptPeriod"};
return ToolBox.appendArrays(super.getAdditionalDataHeader(), addVals);
}
/**
* Retrieve the current period of the sinusoidal sigma adaption (in case it is controlled by a threshold adaption)
* or -1 if this does not apply.
*
* @return
*/
private int getCurrentPeriod() {
ParamAdaption[] adaptors = super.getParameterControl();
SinusoidalParamAdaption sinA=null;
CbpsoFitnessThresholdBasedAdaption ftA=null;
if (adaptors!=null) for (int i=0; i<adaptors.length; i++) {
if (adaptors[i] instanceof SinusoidalParamAdaption) sinA = (SinusoidalParamAdaption)adaptors[i];
else if (adaptors[i] instanceof CbpsoFitnessThresholdBasedAdaption) ftA = (CbpsoFitnessThresholdBasedAdaption)adaptors[i];
}
if (sinA!=null && (ftA!=null)) return sinA.getIterationPeriod();
else return -1;
}
@Override
public Object[] getAdditionalDataValue(PopulationInterface pop) {
Object[] addVals = null;
double freq = getCurrentPeriod();
if (freq>=0) addVals = new Object[]{getInterestingSolutionRatio(), freq};
else addVals = new Object[]{getInterestingSolutionRatio()};
return ToolBox.appendArrays(super.getAdditionalDataValue(pop), addVals);
}
}

381
src/eva2/server/go/strategies/ClusterBasedNichingEA.java
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@ -12,20 +12,29 @@ import eva2.gui.GraphPointSet;
import eva2.gui.Plot;
import eva2.gui.TopoPlot;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.InterfaceTerminator;
import eva2.server.go.PopulationInterface;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.AbstractEAIndividualComparator;
import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.operators.cluster.ClusteringDensityBased;
import eva2.server.go.operators.cluster.InterfaceClustering;
import eva2.server.go.operators.cluster.InterfaceClusteringDistanceParam;
import eva2.server.go.operators.cluster.InterfaceClusteringMetricBased;
import eva2.server.go.operators.distancemetric.ObjectiveSpaceMetric;
import eva2.server.go.operators.paramcontrol.InterfaceHasUpperDoubleBound;
import eva2.server.go.operators.paramcontrol.ParamAdaption;
import eva2.server.go.operators.paramcontrol.ParameterControlManager;
import eva2.server.go.operators.terminators.HistoryConvergenceTerminator;
import eva2.server.go.populations.Population;
import eva2.server.go.populations.SolutionSet;
import eva2.server.go.problems.B1Problem;
import eva2.server.go.problems.Interface2DBorderProblem;
import eva2.server.go.problems.InterfaceAdditionalPopulationInformer;
import eva2.server.go.problems.InterfaceOptimizationProblem;
import eva2.server.go.problems.InterfaceProblemDouble;
import eva2.server.go.problems.TF1Problem;
import eva2.tools.EVAERROR;
import eva2.tools.chart2d.Chart2DDPointIconCircle;
import eva2.tools.chart2d.Chart2DDPointIconText;
import eva2.tools.chart2d.DPoint;
@ -37,12 +46,18 @@ import eva2.tools.math.Mathematics;
* It should be able to identify and track multiple global/local optima
* at the same time.
*
* Copyright: Copyright (c) 2003
* Notes: For std. GA, the mutation rate may have to reduced, because the
* initial step size tends to be rel. large and easily disperse clustered
* species (so that they fall below the minimum swarm size and the local
* optimum is lost).
*
* For the CBN-PSO remember to use the IndividualDataMetric so that the
* remembered positions are used for clustering (which are rel. stable -
* so that species clustering actually makes sense).
*
* Copyright: Copyright (c) 2010
* Company: University of Tuebingen, Computer Architecture
* @author Felix Streichert
* @version: $Revision: 322 $
* $Date: 2007-12-11 17:24:07 +0100 (Tue, 11 Dec 2007) $
* $Author: mkron $
* @author Felix Streichert, Marcel Kronfeld
*/
public class ClusterBasedNichingEA implements InterfacePopulationChangedEventListener, InterfaceAdditionalPopulationInformer, InterfaceOptimizer, java.io.Serializable {
@ -57,6 +72,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm();
private InterfaceClustering m_CAForSpeciesDifferentation = new ClusteringDensityBased();
private InterfaceClustering m_CAForSpeciesMerging = new ClusteringDensityBased();
private double clusterDiffDist = 0.05;
// private double clusterMergeDist = 0.0001;
// private Distraction distraction = null;
private boolean useDistraction = false;
// private double distrDefaultStrength = .7;
@ -75,8 +93,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private boolean m_mergeSpecies = true;
private int m_PopulationSize = 50;
private int convergedCnt = 0;
private int collisions = 0;
private static boolean TRACE = false, TRACE_STATE=false;
private static boolean TRACE = false, TRACE_STATE=false, TRACE_EVTS=false;
private int m_ShowCycle = 0;
transient private TopoPlot m_Topology;
private int haltingWindow = 15;
@ -84,13 +103,51 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private int sleepTime = 0;
private int m_maxSpeciesSize = 15;
private AbstractEAIndividualComparator reduceSizeComparator = new AbstractEAIndividualComparator();
private AbstractEAIndividualComparator histComparator = new AbstractEAIndividualComparator("", -1, true);
protected ParameterControlManager paramControl = new ParameterControlManager();
private double avgDistForConvergence = 0.1; // Upper bound for average indy distance in a species in the test for convergence
public ClusterBasedNichingEA() {
this.m_CAForSpeciesMerging = new ClusteringDensityBased();
((ClusteringDensityBased)this.m_CAForSpeciesMerging).setMinimumGroupSize(m_minGroupSize);
// ((ClusteringDensityBased)this.m_CAForSpeciesMerging).setMinimumGroupSize(m_minGroupSize);
// if (useDistraction) distraction = new Distraction(distrDefaultStrength, Distraction.METH_BEST);
}
/**********************************************************************************************************************
* These are for InterfaceParamControllable
*/
public Object[] getParamControl() {
List<Object> ctrlbls = ParameterControlManager.listOfControllables(this);
ctrlbls.add(paramControl);
return ctrlbls.toArray();
// this works - however differently than when returning a ParameterControlManager
// Only instances are returned which
}
/**
* This method is necessary to allow access from the Processor.
