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See JOpt-Notes.txt for changelog (11.03.08)

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This commit is contained in:
Marcel Kronfeld 2008-03-11 10:57:37 +00:00
parent c794bf44a0
commit 107670428b
87 changed files with 2309 additions and 2017 deletions
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86
src/javaeva/OptimizerFactory.java
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@ -5,18 +5,21 @@ import javaeva.server.go.IndividualInterface;
import javaeva.server.go.InterfaceTerminator;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.InterfaceESIndividual;
import javaeva.server.go.operators.cluster.ClusteringDensityBased;
import javaeva.server.go.operators.crossover.CrossoverESDefault;
import javaeva.server.go.operators.mutation.MutateESCovarianceMartixAdaption;
import javaeva.server.go.operators.mutation.MutateESDefault;
import javaeva.server.go.operators.mutation.MutateESGlobal;
import javaeva.server.go.operators.terminators.CombinedTerminator;
import javaeva.server.go.operators.terminators.EvaluationTerminator;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.AbstractOptimizationProblem;
import javaeva.server.go.strategies.ClusterBasedNichingEA;
import javaeva.server.go.strategies.DifferentialEvolution;
import javaeva.server.go.strategies.EvolutionStrategies;
import javaeva.server.go.strategies.GeneticAlgorithm;
import javaeva.server.go.strategies.HillClimbing;
import javaeva.server.go.strategies.InterfaceOptimizer;
import javaeva.server.go.strategies.MonteCarloSearch;
import javaeva.server.go.strategies.ParticleSwarmOptimization;
import javaeva.server.go.strategies.Tribes;
import javaeva.server.modules.GOParameters;
@ -37,6 +40,9 @@ public class OptimizerFactory {
public final static int PSO = 4;
public final static int DE = 5;
public final static int TRIBES = 6;
public final static int RANDOM = 7;
public final static int HILLCL = 8;
public final static int CBN_ES = 9;
public final static int defaultFitCalls = 10000;
public final static int randSeed = 0;
@ -47,7 +53,7 @@ public class OptimizerFactory {
* @return a String listing the accessible optimizers
*/
public static String showOptimizers() {
return "1: Standard ES; 2: CMA-ES; 3: GA; 4: PSO; 5: DE; 6: Tribes";
return "1: Standard ES; 2: CMA-ES; 3: GA; 4: PSO; 5: DE; 6: Tribes; 7: Random (Monte Carlo); 8: HillClimbing; 9: Cluster-based niching ES";
}
/**
@ -84,22 +90,45 @@ public class OptimizerFactory {
}
public static OptimizerRunnable getOptRunnable(final int optType, AbstractOptimizationProblem problem, String outputFilePrefix) {
return getOptRunnable(optType, problem, defaultFitCalls, outputFilePrefix);
}
public static OptimizerRunnable getOptRunnable(final int optType, AbstractOptimizationProblem problem, int fitCalls, String outputFilePrefix) {
OptimizerRunnable opt = null;
switch (optType) {
case STD_ES:
return new OptimizerRunnable(standardES(problem), outputFilePrefix);
opt = new OptimizerRunnable(standardES(problem), outputFilePrefix);
break;
case CMA_ES:
return new OptimizerRunnable(cmaES(problem), outputFilePrefix);
opt = new OptimizerRunnable(cmaES(problem), outputFilePrefix);
break;
case STD_GA:
return new OptimizerRunnable(standardGA(problem), outputFilePrefix);
opt = new OptimizerRunnable(standardGA(problem), outputFilePrefix);
break;
case PSO:
return new OptimizerRunnable(standardPSO(problem), outputFilePrefix);
opt = new OptimizerRunnable(standardPSO(problem), outputFilePrefix);
break;
case DE:
return new OptimizerRunnable(standardDE(problem), outputFilePrefix);
opt = new OptimizerRunnable(standardDE(problem), outputFilePrefix);
break;
case TRIBES:
return new OptimizerRunnable(tribes(problem), outputFilePrefix);
opt = new OptimizerRunnable(tribes(problem), outputFilePrefix);
break;
case RANDOM:
opt = new OptimizerRunnable(monteCarlo(problem), outputFilePrefix);
break;
case HILLCL:
opt = new OptimizerRunnable(hillClimbing(problem), outputFilePrefix);
break;
case CBN_ES:
opt = new OptimizerRunnable(cbnES(problem), outputFilePrefix);
break;
default:
System.err.println("Error: optimizer type " + optType + " is unknown!");
return null;
}
System.err.println("Error: optimizer type " + optType + " is unknown!");
return null;
if (fitCalls != defaultFitCalls) opt.getGOParams().setTerminator(new EvaluationTerminator(fitCalls));
return opt;
}
public static InterfaceTerminator defaultTerminator() {
@ -167,6 +196,7 @@ public class OptimizerFactory {
es.setLambda(50);
es.setPlusStrategy(false);
// TODO improve this by adding getEAIndividual to AbstractEAIndividual?
Object maybeTemplate = BeanInspector.callIfAvailable(problem, "getEAIndividual", null);
if ((maybeTemplate != null) && (maybeTemplate instanceof InterfaceESIndividual)) {
// Set CMA operator for mutation
@ -227,4 +257,40 @@ public class OptimizerFactory {
problem.initPopulation(pop);
return makeParams(tr, pop, problem, randSeed, defaultTerminator());
}
public static GOParameters hillClimbing(AbstractOptimizationProblem problem) {
HillClimbing hc = new HillClimbing();
hc.SetProblem(problem);
Population pop = new Population();
pop.setPopulationSize(50);
problem.initPopulation(pop);
return makeParams(hc, pop, problem, randSeed, defaultTerminator());
}
public static GOParameters monteCarlo(AbstractOptimizationProblem problem) {
MonteCarloSearch mc = new MonteCarloSearch();
Population pop = new Population();
pop.setPopulationSize(50);
problem.initPopulation(pop);
return makeParams(mc, pop, problem, randSeed, defaultTerminator());
}
public static GOParameters cbnES(AbstractOptimizationProblem problem) {
ClusterBasedNichingEA cbn = new ClusterBasedNichingEA();
EvolutionStrategies es = new EvolutionStrategies();
es.setMyu(15);
es.setLambda(50);
es.setPlusStrategy(false);
cbn.setOptimizer(es);
ClusteringDensityBased clustering = new ClusteringDensityBased(0.1);
cbn.setConvergenceCA((ClusteringDensityBased)clustering.clone());
cbn.setDifferentationCA(clustering);
cbn.setShowCycle(10); // dont do graphical output
Population pop = new Population();
pop.setPopulationSize(100);
problem.initPopulation(pop);
return makeParams(cbn, pop, problem, randSeed, defaultTerminator());
}
}

17
src/javaeva/OptimizerRunnable.java
View File

@ -25,9 +25,15 @@ import javaeva.server.stat.StatisticsStandalone;
public class OptimizerRunnable implements Runnable {
Processor proc;
boolean isFinished = false;
boolean doRestart = false; // indicate whether start or restart should be done --> whether pop will be reinitialized.
public OptimizerRunnable(GOParameters params, String outputFilePrefix) {
this(params, outputFilePrefix, false);
}
public OptimizerRunnable(GOParameters params, String outputFilePrefix, boolean restart) {
proc = new Processor(new StatisticsStandalone(outputFilePrefix), null, params);
doRestart = restart;
}
public InterfaceGOParameters getGOParams() {
@ -37,7 +43,9 @@ public class OptimizerRunnable implements Runnable {
public void run() {
isFinished = false;
try {
proc.startOpt();
proc.setSaveParams(false);
if (doRestart) proc.restartOpt();
else proc.startOpt();
proc.runOptOnce();
} catch(Exception e) {
proc.getStatistics().printToTextListener("Exception in OptimizeThread::run: " + e.getMessage() + "\n");
@ -63,11 +71,14 @@ public class OptimizerRunnable implements Runnable {
return proc.getStatistics().getBestSolution();
}
public int getProgress() {
return proc.getGOParams().getOptimizer().getPopulation().getFunctionCalls();
}
public String terminatedBecause() {
if (isFinished) {
InterfaceTerminator term = proc.getGOParams().getTerminator();
if (term instanceof CombinedTerminator) return ((CombinedTerminator)term).terminatedBecause(proc.getGOParams().getOptimizer().getPopulation());
else return "Terminated because " + BeanInspector.toString(term) + " terminated;";
return term.terminatedBecause(proc.getGOParams().getOptimizer().getPopulation());
} else return "Not yet terminated";
}

19
src/javaeva/gui/BeanInspector.java
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@ -12,9 +12,13 @@ package javaeva.gui;
/*==========================================================================*
* IMPORTS
*==========================================================================*/
import java.beans.*;
import java.beans.BeanInfo;
import java.beans.IntrospectionException;
import java.beans.Introspector;
import java.beans.PropertyDescriptor;
import java.lang.reflect.Array;
import java.lang.reflect.Method;
import java.util.List;
/*
* ==========================================================================*
@ -143,6 +147,19 @@ public class BeanInspector {
return sbuf.toString();
}
if (Target instanceof List) { // handle the list case
StringBuffer sbuf = new StringBuffer("[ ");
List lst = (List)Target;
for (Object o : lst) {
sbuf.append(o.toString());
sbuf.append("; ");
}
sbuf.setCharAt(sbuf.length()-2, ' ');
sbuf.setCharAt(sbuf.length()-1, ']');
return sbuf.toString();
}
Method[] methods = Target.getClass().getDeclaredMethods();
for (int ii = 0; ii < methods.length; ii++) { // check if the object has its own toString method, in this case use it
if ((methods[ii].getName().equals("toString") /*|| (methods[ii].getName().equals("getStringRepresentation"))*/) && (methods[ii].getParameterTypes().length == 0)) {