* @return
*/
// public ParameterControlManager getParamControl() {
// return paramControl;
// }
public ParamAdaption[] getParameterControl() {
return paramControl.getSingleAdapters();
}
public void setParameterControl(ParamAdaption[] paramControl) {
this.paramControl.setSingleAdapters(paramControl);
}
public String parameterControlTipText() {
return "You may define dynamic paramter control strategies using the parameter name.";
}
public void addParameterControl(ParamAdaption pa) {
this.paramControl.addSingleAdapter(pa);
}
public ClusterBasedNichingEA(ClusterBasedNichingEA a) {
this.epsilonBound = a.epsilonBound;
this.m_Population = (Population)a.m_Population.clone();
@ -112,6 +169,11 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.muLambdaRatio = a.muLambdaRatio;
this.sleepTime = a.sleepTime;
this.convergedCnt = a.convergedCnt;
this.collisions = a.collisions;
this.clusterDiffDist = a.clusterDiffDist;
this.useDistraction = a.useDistraction;
this.m_ShowCycle = a.m_ShowCycle;
this.m_maxSpeciesSize = a.m_maxSpeciesSize;
}
public Object clone() {
@ -119,18 +181,24 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
}
public void init() {
this.m_Undifferentiated = new Population(m_PopulationSize);
if (m_Undifferentiated==null) this.m_Undifferentiated = new Population(m_PopulationSize);
else {
m_Undifferentiated.resetProperties();
m_Undifferentiated.setTargetSize(m_PopulationSize);
}
this.m_Undifferentiated.setUseHistory(true);
this.m_Problem.initPopulation(this.m_Undifferentiated);
this.m_Optimizer.setPopulation(m_Undifferentiated);
this.m_Optimizer.initByPopulation(m_Undifferentiated, true);
m_Undifferentiated=this.m_Optimizer.getPopulation(); // some optimizers clone the given one.
if (m_Optimizer instanceof EvolutionStrategies) {
EvolutionStrategies es = (EvolutionStrategies)m_Optimizer;
es.setLambda(getPopulationSize());
es.setMu((int)(muLambdaRatio*(double)getPopulationSize()));
}
this.m_Optimizer.init();
m_doomedPop = new Population();
// this.m_Optimizer.init();
m_doomedPop = m_Undifferentiated.cloneWithoutInds();
if (m_Undifferentiated.getFunctionCalls()!=m_PopulationSize) {
System.err.println("Whats that in CBN!?");
}
@ -152,12 +220,14 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.m_Optimizer.addPopulationChangedEventListener(this);
this.m_Undifferentiated.setTargetSize(this.m_PopulationSize);
this.m_Species = new ArrayList<Population>();
this.m_Archive = new Population();
this.m_Archive = m_Undifferentiated.cloneWithoutInds();
// if (useDistraction) distraction = new Distraction(distrDefaultStrength, Distraction.METH_BEST);
convergedCnt = 0;
collisions=0;
if (evalPop) this.evaluatePopulation(this.m_Undifferentiated);
this.m_Optimizer.initByPopulation(m_Undifferentiated, false);
this.m_Undifferentiated = m_Optimizer.getPopulation(); // required for changes to the population by the optimizer
m_Population = m_Undifferentiated;
this.firePropertyChangedEvent("FirstGenerationPerformed");
}
@ -223,7 +293,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
Population pop;
this.m_Topology = new TopoPlot("CBN-Species at gen. " + gen,"x","y",a,a);
this.m_Topology.setParams(60, 60);
this.m_Topology.setParams(50, 50);
this.m_Topology.setTopology((Interface2DBorderProblem)this.m_Problem);
//draw the undifferentiated
for (int i = 0; i < this.m_Undifferentiated.size(); i++) {
@ -238,7 +308,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
}
if (!useDistraction) {
for (int i = 0; i < this.m_Archive.size(); i++) {
plotIndy('x',(InterfaceDataTypeDouble)m_Archive.get(i));
plotIndy(m_Topology, 'x',(InterfaceDataTypeDouble)m_Archive.get(i));
}
} else {
// for (int i = 0; i < this.distraction.getDistractorSetSize(); i++) {
@ -246,16 +316,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// }
}
}
if (sleepTime > 0) {
try {
Thread.sleep(sleepTime);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
private void plotPopConnected(TopoPlot tp, Population pop) {
public static void plotPopConnected(TopoPlot tp, Population pop) {
DPointSet popRep;
InterfaceDataTypeDouble tmpIndy1;
if (pop.size()>1) {
@ -269,31 +332,26 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
}
} else {
// this is an inactive species
plotIndy('+',(InterfaceDataTypeDouble)pop.get(0));
plotIndy(tp, '+',(InterfaceDataTypeDouble)pop.get(0));
}
}
private void plotLine(TopoPlot tp, AbstractEAIndividual indy1,
public static void plotLine(TopoPlot tp, AbstractEAIndividual indy1,
AbstractEAIndividual indy2) {
DPointSet popRep;
// DPointSet popRep;
double[] pos1, pos2;
if (indy1 instanceof InterfaceDataTypeDouble) pos1=((InterfaceDataTypeDouble)indy1).getDoubleData();
else pos1=(indy1).getDoublePosition();
if (indy2 instanceof InterfaceDataTypeDouble) pos2=((InterfaceDataTypeDouble)indy2).getDoubleData();
else pos2 =(indy2).getDoublePosition();
popRep = new DPointSet();
popRep.setConnected(true);
popRep.addDPoint(new DPoint(pos1[0], pos1[1]));
popRep.addDPoint(new DPoint(pos2[0], pos2[1]));
tp.getFunctionArea().addDElement(popRep);
tp.getFunctionArea().drawLine(pos1, pos2);
}
private void plotIndy(char c, InterfaceDataTypeDouble tmpIndy) {
plotPosFit(c, tmpIndy.getDoubleData(), ((AbstractEAIndividual)tmpIndy).getFitness(0));
public static void plotIndy(Plot p, char c, InterfaceDataTypeDouble tmpIndy) {
plotPosFit(p, c, tmpIndy.getDoubleData(), ((AbstractEAIndividual)tmpIndy).getFitness(0));
}
private void plotPosFit(char c, double[] position, double fitness) {
public static void plotPosFit(Plot p, char c, double[] position, double fitness) {
DPointSet popRep;
popRep = new DPointSet();
popRep.addDPoint(new DPoint(position[0], position[1]));
@ -301,7 +359,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
DPointIcon icon = new Chart2DDPointIconText(c+""+d);
((Chart2DDPointIconText)icon).setIcon(new Chart2DDPointIconCircle());
popRep.setIcon(icon);
this.m_Topology.getFunctionArea().addDElement(popRep);
p.getFunctionArea().addDElement(popRep);
}
/** This method is called to generate n freshly initialized individuals
@ -309,7 +367,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
* @return A population of new individuals
*/
private Population initializeIndividuals(int n) {
Population result = new Population();
Population result = m_Undifferentiated.cloneWithoutInds();
result.setUseHistory(true);
result.setTargetSize(n);
//@todo: crossover between species is to be implemented
@ -328,37 +386,56 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
* @return True if converged.
*/
private boolean testSpeciesForConvergence(Population pop) {
List<AbstractEAIndividual> speciesHistory = pop.getHistory();
int histLen = speciesHistory.size();
int histLen = pop.getHistoryLength();
if (histLen <= haltingWindow) {
if (histLen < haltingWindow) {
// System.out.println("not long enough... gen " + pop.getGeneration());
return false;
} else {
AbstractEAIndividual historicHWAgo = speciesHistory.get(histLen-haltingWindow);
for (int i = 1; i < haltingWindow; i++) {
// if historic[-hW] is worse than historic[-hW+i] return false
AbstractEAIndividual historicIter = speciesHistory.get(histLen-haltingWindow+i);
// if the iterated indy (the later one in history) has improved, there is no convergence.
if (testSecondForImprovement(historicHWAgo, historicIter)) return false;
// TODO: undo: InterfaceTerminator convergenceTerminator = new DiversityTerminator(epsilonBound,new PhenotypeMetric(),0);
// boolean term = convergenceTerminator.isTerminated(pop);
// if (historicHWAgo.getFitness(0) > ((AbstractEAIndividual)pop.m_History.get(length-haltingWindow+i)).getFitness(0)) {
// System.out.println("( " + historic.getFitness(0) + "/" + ((AbstractEAIndividual)pop.m_History.get(length-haltingWindow+i)).getFitness(0));
// return false;
// }
}
InterfaceTerminator convergenceTerminator = new HistoryConvergenceTerminator(haltingWindow, epsilonBound, 0, false);
boolean term = convergenceTerminator.isTerminated(pop);
// if (term) {
// System.out.println("Conv spec. aged " + pop.getGeneration() + " with meas. " + BeanInspector.toString(pop.getPopulationMeasures()));
// }
if (term) {
// this case is especially relevant if sequential niching is "faked" using the CBN approach,
// because in this case, the niching parameter is very large and the single (sequentially build) species
// may be still spread throughout the search space and still not improve for the given halting window.
// Omitting this causes high numbers of phantom solutions which are far from converged optima
double[] specMeas = pop.getPopulationMeasures();
if (specMeas[0]>avgDistForConvergence ) {
// if (getClusterDiffDist()<=0.5)
// System.err.println("ALTERNATIVE BREAK, FORBIDDING CONVERGENCE! sig=" + getClusterDiffDist() + " / avD="+ specMeas[0]);
InterfaceTerminator convTerm2 = new HistoryConvergenceTerminator(2*haltingWindow, epsilonBound, 0, false);
term = convTerm2.isTerminated(pop);
if (term) {
// System.out.println("Twice the halting window passed without improvement and still no phenotypic convergence!!!");
return true;
} else return false;
}
}
if (term) {
// System.out.println();
}
// TODO something like this may be used as additional convergence criterion.