72
src/javaeva/gui/FunctionArea.java
View File

@ -268,18 +268,33 @@ public class FunctionArea extends DArea implements Serializable {
/**
*
*/
public void setConnectedPoint(double x, double y, int GraphLabel) {
if (m_log == true && y <= 0.0) {
y = 1;
// y = Double.MIN_VALUE;
if (notifyNegLog) {
System.err.println("Warning: trying to plot value (" + x + "/" + y + ") with y < 0 in logarithmic mode! Setting y to " + y);
notifyNegLog = false;
}
public void setConnectedPoint(double x, double y, int graphLabel) {
if (!checkLogValidYValue(x, y, graphLabel)) {
if (m_log) toggleLog();
}
getGraphPointSet(GraphLabel).addDPoint(x, y);
// if (y <= 0.0) {
//// y = Double.MIN_VALUE;
// if (notifyNegLog) {
// System.err.println("Warning: trying to plot value (" + x + "/" + y + ") with y <= 0 in logarithmic mode! Setting y to " + 1e-30);
// notifyNegLog = false;
// }
// y = 1e-30;
// }
getGraphPointSet(graphLabel).addDPoint(x, y);
}
// public void setConnectedPoint(double x, double y, int GraphLabel) {
// if (m_log == true && y <= 0.0) {
//// y = Double.MIN_VALUE;
// if (notifyNegLog) {
// System.err.println("Warning: trying to plot value (" + x + "/" + y + ") with y <= 0 in logarithmic mode! Setting y to " + 1e-30);
// notifyNegLog = false;
// }
// y = 1e-30;
// }
// getGraphPointSet(GraphLabel).addDPoint(x, y);
//
// }
public void addGraphPointSet(GraphPointSet d) {
this.m_PointSetContainer.add(d);
@ -297,9 +312,9 @@ public class FunctionArea extends DArea implements Serializable {
/**
*
*/
public void clearGraph(int GraphLabel) {
getGraphPointSet(GraphLabel).removeAllPoints();
m_PointSetContainer.remove(getGraphPointSet(GraphLabel));
public void clearGraph(int graphLabel) {
getGraphPointSet(graphLabel).removeAllPoints();
m_PointSetContainer.remove(getGraphPointSet(graphLabel));
repaint();
notifyNegLog = true;
}
@ -403,18 +418,35 @@ public class FunctionArea extends DArea implements Serializable {
*
*/
public void setUnconnectedPoint(double x, double y, int GraphLabel) {
if (m_log == true && y <= 0.0) {
if (notifyNegLog) {
System.err.println("Warning: trying to plot value (" + x + "/" + y + ") with y < 0 in logarithmic mode! Setting y to " + y );
notifyNegLog = false;
}
y = 1;
if (!checkLogValidYValue(x, y, GraphLabel)) {
if (m_log) toggleLog();
}
this.getGraphPointSet(GraphLabel).addDPoint(x, y);
this.getGraphPointSet(GraphLabel).setConnectedMode(false);
repaint();
}
protected boolean checkLoggable() {
double minY = Double.MAX_VALUE;
for (int i = 0; i < m_PointSetContainer.size(); i++) {
DPointSet pSet = (m_PointSetContainer.get(i).getConnectedPointSet());
if (pSet.getSize() > 0) minY = Math.min(minY, pSet.getMinYVal());
}
// if (TRACE) System.out.println("min is " + minY);
return (minY > 0);
}
protected boolean checkLogValidYValue(double x, double y, int graphLabel) {
if (y <= 0.0) {
if (m_log && notifyNegLog) {
System.err.println("Warning: trying to plot value (" + x + "/" + y + ") with y <= 0 in logarithmic mode!" );
notifyNegLog = false;
}
return false;
}
return true;
}
Color[] Colors = new Color[] {Color.black, Color.red, Color.blue, Color.green,Color.magenta, Color.orange, Color.pink, Color.yellow};
public void setGraphColor(int GraphLabel,int colorindex) {
@ -467,6 +499,10 @@ public class FunctionArea extends DArea implements Serializable {
*/
public void toggleLog() {
//System.out.println("ToggleLog log was: "+m_log);
if (!m_log && !checkLoggable()) {
System.err.println("Cant toggle log with values <= 0!");
return;
}
if (m_log == false) {
setMinRectangle(0.001, 0.001, 1, 1);
//setVisibleRectangle( 0.001, 0.001, 100000, 1000 );

2
src/javaeva/gui/Graph.java
View File

@ -79,7 +79,7 @@ public class Graph implements Serializable {
* Add a graph to this graph object. Uses "force" for mismatching point counts, but returns false
* if force was used and points possibly have been lost.
*
* @return true if the graph could be added direcly or false if the graph was added by force losing some data points
* @return true if the graph could be added directly or false if the graph was added by force losing some data points
* @see PlotInterface.addGraph
*/
public boolean addGraph(Graph x) {

65
src/javaeva/gui/GraphPointSet.java
View File

@ -105,19 +105,7 @@ public class GraphPointSet {
// else
// if (GraphLabel == 10) m_Color = (Color.black);
int k = GraphLabel%10;
switch(k) {
case 0: m_Color = Color.black; break;
case 1: m_Color = Color.red; break;
case 2: m_Color = Color.blue; break;
case 3: m_Color = Color.pink; break;
case 4: m_Color = Color.orange; break;
case 5: m_Color = Color.gray; break;
case 6: m_Color = Color.green; break;
case 7: m_Color = Color.magenta; break;
case 8: m_Color = Color.cyan; break;
case 9: m_Color = Color.darkGray; break;
}
m_Color=labelToColor(GraphLabel);
m_ConnectedPointSet.setColor(m_Color);
// m_PointSet_1.setColor(m_Color);
@ -125,6 +113,24 @@ public class GraphPointSet {
// m_PointSet_3.setColor(m_Color);
initGraph(Area);
}
private Color labelToColor(int label) {
Color c = Color.black;
int k = label%10;
switch(k) {
case 0: c = Color.black; break;
case 1: c = Color.red; break;
case 2: c = Color.blue; break;
case 3: c = Color.pink; break;
case 4: c = Color.orange; break;
case 5: c = Color.gray; break;
case 6: c = Color.green; break;
case 7: c = Color.magenta; break;
case 8: c = Color.cyan; break;
case 9: c = Color.darkGray; break;
}
return c;
}
/**
*
*/
@ -158,40 +164,11 @@ public class GraphPointSet {
m_ConnectedPointSet.setConnected(true);
m_Color = Color.black;
// int colors = 5;
// int b = GraphLabel*(int)(255/10)%255;
// int r = (GraphLabel-colors)*(int)(255/10)%255;
// int g = (GraphLabel-2*colors)*(int)(255/10)%255;
// if (r<0) r = 0;
// if (g<0) g = 0;
// m_Color = new Color(r,g,b);
if (GraphLabel == 0) m_Color = (Color.black);
else
if (GraphLabel == 1) m_Color = (Color.red);
else
if (GraphLabel == 2) m_Color = (Color.blue);
else
if (GraphLabel == 3) m_Color = (Color.red);
else
if (GraphLabel == 4) m_Color = (Color.black);
else
if (GraphLabel == 5) m_Color = (Color.red);
else
if (GraphLabel == 6) m_Color = (Color.blue);
else
if (GraphLabel == 7) m_Color = (Color.red);
else
if (GraphLabel == 8) m_Color = (Color.blue);
else
if (GraphLabel == 9) m_Color = (Color.red);
else
if (GraphLabel == 10) m_Color = (Color.black);
m_Color = labelToColor(GraphLabel);
m_ConnectedPointSet.setColor(m_Color);
// m_PointSet_1.setColor(m_Color);
// m_PointSet_2.setColor(m_Color);
// m_PointSet_3.setColor(m_Color);
}
/**
*

8
src/javaeva/gui/JTextoutputFrame.java
View File

@ -52,16 +52,20 @@ public class JTextoutputFrame implements JTextoutputFrameInterface,
/**
*
*/
public void print (String Text) {
public void print(String Text) {
//System.out.println("Print:"+Text);
if (m_firstprint==true) {
m_firstprint = false;
createFrame();
}
m_TextArea.append (Text+"\n");
m_TextArea.append(Text);
m_TextArea.repaint();
}
public void println(String txt) {
print(txt+'\n');
}
public void setShow(boolean bShow) {
if (frame.isVisible() != bShow) {
if (frame.isVisible()) {

3
src/javaeva/gui/TopoPlot.java
View File

@ -90,7 +90,8 @@ public class TopoPlot extends Plot {
ry = problem.get2DBorder()[1][0]+y*rh;
pos[0] = rx; pos[1] = ry;
DRectangle rect = new DRectangle(rx,ry,rw,rh);
Color color = new Color(colorBar.getRGB((float)(problem.functionValue(pos)/fitRange))); // Color color = new Color(255,(int)(problem.doEvaluation(pos)[0]/fitRange*255),(int)(problem.doEvaluation(pos)[0]/fitRange*255));
Color color = new Color(colorBar.getRGB((float)((problem.functionValue(pos)-min)/fitRange))); // Color color = new Color(255,(int)(problem.doEvaluation(pos)[0]/fitRange*255),(int)(problem.doEvaluation(pos)[0]/fitRange*255));
// Color color = new Color(colorBar.getRGB((float)(problem.functionValue(pos)/fitRange))); // Color color = new Color(255,(int)(problem.doEvaluation(pos)[0]/fitRange*255),(int)(problem.doEvaluation(pos)[0]/fitRange*255));
rect.setColor(color);
rect.setFillColor(color);
m_PlotArea.addDElement(rect);

18
src/javaeva/server/go/InterfaceGOParameters.java
View File

@ -1,5 +1,7 @@
package javaeva.server.go;
import javaeva.gui.GenericObjectEditor;
import javaeva.server.go.operators.postprocess.InterfacePostProcessParams;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.server.go.strategies.InterfaceOptimizer;
@ -62,4 +64,20 @@ public interface InterfaceGOParameters {
// public void setOutputFileName (String name);
// public String getOutputFileName ();
// public String outputFileNameTipText();
public InterfacePostProcessParams getPostProcessParams();
public void setPostProcessParams(InterfacePostProcessParams ppp);
public String postProcessParamsTipText();
public void setDoPostProcessing(boolean doPP);
// public int getPostProcessSteps();
// public void setPostProcessSteps(int ppSteps);
// public String postProcessStepsTipText();
//
// public boolean isPostProcess();
// public void setPostProcess(boolean postProcess);
// public String postProcessTipText();
//
// public double getPostProcessClusterSigma();
// public void setPostProcessClusterSigma(double postProcessClusterSigma);
// public String postProcessClusterSigmaTipText();
}

1
src/javaeva/server/go/InterfacePopulationChangedEventListener.java
View File

@ -1,6 +1,5 @@
package javaeva.server.go;
import javaeva.server.go.populations.Population;
/**
* Created by IntelliJ IDEA.

14
src/javaeva/server/go/individuals/AbstractEAIndividual.java
View File

@ -645,7 +645,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
public static String getDefaultStringRepresentation(AbstractEAIndividual individual) {
StringBuffer sb = new StringBuffer(getDefaultDataString(individual));
sb.append("Fitness :");
sb.append(", fitness: ");
sb.append(BeanInspector.toString(individual.getFitness()));
return sb.toString();
}
@ -661,49 +661,41 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
StringBuffer sb = new StringBuffer("");
char left = '[';
char right = ']';
sb.append(left);
if (individual instanceof InterfaceDataTypeBinary) {
sb.append(left);
BitSet b = ((InterfaceDataTypeBinary)individual).getBinaryData();
for (int i = 0; i < ((InterfaceDataTypeBinary)individual).size(); i++) {
if (b.get(i)) sb.append("1");
else sb.append("0");
}
sb.append(right);
} else if (individual instanceof InterfaceDataTypeInteger) {
sb.append(left);
int[] b = ((InterfaceDataTypeInteger)individual).getIntegerData();
for (int i = 0; i < b.length; i++) {
sb.append(b[i]);
if ((i+1) < b.length) sb.append("; ");
}
sb.append(right);
} else if (individual instanceof InterfaceDataTypeDouble) {
sb.append(left);
double[] b = ((InterfaceDataTypeDouble)individual).getDoubleData();
for (int i = 0; i < b.length; i++) {
sb.append(b[i]);
if ((i+1) < b.length) sb.append("; ");
}
sb.append(right);
} else if (individual instanceof InterfaceDataTypePermutation) {
sb.append(left);
int[] b = ((InterfaceDataTypePermutation)individual).getPermutationData()[0];
for (int i = 0; i < b.length; i++) {
sb.append(b[i]);
if ((i+1) < b.length) sb.append("; ");
}
sb.append(right);
} else if (individual instanceof InterfaceDataTypeProgram) {
sb.append(left);
InterfaceProgram[] b = ((InterfaceDataTypeProgram)individual).getProgramData();
for (int i = 0; i < b.length; i++) {
sb.append(b[i].getStringRepresentation());
if ((i+1) < b.length) sb.append("; ");
}
sb.append(right);
} else {
System.err.println("error in AbstractEAIndividual::getDefaultDataString: type " + individual.getClass() + " not implemented");
}
sb.append(right);
return sb.toString();
}

24
src/javaeva/server/go/operators/cluster/ClusteringDensityBased.java
View File

@ -1,13 +1,12 @@
package javaeva.server.go.operators.cluster;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.InterfaceDataTypeDouble;
import javaeva.server.go.operators.distancemetric.InterfaceDistanceMetric;
import javaeva.server.go.operators.distancemetric.PhenotypeMetricDoubleData;
import javaeva.server.go.populations.Population;
import java.util.ArrayList;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.operators.distancemetric.InterfaceDistanceMetric;
import javaeva.server.go.operators.distancemetric.PhenotypeMetric;
import javaeva.server.go.populations.Population;
/** The DBSCAN method. As far as I recall this is an hierachical
* clustering method like the single-link method.
* Created by IntelliJ IDEA.
@ -18,7 +17,7 @@ import java.util.ArrayList;
*/
public class ClusteringDensityBased implements InterfaceClustering, java.io.Serializable {
private InterfaceDistanceMetric m_Metric = new PhenotypeMetricDoubleData();
private InterfaceDistanceMetric m_Metric = new PhenotypeMetric();
private double m_ClusterDistance = 0.1;
private int m_MinimumGroupSize = 3;
private boolean[][] ConnectionMatrix;
@ -26,7 +25,14 @@ public class ClusteringDensityBased implements InterfaceClustering, java.io.Seri
private boolean m_TestConvergingSpeciesOnBestOnly = true;
public ClusteringDensityBased() {
}
/**
* Directly set the minimum cluster distance sigma.
* @param sigma the minimum cluster distance
*/
public ClusteringDensityBased(double sigma) {
m_ClusterDistance = sigma;
}
public ClusteringDensityBased(ClusteringDensityBased a) {
@ -62,7 +68,7 @@ public class ClusteringDensityBased implements InterfaceClustering, java.io.Seri
/** This method allows you to search for clusters in a given population. The method
* returns Number of populations. The first population contains all individuals that
* could not be asociated with any cluster and may be empty.
* could not be associated with any cluster and may be empty.
* All other populations group individuals into clusters.
* @param pop The population of individuals that is to be clustered.
* @return Population[]
@ -83,7 +89,7 @@ public class ClusteringDensityBased implements InterfaceClustering, java.io.Seri
for (int i = 0; i < pop.size(); i++) {
tmpIndy1 = (AbstractEAIndividual)pop.get(i);
ConnectionMatrix[i][i] = true;
for (int j = i; j < pop.size(); j++) {
for (int j = i+1; j < pop.size(); j++) {
tmpIndy2 = (AbstractEAIndividual)pop.get(j);
if (this.m_Metric.distance(tmpIndy1, tmpIndy2) < this.m_ClusterDistance) {
ConnectionMatrix[i][j] = true;

18
src/javaeva/server/go/operators/cluster/ClusteringKMeans.java
View File

@ -1,20 +1,15 @@
package javaeva.server.go.operators.cluster;
import javaeva.gui.Chart2DDPointIconCircle;
import javaeva.gui.Chart2DDPointIconText;
import javaeva.gui.GraphPointSet;
import javaeva.gui.Plot;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.ESIndividualDoubleData;
import javaeva.server.go.individuals.InterfaceDataTypeDouble;
import javaeva.server.go.operators.distancemetric.InterfaceDistanceMetric;
import javaeva.server.go.operators.distancemetric.PhenotypeMetricDoubleData;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.F1Problem;
import javaeva.server.go.tools.RandomNumberGenerator;
import javaeva.gui.Plot;
import javaeva.gui.GraphPointSet;
import javaeva.gui.Chart2DDPointIconText;
import javaeva.gui.Chart2DDPointIconCircle;
import java.util.ArrayList;
import wsi.ra.chart2d.DPoint;
/** The k-mean clustering algorithms. I guess it is not a hierachical
@ -217,7 +212,10 @@ public class ClusteringKMeans implements InterfaceClustering, java.io.Serializab
double[][] data = this.extractClusterDataFrom(pop);
int clusterAssigned;
for (int i = 0; i < result.length; i++) result[i] = new Population();
for (int i = 0; i < result.length; i++) {
result[i] = new Population();
result[i].setSameParams(pop);
}
// let's assign the elements of the population to a c
for (int i = 0; i < data.length; i++) {
// find the closest c

17
src/javaeva/server/go/operators/cluster/InterfaceClustering.java
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@ -3,11 +3,9 @@ package javaeva.server.go.operators.cluster;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.populations.Population;
import java.util.ArrayList;
//import javaeva.server.oa.go.GraphicUserInterface.Plot.TopoPlot;
/** This the interface to clustering algorithms, but since there
* is no true concept on how to calculate a possbily problem
/**
* This the interface to clustering algorithms, but since there
* 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.
@ -26,14 +24,14 @@ public interface InterfaceClustering {
/** This method allows you to search for clusters in a given population. The method
* returns Number of populations. The first population contains all individuals that
* could not be asociated with any cluster and may be empty.
* could not be associated with any cluster and may be empty.
* All other populations group individuals into clusters.
* @param pop The population of individuals that is to be clustered.
* @return Population[]
*/
public Population[] cluster(Population pop);
/** This method allows you to decied if two species converge.
/** This method allows you to decide if two species converge.
* @param species1 The first species.
* @param species2 The second species.
* @return True if species converge, else False.
@ -46,9 +44,4 @@ public interface InterfaceClustering {
* @return True or False.
*/
public boolean belongsToSpecies(AbstractEAIndividual indy, Population species);
// /** For debuggy only
// * @param plot TopoPlot
// */
// public void draw(TopoPlot plot, Population pop);
}

66
src/javaeva/server/go/operators/distancemetric/PhenotypeMetricDoubleData.java
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@ -1,66 +0,0 @@
package javaeva.server.go.operators.distancemetric;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.InterfaceDataTypeDouble;
/** A Phenotype distance for double data, i guess
* this is guess for the clustering based niching.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 24.04.2003
* Time: 13:27:39
* To change this template use Options | File Templates.
*/
public class PhenotypeMetricDoubleData implements InterfaceDistanceMetric, java.io.Serializable {
public PhenotypeMetricDoubleData() {
}
public PhenotypeMetricDoubleData(PhenotypeMetricDoubleData a) {
}
public Object clone() {
return (Object) new PhenotypeMetricDoubleData(this);
}
/** This method allows you to compute the distance between two individuals.
* Depending on the metric this method may reject some types of individuals.
* The default return value would be 1.0.
* @param indy1 The first individual.
* @param indy2 The second individual.
* @return double
*/
public double distance(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
double[] dIndy1, dIndy2;
double[][] range1, range2;
double result = 0;
if ((indy1 instanceof InterfaceDataTypeDouble) && (indy2 instanceof InterfaceDataTypeDouble)) {
dIndy1 = ((InterfaceDataTypeDouble) indy1).getDoubleData();
range1 = ((InterfaceDataTypeDouble) indy1).getDoubleRange();
dIndy2 = ((InterfaceDataTypeDouble) indy2).getDoubleData();
range2 = ((InterfaceDataTypeDouble) indy2).getDoubleRange();
} else return 1.0;
for (int i = 0; (i < dIndy1.length) && (i < dIndy2.length); i++) {
result += Math.pow(((dIndy1[i] - range1[i][0])/(range1[i][1] - range1[i][0])) - ((dIndy2[i] - range2[i][0])/(range2[i][1] - range2[i][0])), 2);
}
return Math.sqrt(result);
}
/**********************************************************************************************************************
* These are for GUI
*/
/** This method returns a global info string
* @return description
*/
public String globalInfo() {
return "This is a phenotype based method suited for double data. Metric is computed on a normalized search space.";
}
/** This method will return a naming String
* @return The name of the algorithm
*/
public String getName() {
return "Phenotype Metric";
}
}

4
src/javaeva/server/go/operators/fitnessmodifier/FitnessSharing.java
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@ -2,7 +2,7 @@ package javaeva.server.go.operators.fitnessmodifier;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.operators.distancemetric.InterfaceDistanceMetric;
import javaeva.server.go.operators.distancemetric.PhenotypeMetricDoubleData;
import javaeva.server.go.operators.distancemetric.PhenotypeMetric;
import javaeva.server.go.populations.Population;
/** The fitness modifier are defunct and are to be moved to
@ -16,7 +16,7 @@ import javaeva.server.go.populations.Population;
public class FitnessSharing implements java.io.Serializable, InterfaceFitnessModifier {
private double m_SharingDistance = 0.05;
private InterfaceDistanceMetric m_Metric = new PhenotypeMetricDoubleData();
private InterfaceDistanceMetric m_Metric = new PhenotypeMetric();
/** This method allows you to modify the fitness of the individuals
* of a population. Note that by altering the fitness you may require