// it influences the number of local optima archived and seems to increase the score, but not the best-found solutions, at least for a large clustering parameter
// System.out.println("Terminated, subswarm measures: " + BeanInspector.toString(pop.getPopulationMeasures()));
// if (m_Optimizer instanceof ParticleSwarmOptimization) {
// double swarmSp = ParticleSwarmOptimization.getPopulationVelSpeed(pop, 2, ParticleSwarmOptimization.partVelKey, null, null)[0];
// System.out.println("Swarm speed: " + swarmSp);
// if (swarmSp > 0.05) {
// System.err.println("Too high speed");
// term = false;
// }
// }
// }
return term;
}
if (false) { // plot the historic fitness values
double[] a = new double[2];
a[0] = 0; a[1] = 0;
Plot plot = new Plot("HaltingWindow", "History", "Fitness", a, a);
plot.setUnconnectedPoint(0, -1, 0);
for (int i = haltingWindow; i > 0; i--) {
a = speciesHistory.get(histLen-i).getFitness();
plot.setUnconnectedPoint(haltingWindow-i+1, a[0], 0);
}
}
return true;
}
/**
@ -377,7 +454,10 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
boolean ret = (secIndy.isDominatingDebConstraints(firstIndy));
ret = ret && (fitDiff > epsilonBound); // there is improvement if the second is dominant and the fitness difference is larger than epsilon
return ret;
} else return (secIndy.isDominatingDebConstraints(firstIndy));
} else {
return (histComparator.compare(firstIndy, secIndy)>0);
// return (secIndy.isDominatingDebConstraints(firstIndy));
}
}
private Population optimizeSpecies(Population species, boolean minorPlot) {
@ -401,7 +481,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
if (BeanInspector.hasMethod(m_Optimizer, "getLastModelPopulation", null)!=null) {
Object pc = BeanInspector.callIfAvailable(m_Optimizer, "getLastTrainingPatterns", null);
System.out.println("MAPSO train set bef optSpec: " + BeanInspector.callIfAvailable(pc, "getStringRepresentation", null));
// System.out.println("MAPSO train set bef optSpec: " + BeanInspector.callIfAvailable(pc, "getStringRepresentation", null));
}
this.m_Optimizer.optimize();
@ -442,7 +522,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
if (m_doomedPop.size()>0) {
reinitPop = this.initializeIndividuals(m_doomedPop.size()); // do not add these to undifferentiated yet, that would mess up the evaluation count
m_doomedPop.clear();
if (TRACE) System.out.println("Reinited " + reinitPop.size() + " indies... ");
// if (TRACE)
// System.out.println("At " + m_Undifferentiated.getFunctionCalls() + " reinited " + reinitPop.size() + " indies... ");
}
int countIndies = (reinitPop != null ? reinitPop.size() : 0) + m_Undifferentiated.size();
for (int i=0; i<m_Species.size(); i++) countIndies+=m_Species.get(i).size();
@ -450,13 +531,19 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
if (this.m_ShowCycle > 0) {
if (m_Undifferentiated.getGeneration()<=1) plot(m_Undifferentiated.getGeneration());
}
if (sleepTime > 0) {
try {
Thread.sleep(sleepTime);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
// species evolution phase
// optimize D_0
this.m_Undifferentiated.synchSize();
if (m_Undifferentiated.size()>0) {
// this.capMutationRate(this.m_Undifferentiated, 0); // MK this sets mutation rate to 0! why?
// this.capMutationRate(this.m_Undifferentiated, 0); // MK this sets mutation rate to 0! why? possibly to guarantee contraction of the species?
m_Undifferentiated.putData(InterfaceSpeciesAware.populationTagKey, InterfaceSpeciesAware.explorerPopTag);
m_Undifferentiated = optimizeSpecies(m_Undifferentiated, false);
} else m_Undifferentiated.incrGeneration();
@ -476,12 +563,17 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// System.out.println("Diff.Size : " + ((Population)this.m_Species.get(i)).size() +"/"+((Population)this.m_Species.get(i)).getPopulationSize());
// }
convergedCnt++;
// if (TRACE)
if (TRACE) System.out.print("--Converged: "+convergedCnt + " - " + testSpeciesForConvergence(curSpecies));
if (TRACE_EVTS) System.out.println("!!!! Converged Spec!");
if (TRACE) {
testSpeciesForConvergence(curSpecies);
System.out.print("--Converged: "+convergedCnt + " - " + testSpeciesForConvergence(curSpecies));
}
if (TRACE) System.out.println(curSpecies.getBestEAIndividual());
// memorize the best one....
AbstractEAIndividual best = (AbstractEAIndividual)curSpecies.getBestEAIndividual().getClone();
// AbstractEAIndividual best = (AbstractEAIndividual)curSpecies.getBestEAIndividual().getClone();
AbstractEAIndividual best = curSpecies.getBestHistoric(); // usually we want the best alltogether
if (best == null) best = (AbstractEAIndividual)curSpecies.getBestEAIndividual().getClone();
// if (useDistraction) { // Add distractor!
// if (distraction == null) distraction = new Distraction(distrDefaultStrength, Distraction.METH_BEST);
// distraction.addDistractorFrom(curSpecies);
@ -490,6 +582,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
int toReinit=0;
if (true) { //if (m_UseArchive) {
m_Archive.add(best);
// System.out.println((""+ m_Population.getFunctionCalls() + " " + (BeanInspector.toString(best.getDoublePosition())).replaceAll(";|\\[|\\]", "")));
m_Species.remove(i); // remove the converged Species
toReinit=curSpecies.size();
}
@ -522,6 +615,17 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
if (TRACE) System.out.println("### funcalls: "+m_Undifferentiated.getFunctionCalls());
}
//////////////////////
synchronized (m_Population) { // fill the m_Population instance with the current individuals from undiff, spec, etc.
this.m_Population = (Population)this.m_Undifferentiated.clone();
m_Population.setUseHistory(true);
for (int i = 0; i < this.m_Species.size(); i++) {
this.m_Population.addPopulation((Population)this.m_Species.get(i));
}
if (m_doomedPop.size()>0) m_Population.addPopulation(reinitPop); // this is just so that the numbers match up...
m_Population.synchSize();
}
//////////////////////
if ((this.m_Undifferentiated.getFunctionCalls()+(reinitPop==null ? 0 : (reinitPop.size()))) % this.m_PopulationSize != 0) {
if (TRACE) System.out.println("### mismatching number of funcalls, inactive species?");// Correcting by " + (m_PopulationSize - (m_Undifferentiated.getFunctionCalls() % m_PopulationSize)));
@ -532,7 +636,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// possible species differentiation and convergence
if (this.m_Undifferentiated.getGeneration()%this.m_SpeciesCycle == 0) {
if (TRACE) System.out.println("Species cycle:");
this.m_CAForSpeciesDifferentation.initClustering(m_Population);
initClustering();
if (this.m_UseSpeciesDifferentiation) {
///////////////////////////// species differentiation phase
if (TRACE) printState("---Species Differentation", reinitPop);
@ -540,12 +645,13 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
ArrayList<Population> newSpecies = new ArrayList<Population>();
//cluster the undifferentiated population
clusters = this.m_CAForSpeciesDifferentation.cluster(this.m_Undifferentiated, m_Population);
if (TRACE) System.out.println("clustered undiff to " + clusters.length);
replaceUndifferentiated(clusters[0]);
if (TRACE)
System.out.println("clustered undiff to " + clusters.length);
for (int j = 1; j < clusters.length; j++) { // loop new clusters
splitFromFirst(m_Undifferentiated, clusters[j], false);
newSpecies.add(clusters[j]);
}
replaceUndifferentiated(clusters[0]);
for (int i = 0; i < this.m_Species.size(); i++) { // loop old species
curSpecies = this.m_Species.get(i);
// if (curSpecies.size()>m_minGroupSize) { // only active populations are clustered
@ -574,10 +680,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// plot the populations
if (this.m_ShowCycle > 0) {
if ((this.m_Undifferentiated.getGeneration() <= 2)) {
if ((this.m_Undifferentiated.getGeneration() <= 2) || (this.m_Undifferentiated.getGeneration()%this.m_ShowCycle == 0)) {
this.plot(this.m_Undifferentiated.getGeneration());
} else {
if (this.m_Undifferentiated.getGeneration()%this.m_ShowCycle == 0) this.plot(this.m_Undifferentiated.getGeneration());
}
}
@ -590,6 +694,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
int[] assocSpec = m_CAForSpeciesMerging.associateLoners(m_Undifferentiated, m_Species.toArray(new Population[m_Species.size()]), m_Population);
for (int i=m_Undifferentiated.size()-1; i>=0; i--) { // backwards or die!
if (assocSpec[i]>=0) {
if (TRACE_EVTS) System.out.println("!!! Loner merge to " + i );
// loner i should be merged to species assocSpec[i]
AbstractEAIndividual tmpIndy = (AbstractEAIndividual)this.m_Undifferentiated.get(i);
if (m_Topology!=null) plotLine(m_Topology, tmpIndy, m_Species.get(assocSpec[i]).getBestEAIndividual());
@ -610,11 +715,13 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
if (!specToRemove.contains(assocSpec[i])) {
// the species has not yet been deactivated
specToRemove.add(assocSpec[i]);
collisions ++;
if (TRACE) System.out.println("Inactive merge - resetting " + spec1.size() + " surplus indies");
if (spec1.getBestEAIndividual().isDominating(aIndy)) {
// update the archived one with the better one? No rather not - it may happen that a large species is assoctiated which is quite large and spans over several optima - in that case an earlier found may get lost
// m_Archive.set(i, spec1.getBestEAIndividual());
}
if (TRACE_EVTS) System.out.println("!!! Reinit Spec " + assocSpec[i] + ", fit " + spec1.getBestEAIndividual());
m_doomedPop.addPopulation(spec1);
}
}
@ -634,7 +741,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
for (int i2 = i1+1; i2 < this.m_Species.size(); i2++) {
spec2 = (Population)this.m_Species.get(i2);
if (this.m_CAForSpeciesMerging.mergingSpecies(spec1, spec2, m_Population)) {
if (TRACE) System.out.println("----Merging species (" + i1 +", " +i2 +") ["+spec1.size()+"/"+spec2.size()+"]");
if (TRACE_EVTS || TRACE) System.out.println("!!! -Merging species (" + i1 +", " +i2 +") ["+spec1.size()+"/"+spec2.size()+"]");
mergeToFirst(spec1, spec2, true);
this.m_Species.remove(i2);
i2--;
@ -649,6 +757,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
for (int i=0; i<m_Species.size(); i++) {
Population curSpec = m_Species.get(i);
if (curSpec.size()>m_maxSpeciesSize) {
if (TRACE_EVTS) System.out.println("!!! Reinit indies " + (m_maxSpeciesSize - curSpec.size()));
ArrayList<AbstractEAIndividual> sorted = curSpec.getSorted(reduceSizeComparator);
for (int k=m_maxSpeciesSize; k<sorted.size();k++) {
if (curSpec.remove(sorted.get(k))) {
@ -705,6 +814,21 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/**
* Initialize the clustering method for differentiation.