28
src/javaeva/server/go/operators/postprocess/InterfacePostProcessParams.java Normal file
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@ -0,0 +1,28 @@
package javaeva.server.go.operators.postprocess;
/**
* Parameters for an optional post processing of found solutions. Mainly contains
* parameters for a hill climbing step, namely the number of evaluations and
* the sigma condition for clustering. The idea is to optimize the best
* individual within each cluster.
*
* @author mkron
*
*/
public interface InterfacePostProcessParams {
public int getPostProcessSteps();
public void setPostProcessSteps(int ppSteps);
public String postProcessStepsTipText();
public boolean isDoPostProcessing();
public void setDoPostProcessing(boolean postProcess);
public String doPostProcessingTipText();
public double getPostProcessClusterSigma();
public void setPostProcessClusterSigma(double postProcessClusterSigma);
public String postProcessClusterSigmaTipText();
public int getPrintNBest();
public void setPrintNBest(int nBest);
public String printNBestTipText();
}

515
src/javaeva/server/go/operators/postprocess/PostProcess.java Normal file
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@ -0,0 +1,515 @@
package javaeva.server.go.operators.postprocess;
import java.io.PrintStream;
import java.util.Collection;
import java.util.List;
import javaeva.OptimizerFactory;
import javaeva.OptimizerRunnable;
import javaeva.gui.BeanInspector;
import javaeva.server.go.InterfaceTerminator;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.operators.cluster.ClusteringDensityBased;
import javaeva.server.go.operators.cluster.InterfaceClustering;
import javaeva.server.go.operators.distancemetric.InterfaceDistanceMetric;
import javaeva.server.go.operators.distancemetric.PhenotypeMetric;
import javaeva.server.go.operators.mutation.InterfaceMutation;
import javaeva.server.go.operators.mutation.MutateESFixedStepSize;
import javaeva.server.go.operators.terminators.EvaluationTerminator;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.AbstractOptimizationProblem;
import javaeva.server.go.problems.FM0Problem;
import javaeva.server.go.problems.InterfaceMultimodalProblem;
import javaeva.server.go.problems.InterfaceMultimodalProblemKnown;
import javaeva.server.go.strategies.HillClimbing;
import javaeva.server.stat.InterfaceTextListener;
import javaeva.tools.Pair;
/**
* Postprocess a population / list of individuals to find out a set of distinct optima.
*
* @author mkron
*
*/
public class PostProcess {
protected static InterfaceDistanceMetric metric = new PhenotypeMetric();
private static final boolean TRACE = false;
// the default mutation step size for HC post processing
private static double defaultMutationStepSize = 0.01;
public static final int BEST_ONLY = 1;
public static final int BEST_RAND = 2;
public static final int RAND_ONLY = 3;
public static final int KEEP_LONERS = 11;
public static final int DISCARD_LONERS = 12;
public static final int LONERS_AS_CLUSTERS = 13;
/**
* This method returns a set of individuals corresponding to an optimum in a given list.
* The individuals returned are to be nearer than epsilon to a given optimum. For each optimum, there is
* returned zero or one individual at max.
* If there are several individuals close to an optimum, the fitter one or the closer one
* may be selected, indicated by the boolean flag bTakeFitter. This means, that an individual may be
* returned in more than one copy if the optima are close together and the individual lies in between.
* The returned array may contain null values if an optimum is not considered found at all.
*
* @param pop A population of possible solutions.
* @param optima a set of predefined optima
* @param epsilon the threshold up to which an optimum is considered found.
* @param bTakeFitter if true, the fitter of two close individuals is selected, otherwise the closer one
* @return an array of individuals corresponding to the optimum with the same index
*/
public static AbstractEAIndividual[] getFoundOptimaArray(Population pop, Population optima, double epsilon, boolean bTakeFitter) {
AbstractEAIndividual candidate, opt;
// Population result = new Population(5);
AbstractEAIndividual[] found = new AbstractEAIndividual[optima.size()];
double indDist;
for (int i = 0; i < found.length; i++) found[i] = null;
for (int i = 0; i < pop.size(); i++) {
candidate = (AbstractEAIndividual) pop.get(i);
for (int j = 0; j < optima.size(); j++) {
opt = (AbstractEAIndividual) optima.get(j);
indDist = metric.distance(candidate, opt);
if (found[j] == null) { // current optimum has not been found yet
if (indDist < epsilon) {
found[j] = (AbstractEAIndividual)candidate.clone();
// result.add(found[j]);
}
} else {// there was another one found. set the fitter one or the closer one
if (indDist < epsilon) {
if ((bTakeFitter && (candidate.isDominatingDebConstraints(found[j]))) // flag "fitter" and new one is fitter
|| (!bTakeFitter && (indDist < metric.distance(found[j], opt)))) { // flag "closer" and new one is closer
// int index = result.indexOf(found[j]); // do replacement
found[j] = (AbstractEAIndividual)candidate.clone();
// result.set(index, found[j]);
}
}
}
}
}
return found;
}
/**
* Convenience method for getFoundOptimaArray(), returning the same set of optima in a Population.
*
* @see getFoundOptimaArray(Population pop, Population optima, double epsilon, boolean bTakeFitter)
* @param pop A population of possible solutions.
* @param optima a set of known optima
* @param epsilon the threshold up to which an optimum is considered found.
* @param bTakeFitter if true, the fitter of two close individuals is selected, otherwise the closer one
* @return a Population of individuals corresponding to the given optima
*/
public static Population getFoundOptima(Population pop, Population optima, double epsilon, boolean bTakeFitter) {
Population result = new Population(5);
AbstractEAIndividual[] optsFound = getFoundOptimaArray(pop, optima, epsilon, bTakeFitter);
for (int i=0; i<optsFound.length; i++) {
if (optsFound[i] != null) result.add(optsFound[i]);
}
return result;
}
/**
* Calls clusterBest with a ClusteringDensitiyBased clustering object with the given sigma.
* @see clusterBest(Population pop, InterfaceClustering clustering, double returnQuota, int lonerMode, int takeOverMode)
* @param pop
* @param sigmaCluster
* @param returnQuota
* @param lonerMode
* @param takeOverMode
* @return
*/
public static Population clusterBest(Population pop, double sigmaCluster, double returnQuota, int lonerMode, int takeOverMode) {
return clusterBest(pop, new ClusteringDensityBased(sigmaCluster), returnQuota, lonerMode, takeOverMode);
}
/**
* Cluster the population. Return for every cluster a subset of representatives which are the best individuals
* or a random subset of the cluster or the best and a random subset. Returns shallow copies!
* returnQuota should be in [0,1] and defines, which percentage of individuals of each cluster is kept, however
* if returnQuota is > 0, at least one is kept.
* lonerMode defines whether loners are discarded, kept, or treated as clusters, meaning they are kept if returnQuota > 0.
* takOverMode defines whether, of a cluster with size > 1, which n individuals are kept. Either the n best only,
* or the single best and random n-1, or all n random.
*
* @param pop
* @param clustering
* @param returnQuota
* @param lonerMode
* @param takeOverMode
* @return for every cluster a population of representatives which are the best individuals or a random subset of the cluster
*/
public static Population clusterBest(Population pop, InterfaceClustering clustering, double returnQuota, int lonerMode, int takeOverMode) {
//cluster the undifferentiated population
Population result = new Population(10);
result.setSameParams(pop);
Population[] clusters = clustering.cluster(pop);
if (TRACE) {
System.out.println("found " + clusters.length + " clusters!");
int sum=0;
for (int j=0; j<clusters.length; j++) {
sum += clusters[j].size();
if (TRACE) System.out.print(j + " w " + clusters[j].size() + ", ");
}
System.out.println("\nsum was "+sum);
}
for (int j = 0; j < clusters.length; j++) {
if (j==0) { // cluster 0 contains non-assigned individuals
if (lonerMode == DISCARD_LONERS) continue; // loners are discarded
else if (lonerMode == KEEP_LONERS) {
result.addAll(clusters[j]); // loners are all kept
continue;
} // default case: treat loners just as the rest
if (lonerMode != LONERS_AS_CLUSTERS) System.err.println("invalid loner mode in (), default is treating them like clusters");
}
if (returnQuota >= 1) result.addAll((Collection<AbstractEAIndividual>)clusters[j]); // easy case
else {
int n = Math.max(1, (int)(returnQuota*clusters[j].size())); // return at least one per cluster!
switch (takeOverMode) {
case BEST_ONLY: // another easy case
result.addAll((Collection<AbstractEAIndividual>)(clusters[j].getBestNIndividuals(n)));
break;
case BEST_RAND:
Population exclude = new Population();
exclude.add(clusters[j].getBestEAIndividual());
result.add(exclude.getEAIndividual(0));
result.addAll(clusters[j].getRandNIndividualsExcept(n-1, exclude));
break;
case RAND_ONLY:
result.addAll(clusters[j].getRandNIndividuals(n));
break;
default: System.err.println("Unknown mode in PostProcess:clusterBest!"); break;
}
}
}
result.setPopulationSize(result.size());
return result;
}
public static double[] populationMeasures(Population pop) {
double[] measures = pop.getPopulationMeasures();
return measures;
}
/**
* Filter the individuals of a population which have a fitness norm within given bounds.
* Returns shallow copies!
*
* @param pop
* @param lower
* @param upper
* @return
*/
public static Population filterFitnessIn(Population pop, double lower, double upper) {
Population result = filterFitness(pop, upper, true);
return filterFitness(result, lower, false);
}
/**
* Filter the individuals of a population which have a fitness norm below a given value.
* Returns shallow copies!
*
* @param pop
* @param fitNorm
* @param bSmaller if true, return individuals with lower or equal, else with higher fitness norm only
* @return
*/
public static Population filterFitness(Population pop, double fitNorm, boolean bSmallerEq) {
AbstractEAIndividual indy;
Population result = new Population();
for (int i=0; i<pop.size(); i++) {
indy = pop.getEAIndividual(i);
if (bSmallerEq && (PhenotypeMetric.norm(indy.getFitness())<=fitNorm)) result.add(indy);
else if (!bSmallerEq && (PhenotypeMetric.norm(indy.getFitness())>fitNorm)) result.add(indy);
}
return result;
}
/**
* Create a fitness histogram of an evaluated population within the given interval and nBins number of bins.
* Therefore a bin is of size (upperBound-lowerBound)/nBins, and bin 0 starts at lowerBound.
* Returns an integer array with the number of individuals in each bin.
*
* @param pop the population to scan.
* @param lowerBound lower bound of the fitness interval
* @param upperBound upper bound of the fitness interval
* @param nBins number of bins
* @return an integer array with the number of individuals in each bin
* @see filterFitnessIn()
*/
public static int[] createFitNormHistogram(Population pop, double lowerBound, double upperBound, int nBins) {
int[] res = new int[nBins];
double lower = lowerBound;
double step = (upperBound - lowerBound) / nBins;
for (int i=0; i<nBins; i++) {
if (TRACE) System.out.println("checking between " + lower + " and " + (lower+step));
res[i] = filterFitnessIn(pop, lower, lower+step).size();
if (TRACE) System.out.println("found " + res[i]);
lower += step;
}
return res;
}
// /**
// * This method returns a set of individuals corresponding to an optimum in a given list.
// * The individuals returned are to be nearer than epsilon to a given optimum. It is not
// * guaranteed, however, that the best individuals are returned. For each optimum, there is
// * returned zero or one individual at max.
// * If the optima are close together (e.g. closer than epsilon), or there is no threshold given,
// * it may happen that an individual is not returned if it is second closest to one optimum
// * and closest to another one.
// *
// * @param pop A population of possible solutions.
// * @param optima a set of predefined optima
// * @param epsilon the threshold up to which an optimum is considered found.
// * @return a list of individuals corresponding to an optimum from a list.
// */
// public static List<AbstractEAIndividual> getClosestFoundOptima(Population pop, Population optima, double epsilon) {
// AbstractEAIndividual indy;
// ArrayList<AbstractEAIndividual> result = new ArrayList<AbstractEAIndividual>(5);
// AbstractEAIndividual[] foundIndy = new AbstractEAIndividual[optima.size()];
//
// for (int i = 0; i < pop.size(); i++) {
// indy = (AbstractEAIndividual) pop.get(i);
// IndexFitnessPair bestHit = getClosestIndy(indy, optima);
// if (foundIndy[bestHit.index] == null) { // there has no indy been assigned yet
// // assign the current indy if no epsilon is required, or epsilon-threshold is fulfilled
// if (epsilon < 0 || (bestHit.dist < epsilon)) foundIndy[bestHit.index] = indy;
// } else {
// // assign current indy only if it is better than the earlier assigned one
// // in that case, epsilon is fulfilled automatically
// double oldDist = metric.distance(foundIndy[bestHit.index], (AbstractEAIndividual)optima.get(bestHit.index));
// if (bestHit.dist < oldDist) foundIndy[bestHit.index] = indy;
// }
// }
// return result;
// }
/**
* Search a population and find the closest individual to a given individual. Return the
* best distance and corresponding index in a pair structure.
*
* @param indy
* @param pop
* @return index and distance to the closest individual in the population
*/
public static Pair<Double, Integer> getClosestIndy(AbstractEAIndividual indy, Population pop) {
double bestDist = -1, tmpDist = -1;
int bestIndex = -1;
AbstractEAIndividual opt;
for (int j = 0; j < pop.size(); j++) {
opt = (AbstractEAIndividual) pop.get(j);
tmpDist = metric.distance(indy, opt); // distance current indy to current optimum
if (bestDist < 0 || (tmpDist < bestDist)) { // we have a better hit
bestIndex = j;
bestDist = tmpDist;
}
}
return new Pair<Double, Integer>(bestDist, bestIndex);
}
/**
* Optimize a population with a default hill-climbing heuristic for a number of fitness evaluations.
* As mutation operator, a fixed step size ES mutation is used.
*
* @param pop
* @param problem
* @param maxSteps
* @param fixedStepSize
*/
public static void processWithHC(Population pop, AbstractOptimizationProblem problem, int maxSteps, double fixedStepSize) {
// pop.SetFunctionCalls(0); // or else optimization wont restart on an "old" population
// pop.setGenerationTo(0);
processWithHC(pop, problem, new EvaluationTerminator(pop.getFunctionCalls()+maxSteps), new MutateESFixedStepSize(fixedStepSize));
}
public static void processWithHC(Population pop, AbstractOptimizationProblem problem, int maxSteps) {
processWithHC(pop, problem, maxSteps, defaultMutationStepSize);
}
/**
* Optimize a population with a default hill-climbing heuristic with a given termination criterion and mutation operator.
*
* @param pop
* @param problem
* @param term
* @param mute
*/
public static void processWithHC(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term, InterfaceMutation mute) {
HillClimbing hc = new HillClimbing();
// HC depends heavily on the selected mutation operator!
hc.SetProblem(problem);
hc.SetMutationOperator(mute);
if (pop.size() != pop.getPopulationSize()) {
System.err.println(pop.size() + " vs. "+ pop.getPopulationSize());
System.err.println("warning: population size and vector size dont match! (PostProcess::processWithHC)");
}
hc.setPopulation(pop);
// hc.initByPopulation(pop, false);
OptimizerRunnable runnable = new OptimizerRunnable(OptimizerFactory.makeParams(hc, pop, problem, 0, term), null, true);
runnable.getGOParams().setDoPostProcessing(false);
runnable.run();
}
public static void main(String[] args) {
AbstractOptimizationProblem problem = new FM0Problem();
InterfaceMultimodalProblemKnown mmp = (InterfaceMultimodalProblemKnown)problem;
OptimizerRunnable runnable = OptimizerFactory.getOptRunnable(OptimizerFactory.STD_GA, problem, 500, null);
runnable.run();
Population pop = runnable.getGOParams().getOptimizer().getPopulation();
// System.out.println("no optima found: " + mmp.getNumberOfFoundOptima(pop));
Population found = getFoundOptima(pop, mmp.getRealOptima(), 0.05, true);
System.out.println("all found (" + found.size() + "): " + BeanInspector.toString(found));
Pair<Population, Double> popD = new Pair<Population, Double>(pop, 1.);
int i=0;
int evalCnt = 0;
while (popD.tail() > 0.01) {
i++;
// public static PopDoublePair clusterHC(pop, problem, sigmaCluster, funCalls, keepClusterRatio, mute) {
popD = clusterHC(popD.head(), problem, 0.01, 1000 - (evalCnt % 1000), 0.5, new MutateESFixedStepSize(0.02));
evalCnt += popD.head().getFunctionCalls();
}
found = getFoundOptima(popD.head(), mmp.getRealOptima(), 0.05, true);
System.out.println("found at " + i + " (" + found.size() + "): " + BeanInspector.toString(found));
System.out.println("funcalls: " + evalCnt);
// System.out.println(BeanInspector.toString(pop.getMeanFitness()));
// System.out.println("no optima found: " + mmp.getNumberOfFoundOptima(pop));
// System.out.println("best after: " + AbstractEAIndividual.getDefaultStringRepresentation(pop.getBestEAIndividual()));
}
/**
* Cluster a population and reduce it by a certain ratio, then optimize the remaining individuals for a given number of function calls with a HC.
* Return a pair of the optimized population and the improvement in the mean fitness (not normed) that was achieved by the HC run. The returned
* individuals are deep clones, so the given population is not altered. Of a cluster of individuals, the given
* ratio of individuals is kept, more precisely, the best one is kept while the remaining are selected randomly. All loners are kept.
*
* @param pop the population to work on
* @param problem the target problem instance
* @param sigmaCluster minimum clustering distance
* @param funCalls number of function calls for the optimization step
* @param keepClusterRatio of a cluster of individuals, this ratio of individuals is kept for optimization
* @param mute the mutation operator to be used by the hill climber
* @return a pair of the optimized population and the improvement in the mean fitness (not normed) achieved by the HC run
*/
public static Pair<Population, Double> clusterHC(Population pop, AbstractOptimizationProblem problem, double sigmaCluster, int funCalls, double keepClusterRatio, InterfaceMutation mute) {
Population clust = (Population)clusterBest(pop, new ClusteringDensityBased(sigmaCluster), keepClusterRatio, KEEP_LONERS, BEST_RAND).clone();
// System.out.println("keeping " + clust.size() + " for hc....");
double[] meanFit = clust.getMeanFitness();
processWithHC(clust, problem, new EvaluationTerminator(pop.getFunctionCalls()+funCalls), mute);
double improvement = PhenotypeMetric.euclidianDistance(meanFit, clust.getMeanFitness());
System.out.println("improvement by " + improvement);
return new Pair<Population, Double>(clust, improvement);
}
// /**
// * Do some post processing on a multimodal problem. If the real optima are known, only the number of
// * found optima is printed. Otherwise, the population is clustered and for the population of cluster-representatives,
// * some diversity measures and a fitness histogram are printed.
// *
// * @see Population.getPopulationMeasures()
// * @see createFitNormHistogram
// * @param mmProb the target problem
// * @param pop the solution set
// * @param sigmaCluster the min clustering distance
// * @param out a PrintStream for data output
// * @param minBnd lower fitness bound
// * @param maxBnd upper fitness bound
// * @param bins number of bins for the fitness histogram
// * @return
// */
// public static Population outputResult(AbstractOptimizationProblem mmProb, Population pop, double sigmaCluster, PrintStream out, double minBnd, double maxBnd, int bins, int hcSteps) {
// ClusteringDensityBased clust = new ClusteringDensityBased(sigmaCluster);
// clust.setMinimumGroupSize(2);
// Population clusteredBest = clusterBest(pop, clust, 0, KEEP_LONERS, BEST_ONLY);
// if (hcSteps > 0) { // HC post process
// Population tmpPop = (Population)clusteredBest.clone();
// processWithHC(tmpPop, (AbstractOptimizationProblem)mmProb, hcSteps, 0.001);
// clusteredBest = clusterBest(tmpPop, clust, 0, KEEP_LONERS, BEST_ONLY);
// }
// double[] meas = clusteredBest.getPopulationMeasures();
// int[] sols = createFitNormHistogram(clusteredBest, minBnd, maxBnd, bins);
// out.println("measures: " + BeanInspector.toString(meas));
// out.println("solution hist.: " + BeanInspector.toString(sols));
//
// Object[] bestArr = clusteredBest.toArray();
// for (Object locOpt : bestArr) {
//// out.print((AbstractEAIndividual.getDefaultDataString((IndividualInterface)locOpt)));
// out.println(AbstractEAIndividual.getDefaultStringRepresentation(((AbstractEAIndividual)locOpt)));
// }
// return clusteredBest;
// }
// public static Population outputResultKnown(InterfaceMultimodalProblemKnown mmProb, Population pop, double sigmaCluster, PrintStream out, double minBnd, double maxBnd, int bins) {
// Population found = getFoundOptima(pop, ((InterfaceMultimodalProblemKnown)mmProb).getRealOptima(), ((InterfaceMultimodalProblemKnown)mmProb).getEpsilon(), true);
// for (double epsilon=0.1; epsilon > 0.00000001; epsilon/=10.) {
// //out.println("no optima found: " + ((InterfaceMultimodalProblemKnown)mmProb).getNumberOfFoundOptima(pop));
// out.println("found " + getFoundOptima(pop, ((InterfaceMultimodalProblemKnown)mmProb).getRealOptima(), epsilon, true).size() + " for epsilon = " + epsilon);
// }
// out.println("max peak ratio is " + mmProb.getMaximumPeakRatio(found));
// return found;
// }
/**
* Do some data output for multimodal problems with known optima.
*/
public static void procMultiModalKnown(Population solutions, InterfaceMultimodalProblemKnown mmkProb, InterfaceTextListener listener) {
// Population found = getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getEpsilon(), true);
listener.println("default epsilon is " + mmkProb.getEpsilon());
listener.println("max peak ratio is " + mmkProb.getMaximumPeakRatio(getFoundOptima(solutions, mmkProb.getRealOptima(), mmkProb.getEpsilon(), true)));
for (double epsilon=0.1; epsilon > 0.00000001; epsilon/=10.) {
// out.println("no optima found: " + ((InterfaceMultimodalProblemKnown)mmProb).getNumberOfFoundOptima(pop));
listener.println("found " + getFoundOptima(solutions, mmkProb.getRealOptima(), epsilon, true).size() + " for epsilon = " + epsilon);
}
}
/**
* Universal post processing method, receiving parameter instance for specification.
* Optional clustering and HC step, output contains population measures, fitness histogram and
* a list of solutions after post processing.
*
* @param params
* @param solutions
* @param problem
* @param listener
*/
public static void postProcess(InterfacePostProcessParams params, Population solutions, AbstractOptimizationProblem problem, InterfaceTextListener listener) {
if (params.isDoPostProcessing()) {
Population outputPop;
if (params.getPostProcessClusterSigma() > 0) {
outputPop = (Population)PostProcess.clusterBest(solutions, params.getPostProcessClusterSigma(), 0, PostProcess.KEEP_LONERS, PostProcess.BEST_ONLY).clone();
if (outputPop.size() < solutions.size()) {
listener.println("Clustering reduced population size from " + solutions.size() + " to " + outputPop.size());
} else listener.println("Clustering yielded no size reduction.");
} else outputPop = solutions;
if (params.getPostProcessSteps() > 0) {
processWithHC(outputPop, problem, params.getPostProcessSteps());
listener.println("HC post processing done.");
}
double upBnd = PhenotypeMetric.norm(outputPop.getWorstEAIndividual().getFitness())*1.1;
upBnd = Math.pow(10,Math.floor(Math.log10(upBnd)+1));
double lowBnd = 0;
int[] sols = PostProcess.createFitNormHistogram(outputPop, lowBnd, upBnd, 20);
// PostProcessInterim.outputResult((AbstractOptimizationProblem)goParams.getProblem(), outputPop, 0.01, System.out, 0, 2000, 20, goParams.getPostProcessSteps());
listener.println("measures: " + BeanInspector.toString(outputPop.getPopulationMeasures()));
listener.println("solution histogram in [" + lowBnd + "," + upBnd + "]: " + BeanInspector.toString(sols));
//////////// multimodal data output?
if (problem instanceof InterfaceMultimodalProblemKnown) procMultiModalKnown(outputPop, (InterfaceMultimodalProblemKnown)problem, listener);
//////////// output some individual data
List<AbstractEAIndividual> bestList = outputPop.getBestNIndividuals(params.getPrintNBest()); // n individuals are returned and sorted, all of them if n<=0
listener.println("Best after post process:" + ((outputPop.size()>bestList.size()) ? ( "(first " + bestList.size() + " of " + outputPop.size() + ")") : ""));
for (AbstractEAIndividual indy : bestList) {
listener.println(AbstractEAIndividual.getDefaultStringRepresentation(indy));
}
}
}
}