*
*/
private void initClustering() {
if (getClusterDiffDist()>0) { // assume that it should be set
if (this.m_CAForSpeciesDifferentation instanceof InterfaceClusteringDistanceParam) {
((InterfaceClusteringDistanceParam)m_CAForSpeciesDifferentation).setClustDistParam(getClusterDiffDist());
if (TRACE) System.out.println("### Clustering distance parameter set to "+ getClusterDiffDist());
} else EVAERROR.errorMsgOnce("Warning: cluster distance is defined in CBN but the clustering method " + m_CAForSpeciesDifferentation.getClass() + " cant interpret it!");
}
this.m_CAForSpeciesDifferentation.initClustering(m_Population);
}
//
// /**
// * Unite all current species and the undiff. pop and return
@ -727,7 +851,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
* @param pop
*/
private void replaceUndifferentiated(Population pop) {
// this.m_Undifferentiated = ClusterResult[0];
// System.out.println("Adding " + pop.size() + " as undiff.");
m_Undifferentiated.clear();
m_Undifferentiated.addPopulation(pop);
}
@ -760,6 +884,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
* @param pop2
*/
protected void mergeToFirst(Population spec1, Population spec2, boolean plot) {
// System.out.println("Merging " + spec2.size() + " to " + spec1.size());
if (plot && (m_Topology!=null)) plotLine(m_Topology, spec1.getBestEAIndividual(), spec2.getBestEAIndividual());
spec1.addPopulation(spec2);
// keep longer history
@ -910,6 +1035,17 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public void setPopulation(Population pop){
this.m_Undifferentiated = pop;
if (m_Archive==null) m_Archive = new Population();
m_Archive.setPopMetric(pop.getPopMetric());
m_Population.setPopMetric(pop.getPopMetric());
m_doomedPop.setPopMetric(pop.getPopMetric());
if (m_CAForSpeciesDifferentation instanceof InterfaceClusteringMetricBased) {
((InterfaceClusteringMetricBased)m_CAForSpeciesDifferentation).setMetric(pop.getPopMetric());
}
if (m_CAForSpeciesMerging instanceof InterfaceClusteringMetricBased) {
((InterfaceClusteringMetricBased)m_CAForSpeciesMerging).setMetric(pop.getPopMetric());
}
pop.setUseHistory(true);
}
@ -917,10 +1053,21 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return "Edit the properties of the population used.";
}
public Population getArchivedSolutions() {
return (Population)m_Archive.clone();
}
public SolutionSet getAllSolutions() {
// return inactive species
Population sols = (Population)m_Archive.clone();
sols.addPopulation(getPopulation());
Population sols = getArchivedSolutions();
// sols.addPopulation(getPopulation());
for (Population sp : m_Species) {
sols.add(sp.getBestIndividual());
}
if (m_Undifferentiated.size()>0) sols.add(m_Undifferentiated.getBestIndividual());
// if (!sols.checkNoNullIndy()) {
// System.err.println("error in CBN...");
// }
sols.synchSize();
return new SolutionSet(getPopulation(), sols);
}
@ -1027,6 +1174,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
}
public void setShowCycle(int b){
this.m_ShowCycle = b;
if (b<=0) m_Topology=null;
}
public String showCycleTipText() {
return "Determines how often show is performed (generations); set to zero to deactivate.";
@ -1144,9 +1292,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
public String[] getAdditionalDataHeader() {
return new String[]{"numUndiff","numActSpec","avgSpecMeas","numArchived",
"archivedMedCorr", "archivedMeanDist"
// , "numCollisions", "clustSig"
};
"archivedMedCorr", "archivedMeanDist", "numCollisions", "clustSig"};
}
public String[] getAdditionalDataInfo() {
@ -1157,8 +1303,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
"The number of stored potential local optima",
"The median correlation of archived solutions",
"The mean distance of archived solutions",
// "The number of collisions events that happened so far",
// "The clustering distance"
"The number of collisions events that happened so far",
"The clustering distance"
};
}
@ -1171,9 +1317,8 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
m_Archive.size(),
m_Archive.getCorrelations()[3],
m_Archive.getPopulationMeasures()[0],
// collisions,
// getClusterDiffDist()
};
collisions,
getClusterDiffDist()};
// return m_Undifferentiated.size() + " \t " + m_Species.size() + " \t " + BeanInspector.toString(getAvgSpeciesMeasures()[0]) + " \t " + (m_Archive.size());
}
@ -1219,4 +1364,64 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return new String[]{"mergingCA", "differentiationCA"};
}
// public void setHistComparator(AbstractEAIndividualComparator histComparator) {
// this.histComparator = histComparator;
// }
public AbstractEAIndividualComparator getHistComparator() {
return histComparator;
}
// public String histComparatorTipText() {
// return "The comparator to keep track of old optima. Should correspond to the clustering metric.";
// }
public void setClusterDiffDist(double clusterDiffDist) {
this.clusterDiffDist = clusterDiffDist;
if (clusterDiffDist<0) {
if ((m_Problem instanceof InterfaceProblemDouble) && (m_CAForSpeciesDifferentation instanceof ClusteringDensityBased)) {
// int numExpectedOptima = (int)((((double)getPopulationSize())*0.9)/((ClusteringDensityBased)m_CAForSpeciesDifferentation).getMinimumGroupSize());
// this.clusterDiffDist = EsDpiNiching.calcEstimatedNicheRadius(((AbstractProblemDouble)m_Problem).makeRange(), numExpectedOptima, new EuclideanMetric());
setUpperBoundClustDiff((InterfaceProblemDouble) m_Problem);
} else System.err.println("Warning, unable to calculate standard niche radius in CBN-EA");
}
}
/**
* Calculate the clustering parameter in such a way that about one q-th part
* of the range of the given problem is within one hyper sphere of the clustering parameter.