96
src/javaeva/server/go/operators/postprocess/PostProcessParams.java Normal file
View File

@ -0,0 +1,96 @@
package javaeva.server.go.operators.postprocess;
import java.io.Serializable;
import javaeva.gui.GenericObjectEditor;
public class PostProcessParams implements InterfacePostProcessParams, Serializable {
protected int postProcessSteps = 5000;
private boolean postProcess = false;
protected double postProcessClusterSigma = 0.05;
protected int printNBest = 10;
public PostProcessParams() {
postProcessSteps = 5000;
postProcess = false;
postProcessClusterSigma = 0.05;
printNBest = 10;
}
public PostProcessParams(boolean doPP) {
postProcessSteps = 5000;
postProcess = doPP;
postProcessClusterSigma = 0.05;
}
public PostProcessParams(int steps, double clusterSigma) {
postProcessSteps = steps;
postProcess = true;
postProcessClusterSigma = clusterSigma;
}
public void hideHideable() {
setDoPostProcessing(isDoPostProcessing());
}
/**
* @return the postProcess
*/
public boolean isDoPostProcessing() {
return postProcess;
}
/**
* @param postProcess the postProcess to set
*/
public void setDoPostProcessing(boolean postProcess) {
this.postProcess = postProcess;
GenericObjectEditor.setShowProperty(this.getClass(), "postProcessSteps", postProcess);
GenericObjectEditor.setShowProperty(this.getClass(), "postProcessClusterSigma", postProcess);
GenericObjectEditor.setShowProperty(this.getClass(), "printNBest", postProcess);
}
public String doPostProcessingTipText() {
return "Toggle post processing of the solutions.";
}
/**
* @return the postProcessClusterSigma
*/
public double getPostProcessClusterSigma() {
return postProcessClusterSigma;
}
/**
* @param postProcessClusterSigma the postProcessClusterSigma to set
*/
public void setPostProcessClusterSigma(double postProcessClusterSigma) {
this.postProcessClusterSigma = postProcessClusterSigma;
}
public String postProcessClusterSigmaTipText() {
return "Set the sigma parameter for clustering during post processing. Set to zero for no clustering.";
}
public String postProcessStepsTipText() {
return "The number of HC post processing steps in fitness evaluations.";
}
public int getPostProcessSteps() {
return postProcessSteps;
}
public void setPostProcessSteps(int ppSteps) {
postProcessSteps = ppSteps;
}
public int getPrintNBest() {
return printNBest;
}
public void setPrintNBest(int nBest) {
printNBest = nBest;
}
public String printNBestTipText() {
return "Print as many solutions at max. Set to -1 to print all";
}
//////////////////////// GUI
public String getName() {
return "PostProcessing " + (postProcess ? (postProcessSteps + "/" + postProcessClusterSigma) : "off");
}
}

4
src/javaeva/server/go/operators/selection/probability/SelProbFitnessSharing.java
View File

@ -2,7 +2,7 @@ package javaeva.server.go.operators.selection.probability;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.operators.distancemetric.InterfaceDistanceMetric;
import javaeva.server.go.operators.distancemetric.PhenotypeMetricDoubleData;
import javaeva.server.go.operators.distancemetric.PhenotypeMetric;
import javaeva.server.go.populations.Population;
/** Here we have the infamous fitness sharing method.
@ -15,7 +15,7 @@ import javaeva.server.go.populations.Population;
public class SelProbFitnessSharing extends AbstractSelProb implements java.io.Serializable {
private InterfaceSelectionProbability m_BasicNormationMethod = new SelProbStandard();
private InterfaceDistanceMetric m_DistanceMetric = new PhenotypeMetricDoubleData();
private InterfaceDistanceMetric m_DistanceMetric = new PhenotypeMetric();
private double m_SharingDistance = 0.1;
public SelProbFitnessSharing() {

4
src/javaeva/server/go/operators/terminators/EvaluationTerminator.java
View File

@ -40,8 +40,10 @@ public class EvaluationTerminator implements InterfaceTerminator,
public String globalInfo() {
return "Terminates after the given number of fitness calls.";
}
/**
*
* Construct Terminator with a maximum number of fitness calls.
* @param maximum number of fitness calls
*/
public EvaluationTerminator(int x) {
m_FitnessCalls = x;