*
* For certain types of parameter adaption schemes, this automatically sets the upper limit
* if the clustering parameter is controlled.
*
* @param prob
* @param q
*/
public void setUpperBoundClustDiff(InterfaceProblemDouble prob) {
if (m_CAForSpeciesDifferentation instanceof ClusteringDensityBased) {
double meanSubSwarmSize=0.5*(((ClusteringDensityBased)m_CAForSpeciesDifferentation).getMinimumGroupSize()+getMaxSpeciesSize());
int numExpectedOptima = (int)((((double)getPopulationSize()))/meanSubSwarmSize);
double[][] range = ((InterfaceProblemDouble)m_Problem).makeRange();
int dim = range.length;
double nRad = EsDpiNiching.calcEstimatedNicheRadius(range, numExpectedOptima, ((ClusteringDensityBased) m_CAForSpeciesDifferentation).getMetric());
nRad = nRad * Math.pow(0.5, 1/dim);
// System.out.println("Alternative clust diff from niche radius... " + nRad);
this.clusterDiffDist = nRad;
// System.out.println("Setting the clusterDiffDist to "+ clusterDiffDist);
ParamAdaption[] adaptors = getParameterControl();
if (adaptors.length>0) {
for (ParamAdaption adpt : adaptors) {
if (adpt.getControlledParam().equals("clusterDiffDist")) {
if (adpt instanceof InterfaceHasUpperDoubleBound) {
((InterfaceHasUpperDoubleBound)adpt).SetUpperBnd(clusterDiffDist);
} else System.err.println("Warning, unknown parameter adaption type for automatic setting of upper bound of the clustering sigma (CBN-EA)");
}
}
}
// double estRad = EsDpiNiching.calcEstimatedNicheRadius(prob.makeRange(), expectedPeaks, metric);
// setClusterDiffDist(estRad);
} else System.err.println("Warning, unable to calculate standard niche radius in CBN-EA");
}
public double getClusterDiffDist() {
return clusterDiffDist;
}
}

1137
src/eva2/server/go/strategies/EsDpiNiching.java Normal file
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47
src/eva2/server/go/strategies/EsDpiNichingCma.java Normal file
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@ -0,0 +1,47 @@
package eva2.server.go.strategies;
import eva2.server.go.operators.selection.SelectBestSingle;
public class EsDpiNichingCma extends EsDpiNiching {
/**
* Preset all values according to the (1,lambda)-ES. This activates the automatic niche radius estimation
* (which frequently overestimates) and expects 10 peaks.
*/
public EsDpiNichingCma() {
this(10, 10, 0, 0);
}
/**
* Preset all values according to the (1,lambda)-ES
*/
public EsDpiNichingCma(double nicheRadius, int lambda, int expectedPeaks) {
this(nicheRadius, lambda, expectedPeaks, 0, 0);
}
/**
* Preset all values according to the (1,lambda)-ES
*/
public EsDpiNichingCma(int lambda, int expectedPeaks, int explorerPeaks, int resetExplInterval) {
this(-1, lambda, expectedPeaks, explorerPeaks, resetExplInterval);
}
/**
* Preset all values according to the (1,lambda)-ES
*/
public EsDpiNichingCma(double nicheRadius, int lambda, int expectedPeaks, int explorerPeaks, int resetExplInterval) {
super(nicheRadius, 1, lambda, expectedPeaks, 0, explorerPeaks, resetExplInterval, 0);
setParentSelection(new SelectBestSingle());
setAllowSingularPeakPops(true);
}
@Override
public String getName() {
return "CMA-"+super.getName();
}
public static String globalInfo() {
return "A variant of the DPI Niching ES to be usable with CMA-Mutation (Shir&Bäck, CEC'05). " +
"Remember to turn off crossover for lambda=1, and to set CMA as mutation in the individual template.";
}
}

24
src/eva2/server/go/strategies/EvolutionStrategies.java
View File

@ -1,11 +1,8 @@
package eva2.server.go.strategies;
import eva2.gui.BeanInspector;
import eva2.gui.GenericObjectEditor;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.operators.mutation.InterfaceAdaptOperatorGenerational;
import eva2.server.go.operators.mutation.MutateESSuccessRule;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectBestIndividuals;
import eva2.server.go.operators.selection.SelectRandom;
@ -43,13 +40,15 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
private InterfaceSelection m_PartnerSelection = new SelectRandom();
private InterfaceSelection m_EnvironmentSelection = new SelectBestIndividuals();
private int m_NumberOfPartners = 1;
private int origPopSize = -1; // especially for CBN
protected int origPopSize = -1; // especially for CBN
// private double[] m_FitnessOfParents = null;
private boolean forceOrigPopSize = true;// especially for CBN
transient private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
private static final boolean TRACE = false;
public static final String esMuParam = "EvolutionStrategyMuParameter";
public static final String esLambdaParam = "EvolutionStrategyLambdaParameter";
public EvolutionStrategies() {
this.m_Population.setTargetSize(this.m_Lambda);