185
src/javaeva/server/go/populations/Population.java
View File

@ -1,17 +1,19 @@
package javaeva.server.go.populations;
import javaeva.server.go.IndividualInterface;
import javaeva.server.go.PopulationInterface;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.GAIndividualBinaryData;
import javaeva.server.go.operators.distancemetric.PhenotypeMetric;
import javaeva.server.go.tools.RandomNumberGenerator;
import java.util.ArrayList;
import java.util.LinkedList;
import java.util.List;
import java.util.PriorityQueue;
import javaeva.server.go.IndividualInterface;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.PopulationInterface;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.AbstractEAIndividualComparator;
import javaeva.server.go.individuals.GAIndividualBinaryData;
import javaeva.server.go.operators.distancemetric.PhenotypeMetric;
import javaeva.server.go.tools.RandomNumberGenerator;
/** This is a basic implementation for a EA Population.
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
@ -27,6 +29,10 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
protected int m_FunctionCalls = 0;
protected int m_Size = 50;
protected Population m_Archive = null;
transient protected InterfacePopulationChangedEventListener m_Listener = null;
protected int notifyEvalInterval = 0;
public static String funCallIntervalReached = "FunCallIntervalReached";
boolean useHistory = false;
public ArrayList<AbstractEAIndividual> m_History = new ArrayList<AbstractEAIndividual>();
@ -39,9 +45,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
}
public Population(Population population) {
this.m_Generation = population.m_Generation;
this.m_FunctionCalls = population.m_FunctionCalls;
this.m_Size = population.m_Size;
setSameParams(population);
for (int i = 0; i < population.size(); i++) {
if (population.get(i) != null)
this.add((((AbstractEAIndividual)population.get(i))).clone());
@ -50,6 +54,19 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
if (population.m_History != null) this.m_History = (ArrayList)population.m_History.clone();
}
/**
* Takes over all scalar parameters of the given population.
* @param population
*/
public void setSameParams(Population population) {
this.m_Generation = population.m_Generation;
this.m_FunctionCalls = population.m_FunctionCalls;
this.m_Size = population.m_Size;
this.useHistory = population.useHistory;
this.notifyEvalInterval = population.notifyEvalInterval;
this.m_Listener = population.m_Listener;
}
public Object clone() {
return (Object) new Population(this);
}
@ -99,12 +116,39 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
*/
public void incrFunctionCalls() {
this.m_FunctionCalls++;
if (doEvalNotify()) {
// System.out.println("checking funcall event...");
if ((m_FunctionCalls % notifyEvalInterval) == 0) firePropertyChangedEvent(funCallIntervalReached);
}
}
/** This method will allow cou to increment the current number of function calls.
/**
* This method will allow you to increment the current number of function calls by a number > 1.
* Notice that it might slightly disturb notification if a notifyEvalInterval is set.
*
* @param d The number of function calls to increment.
*/
public void incrFunctionCallsby(int d) {
this.m_FunctionCalls += d;
if (doEvalNotify()) {
System.out.println("checking funcall event...");
int nextStep = ((m_FunctionCalls/notifyEvalInterval)+1) * notifyEvalInterval; // next interval boundary
if (nextStep <= (m_FunctionCalls+d)) {
// the notify interval will be stepped over or hit
int toHit = (nextStep - m_FunctionCalls);
this.m_FunctionCalls += toHit; // little cheat, notify may be after some more evals
firePropertyChangedEvent(funCallIntervalReached);
this.m_FunctionCalls += (d-toHit);
}
} else this.m_FunctionCalls += d;
}
/** Something has changed
*/
protected void firePropertyChangedEvent(String name) {
if (this.m_Listener != null) this.m_Listener.registerPopulationStateChanged(this, name);
}
private boolean doEvalNotify() {
return ((this.m_Listener != null) && (notifyEvalInterval > 0));
}
/** This method return the current number of function calls performed.
@ -131,6 +175,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
if (useHistory && (this.size() >= 1)) this.m_History.add(this.getBestEAIndividual());
for (int i=0; i<size(); i++) ((AbstractEAIndividual)get(i)).incrAge();
this.m_Generation++;
firePropertyChangedEvent("NextGenerationPerformed");
}
/** This method returns the current generation.
@ -147,6 +192,13 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
this.m_Generation = gen;
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
/** This method allows you to add a complete population to the current population.
* Note: After this operation the target population size may be exceeded.
* @param pop The population that is to be added.
@ -245,34 +297,92 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
return result;
}
/**
* This method returns the n current best individuals from the population, where
* the sorting criterion is delivered by an AbstractEAIndividualComparator.
* There are less than n individuals returned if the population is smaller than n.
* If n is <= 0, then all individuals are returned and effectively just sorted
* by fitness.
*
* @param n number of individuals to look out for
* @return The m best individuals, where m <= n
*
*/
public List<AbstractEAIndividual> getBestNIndividuals(int n) {
LinkedList<AbstractEAIndividual> indList = new LinkedList<AbstractEAIndividual>();
PriorityQueue<AbstractEAIndividual> queue = new PriorityQueue<AbstractEAIndividual>(super.size(), new AbstractEAIndividualComparator());
for (int i = 0; i < super.size(); i++) {
queue.add(getEAIndividual(i));
}
if (n <= 0) n = queue.size();
for (int i = 0; i<n ; i++) {
if (queue.size() == 0) break;
indList.add(queue.poll());
}
return indList;
}
/** This method returns n random best individuals from the population.
*
* @param n number of individuals to look out for
* @return The n best individuals
*
*/
public List<AbstractEAIndividual> getRandNIndividuals(int n) {
return getRandNIndividualsExcept(n, new Population());
}
/** This method returns the n current best individuals from the population in an object array.
*
* @param n number of individuals to look out for
* @return The n best individuals
*
*/
public Object[] getBestNIndividuals(int n) {
LinkedList<AbstractEAIndividual> indList = new LinkedList<AbstractEAIndividual>();
PriorityQueue<Double> queue = new PriorityQueue<Double>(n);
AbstractEAIndividual indy;
double curNBestFitness = Double.POSITIVE_INFINITY;
for (int i = 0; i < super.size(); i++) {
indy = (AbstractEAIndividual)super.get(i);
if ((!indy.violatesConstraint()) && (indy.getFitness(0) < curNBestFitness)) {
if (indList.size() >= n) {
indList.removeLast();
queue.remove();
}
indList.addFirst((AbstractEAIndividual)super.get(i));
// use negative fitness, because queue orders the smallest to top.
queue.add(new Double(- indy.getFitness(0)));
if (indList.size() == n) curNBestFitness = - ((Double)queue.peek()).doubleValue();
}
}
return indList.toArray();
public Population getRandNIndividualsExcept(int n, Population exclude) {
return moveNInds(n, filter(exclude), new Population());
}
/**
* Moves n random individuals from src Population to dst Population and returns dst Population.
*
* @param n
* @param from
* @param to
* @return
*/
public static Population moveNInds(int n, Population src, Population dst) {
if ((n == 0) || (src.size() == 0)) return dst;
else { // Ingenious superior Scheme tail recursive style!
moveRandIndFromTo(src, dst);
return moveNInds(n-1, src, dst);
}
}
/**
* Move one random individual from src to dst population.
* @param from
* @param to
*/
public static void moveRandIndFromTo(Population src, Population dst) {
int k = RandomNumberGenerator.randomInt(src.size());
dst.add(src.remove(k));
}
/**
* Returns a subset of this population which does not contain the individuals
* in the given exclude list as shallow copies.
* @param exclude
* @return
*/
public Population filter(Population exclude) {
if (exclude.size() == 0) return this;
Population pop = new Population();
for (Object o : this) {
if (!exclude.contains(o)) pop.add(o);
}
return pop;
}
/** This method returns the currently worst individual from the population
* @return The best individual
@ -560,4 +670,13 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
public int getGenerations() {
return this.m_Generation;
}
/**
* Fire an event every n function calls, the event sends the public String funCallIntervalReached.
*
* @param notifyEvalInterval the notifyEvalInterval to set
*/
public void setNotifyEvalInterval(int notifyEvalInterval) {
this.notifyEvalInterval = notifyEvalInterval;
}
}

73
src/javaeva/server/go/problems/AbstractOptimizationProblem.java
View File

@ -83,78 +83,10 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
}
/** This method evaluate a single individual and sets the fitness values
* @param individual The individual that is to be evalutated
* @param individual The individual that is to be evaluated
*/
public abstract void evaluate(AbstractEAIndividual individual);
/** This method should be used to calculate the distance between two
* individuals. Per default i implemented a phenotypic distance metric.
* @param indy1 The first individual.
* @param indy2 The second individual.
* @return The distance.
*/
public double distanceBetween(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
double result = 1;
if ((indy1 instanceof InterfaceDataTypeBinary) && (indy2 instanceof InterfaceDataTypeBinary)) {
BitSet b1, b2;
b1 = ((InterfaceDataTypeBinary)indy1).getBinaryData();
b2 = ((InterfaceDataTypeBinary)indy2).getBinaryData();
if (b1.length() != b2.length()) return Math.max(b1.length(), b2.length());
result = b1.length();
for (int i = 0; i < b1.length(); i++) {
if (b1.get(i) == b2.get(i)) result--;
}
result = result/((double)b1.length());
}
if ((indy1 instanceof InterfaceDataTypeInteger) && (indy2 instanceof InterfaceDataTypeInteger)) {
int[] b1, b2;
int[][] range;
int max = 0;
b1 = ((InterfaceDataTypeInteger)indy1).getIntegerData();
b2 = ((InterfaceDataTypeInteger)indy2).getIntegerData();
if (b1.length != b2.length) return Math.max(b1.length, b2.length);
range = ((InterfaceDataTypeInteger)indy2).getIntRange();
result = 0;
for (int i = 0; i < b1.length; i++) {
result += Math.abs(b1[i]-b2[i]);
max += range[i][1]-range[i][0];
}
result = result/((double)max);
}
if ((indy1 instanceof InterfaceDataTypeDouble) && (indy2 instanceof InterfaceDataTypeDouble)) {
double[] b1, b2;
double[][] range;
double max = 0;
b1 = ((InterfaceDataTypeDouble)indy1).getDoubleData();
b2 = ((InterfaceDataTypeDouble)indy2).getDoubleData();
if (b1.length != b2.length) return Math.max(b1.length, b2.length);
result = 0;
range = ((InterfaceDataTypeDouble)indy1).getDoubleRange();
for (int i = 0; i < (Math.min(b1.length, b2.length)); i++) {
result += Math.abs(b1[i]-b2[i]);
max += range[i][1]-range[i][0];
}
result = result/max;
}
if ((indy1 instanceof InterfaceDataTypePermutation) && (indy2 instanceof InterfaceDataTypePermutation)) {
int[] b1, b2;
b1 = ((InterfaceDataTypePermutation)indy1).getPermutationData()[0];
b2 = ((InterfaceDataTypePermutation)indy2).getPermutationData()[0];
if (b1.length != b2.length) return Math.max(b1.length, b2.length);
result = b1.length;
int tmp;
for (int i = 0; i < b1.length; i++) {
tmp = b1.length;
for (int j = 0; j < b1.length; j++) {
if (b1[j] != b2[(j+i)%b1.length]) tmp++;
}
result = Math.min(result, tmp);
}
result = result/((double)b1.length);
}
return result;
}
/******************** Some output methods *******************************************/
/** This method allows you to output a string that describes a found solution
@ -238,7 +170,8 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati
}
/**
* TODO
* For some evaluation cases it may be necessary to inform the problem class about the optimizer in use.
*
* @param opt
*/
public void informAboutOptimizer(InterfaceOptimizer opt) {

15
src/javaeva/server/go/problems/F10Problem.java
View File

@ -1,6 +1,5 @@
package javaeva.server.go.problems;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.ESIndividualDoubleData;
/**
@ -10,7 +9,7 @@ import javaeva.server.go.individuals.ESIndividualDoubleData;
* Time: 14:33:07
* To change this template use File | Settings | File Templates.
*/
public class F10Problem extends F1Problem implements java.io.Serializable {
public class F10Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
private double m_D = 1.5;
private double m_b = 2.3;
@ -20,17 +19,7 @@ public class F10Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F10Problem(F10Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
this.m_D = b.m_D;
this.m_b = b.m_b;
this.m_Iterations = b.m_Iterations;

14
src/javaeva/server/go/problems/F11Problem.java
View File

@ -12,7 +12,7 @@ import javaeva.server.go.populations.Population;
* Time: 14:59:23
* To change this template use File | Settings | File Templates.
*/
public class F11Problem extends F1Problem implements java.io.Serializable {
public class F11Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
private double m_D = 4000;
@ -23,17 +23,7 @@ public class F11Problem extends F1Problem implements java.io.Serializable {
}
public F11Problem(F11Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
this.m_D = b.m_D;
}

14
src/javaeva/server/go/problems/F12Problem.java
View File

@ -18,17 +18,7 @@ public class F12Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F12Problem(F12Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
}
/** This method returns a deep clone of the problem.
@ -44,7 +34,7 @@ public class F12Problem extends F1Problem implements java.io.Serializable {
public void initPopulation(Population population) {
AbstractEAIndividual tmpIndy;
this.m_OverallBest = null;
// this.m_OverallBest = null;
population.clear();

14
src/javaeva/server/go/problems/F13Problem.java
View File

@ -9,23 +9,13 @@ import javaeva.server.go.populations.Population;
* Schwefels sine root function (1981) with a minimum at 420.9687^n of value 0.
* Function f(x) = (418.9829 * n) - sum_n(x_i * sin(sqrt(abs(x_i)))) + (418.9829 * n);
*/
public class F13Problem extends F1Problem {
public class F13Problem extends F1Problem implements InterfaceMultimodalProblem {
public F13Problem() {
this.m_Template = new ESIndividualDoubleData();
}
public F13Problem(F13Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
}
/** This method returns a deep clone of the problem.