@ -63,15 +62,18 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
}
public EvolutionStrategies(EvolutionStrategies a) {
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_Mu = a.m_Mu;
this.m_Lambda = a.m_Lambda;
this.m_UsePlusStrategy = a.m_UsePlusStrategy;
this.m_Population = (Population)a.m_Population.clone();
this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
this.m_NumberOfPartners = a.m_NumberOfPartners;
this.m_ParentSelection = (InterfaceSelection)a.m_ParentSelection.clone();
this.m_PartnerSelection = (InterfaceSelection)a.m_PartnerSelection.clone();
this.m_EnvironmentSelection = (InterfaceSelection)a.m_EnvironmentSelection.clone();
this.m_NumberOfPartners = a.m_NumberOfPartners;
this.origPopSize = a.origPopSize;
this.forceOrigPopSize = a.forceOrigPopSize;
}
/**
@ -83,7 +85,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
}
public void hideHideable() {
GenericObjectEditor.setHideProperty(this.getClass(), "population", true);
// GenericObjectEditor.setHideProperty(this.getClass(), "population", true);
}
public Object clone() {
@ -97,8 +99,10 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
// this.m_Population.setPopulationSize(this.m_InitialPopulationSize);
// }
//System.out.println("init");
checkPopulationConstraints();
m_Population.putData(esMuParam, getMu());
m_Population.putData(esLambdaParam, getLambda());
this.m_Problem.initPopulation(this.m_Population);
this.evaluatePopulation(this.m_Population);
// this.m_Population.setPopulationSize(orgPopSize);
@ -128,6 +132,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
protected void evaluatePopulation(Population population) {
this.m_Problem.evaluate(population);
population.incrGeneration();
if (TRACE) System.out.println("ES Evaluated " + population.size());
}
// /** This method allows you to set myu and lambda
@ -183,7 +188,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
AbstractEAIndividual tmpIndy;
AbstractEAIndividual[] offSprings;
Population parents;
if (TRACE ) System.out.println("ES From pop size " + fromPopulation.size() + " selecting parents/creating children: " + lambda);
this.m_ParentSelection.prepareSelection(fromPopulation);
this.m_PartnerSelection.prepareSelection(fromPopulation);
parents = this.m_ParentSelection.selectFrom(fromPopulation, lambda);
@ -198,6 +203,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
}
protected Population selectParents(Population fromPop, int mu) {
if (TRACE) System.out.println("ES env selecting parents: " + mu + " of " + fromPop.size());
this.m_EnvironmentSelection.prepareSelection(fromPop);
return this.m_EnvironmentSelection.selectFrom(fromPop, mu);
}
@ -428,10 +434,12 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
// for internal usage
protected void setPop(Population pop) {
if (TRACE) System.out.println("ES Setting pop of " + pop.size());
m_Population = pop;
}
public void setPopulation(Population pop){
if (TRACE) System.out.println("ES Setting pop of " + pop.size());
origPopSize = pop.size();
// System.err.println("In ES: orig popsize is " + origPopSize);
this.m_Population = pop;

45
src/eva2/server/go/strategies/SqPSO.java Normal file
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@ -0,0 +1,45 @@
package eva2.server.go.strategies;
import eva2.server.go.enums.PSOTopologyEnum;
import eva2.server.go.operators.cluster.ClusteringDensityBased;
import eva2.server.go.operators.distancemetric.IndividualDataMetric;
import eva2.server.go.operators.paramcontrol.ParamAdaption;
/**
* A thunk class preconfiguring CBN-EA to function as a sequential niching method. This
* is to be comparable to parallel and semi-sequential niching (esp. using the same convergence
* criterion).
*
* @author mkron
*
*/
public class SqPSO extends ClusterBasedNichingEA {
public SqPSO() {
this(1e-10, 15, 15); // default values
}
public SqPSO(double epsilonConv, int haltingWindow, int popSize) {
super();
setClusterDiffDist(Double.MAX_VALUE);
setMaxSpeciesSize(-1);
// dummy: cluster all always
setDifferentiationCA(new ClusteringDensityBased(Double.MAX_VALUE, 1,
new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey)));
// just a dummy
setMergingCA(new ClusteringDensityBased(0., 0, new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey)));
setEpsilonBound(epsilonConv);
setHaltingWindow(haltingWindow);
setMaxSpeciesSize(popSize);
setOptimizer(new ParticleSwarmOptimization(popSize, 2.05, 2.05, PSOTopologyEnum.grid, 2));
ParamAdaption[] defAdpt = new ParamAdaption[]{};
setParameterControl(defAdpt);
// if (threshAdaption) addParameterControl(getDefaultThreshAdaption());
setPopulationSize(popSize);
}
// public void hideHideable()
public String getName() {
return "SqPSO";
}
}