14
src/javaeva/server/go/problems/F14Problem.java
View File

@ -1,6 +1,5 @@
package javaeva.server.go.problems;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.ESIndividualDoubleData;
/**
@ -19,17 +18,8 @@ public class F14Problem extends F1Problem implements java.io.Serializable {
}
public F14Problem(F14Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
rotation = b.rotation;
}
/** This method returns a deep clone of the problem.

14
src/javaeva/server/go/problems/F1Problem.java
View File

@ -18,7 +18,7 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
*
*/
private static final long serialVersionUID = 4870484001737601464L;
protected AbstractEAIndividual m_OverallBest = null;
// protected AbstractEAIndividual m_OverallBest = null;
protected int m_ProblemDimension = 10;
protected double m_XOffSet = 0.0;
protected double m_YOffSet = 0.0;
@ -33,8 +33,8 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
// if (b.m_OverallBest != null)
// this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_DefaultRange = b.m_DefaultRange;
@ -53,7 +53,7 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
/** This method inits the Problem to log multiruns
*/
public void initProblem() {
this.m_OverallBest = null;
// this.m_OverallBest = null;
}
/** This method inits a given population
@ -76,9 +76,9 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde
if (this.m_UseTestConstraint) {
if (x[0] < 1) individual.addConstraintViolation(1-x[0]);
}
if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) {
this.m_OverallBest = (AbstractEAIndividual)individual.clone();
}
// if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) {
// this.m_OverallBest = (AbstractEAIndividual)individual.clone();
// }
}
/** Ths method allows you to evaluate a simple bit string to determine the fitness

14
src/javaeva/server/go/problems/F2Problem.java
View File

@ -11,23 +11,13 @@ import javaeva.server.go.strategies.InterfaceOptimizer;
* Time: 19:03:09
* To change this template use File | Settings | File Templates.
*/
public class F2Problem extends F1Problem implements java.io.Serializable {
public class F2Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
public F2Problem() {
this.m_Template = new ESIndividualDoubleData();
}
public F2Problem(F2Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
}
/** This method returns a deep clone of the problem.

12
src/javaeva/server/go/problems/F3Problem.java
View File

@ -16,17 +16,7 @@ public class F3Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F3Problem(F3Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
}
/** This method returns a deep clone of the problem.

14
src/javaeva/server/go/problems/F4Problem.java
View File

@ -19,17 +19,7 @@ public class F4Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F4Problem(F4Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
}
/** This method inits a given population
@ -38,7 +28,7 @@ public class F4Problem extends F1Problem implements java.io.Serializable {
public void initPopulation(Population population) {
AbstractEAIndividual tmpIndy;
this.m_OverallBest = null;
// this.m_OverallBest = null;
population.clear();

14
src/javaeva/server/go/problems/F5Problem.java
View File

@ -19,17 +19,7 @@ public class F5Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F5Problem(F5Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
}
/** This method inits a given population
@ -38,7 +28,7 @@ public class F5Problem extends F1Problem implements java.io.Serializable {
public void initPopulation(Population population) {
AbstractEAIndividual tmpIndy;
this.m_OverallBest = null;
// this.m_OverallBest = null;
population.clear();

16
src/javaeva/server/go/problems/F6Problem.java
View File

@ -1,6 +1,5 @@
package javaeva.server.go.problems;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.ESIndividualDoubleData;
import javaeva.server.go.strategies.ParticleSwarmOptimization;
import wsi.ra.math.Jama.Matrix;
@ -12,7 +11,7 @@ import wsi.ra.math.Jama.Matrix;
* Time: 13:09:36
* To change this template use File | Settings | File Templates.
*/
public class F6Problem extends F1Problem implements java.io.Serializable {
public class F6Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
private boolean doRotation = false;
private double m_A = 10;
@ -23,17 +22,8 @@ public class F6Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F6Problem(F6Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
doRotation = b.doRotation;
this.m_A = b.m_A;
this.m_Omega = b.m_Omega;
}

23
src/javaeva/server/go/problems/F7Problem.java
View File

@ -30,19 +30,10 @@ public class F7Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F7Problem(F7Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
this.m_Change = b.m_Change;
this.m_t = b.m_t;
this.m_TimeIntervalType = (SelectedTag)b.m_TimeIntervalType;
this.m_TimeIntervalType = (SelectedTag)b.m_TimeIntervalType.clone();
}
/** This method returns a deep clone of the problem.
@ -59,6 +50,7 @@ public class F7Problem extends F1Problem implements java.io.Serializable {
public void evaluate(Population population) {
AbstractEAIndividual tmpIndy;
evaluatePopulationStart(population);
for (int i = 0; i < population.size(); i++) {
tmpIndy = (AbstractEAIndividual) population.get(i);
tmpIndy.resetConstraintViolation();
@ -70,6 +62,7 @@ public class F7Problem extends F1Problem implements java.io.Serializable {
this.evaluate(tmpIndy);
population.incrFunctionCalls();
}
evaluatePopulationEnd(population);
}
/** This method evaluate a single individual and sets the fitness values
@ -94,9 +87,9 @@ public class F7Problem extends F1Problem implements java.io.Serializable {
if (this.m_UseTestConstraint) {
if (x[0] < 1) individual.addConstraintViolation(1-x[0]);
}
if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) {
this.m_OverallBest = (AbstractEAIndividual)individual.clone();
}
// if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) {
// this.m_OverallBest = (AbstractEAIndividual)individual.clone();
// }
}
/** Ths method allows you to evaluate a double[] to determine the fitness

20
src/javaeva/server/go/problems/F8Problem.java
View File

@ -4,7 +4,6 @@ import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.ESIndividualDoubleData;
import javaeva.server.go.individuals.InterfaceDataTypeDouble;
import javaeva.server.go.populations.Population;
import javaeva.server.go.tools.RandomNumberGenerator;
/**
* Created by IntelliJ IDEA.
@ -13,7 +12,7 @@ import javaeva.server.go.tools.RandomNumberGenerator;
* Time: 19:40:28
* To change this template use File | Settings | File Templates.
*/
public class F8Problem extends F1Problem implements java.io.Serializable {
public class F8Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable {
private double a = 20;
private double b = 0.2;
@ -23,17 +22,10 @@ public class F8Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F8Problem(F8Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
this.a = b.a;
this.b = b.b;
this.c = b.c;
}
/** This method returns a deep clone of the problem.
@ -49,7 +41,7 @@ public class F8Problem extends F1Problem implements java.io.Serializable {
public void initPopulation(Population population) {
AbstractEAIndividual tmpIndy;
this.m_OverallBest = null;
// this.m_OverallBest = null;
population.clear();

12
src/javaeva/server/go/problems/F9Problem.java
View File

@ -16,17 +16,7 @@ public class F9Problem extends F1Problem implements java.io.Serializable {
this.m_Template = new ESIndividualDoubleData();
}
public F9Problem(F9Problem b) {
//AbstractOptimizationProblem
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
super(b);
}
/** This method returns a deep clone of the problem.

173
src/javaeva/server/go/problems/FM0Problem.java
View File

@ -1,13 +1,15 @@
package javaeva.server.go.problems;
import java.io.Serializable;
import java.util.List;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.ESIndividualDoubleData;
import javaeva.server.go.individuals.InterfaceDataTypeDouble;
import javaeva.server.go.operators.distancemetric.InterfaceDistanceMetric;
import javaeva.server.go.operators.distancemetric.PhenotypeMetricDoubleData;
import javaeva.server.go.operators.distancemetric.PhenotypeMetric;
import javaeva.server.go.operators.postprocess.PostProcess;
import javaeva.server.go.populations.Population;
import javaeva.server.go.strategies.InterfaceOptimizer;
import javaeva.server.go.tools.RandomNumberGenerator;
/**
* Created by IntelliJ IDEA.
@ -16,16 +18,17 @@ import javaeva.server.go.tools.RandomNumberGenerator;
* Time: 11:10:43
* To change this template use Options | File Templates.
*/
public class FM0Problem extends F1Problem implements Interface2DBorderProblem, InterfaceMultimodalProblem, java.io.Serializable {
public class FM0Problem extends AbstractProblemDouble implements Interface2DBorderProblem, InterfaceMultimodalProblemKnown, Serializable {
protected InterfaceDistanceMetric m_Metric = new PhenotypeMetricDoubleData();
protected static InterfaceDistanceMetric m_Metric = new PhenotypeMetric();
protected double m_GlobalOpt = 0;
protected Population m_Optima;
protected double m_Epsilon = 0.05;
protected boolean m_UseXCrit = true;
protected boolean m_UseYCrit = true;
// protected boolean m_UseXCrit = true;
// protected boolean m_UseYCrit = true;
protected double[][] m_Range;
protected double[] m_Extrema;
protected int m_ProblemDimension = 2;
public FM0Problem() {
this.m_Template = new ESIndividualDoubleData();
@ -54,21 +57,21 @@ public class FM0Problem extends F1Problem implements Interface2DBorderProblem, I
if (b.m_Template != null)
this.m_Template = (AbstractEAIndividual)((AbstractEAIndividual)b.m_Template).clone();
//F1Problem
if (b.m_OverallBest != null)
this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
// if (b.m_OverallBest != null)
// this.m_OverallBest = (AbstractEAIndividual)((AbstractEAIndividual)b.m_OverallBest).clone();
this.m_ProblemDimension = b.m_ProblemDimension;
this.m_Noise = b.m_Noise;
this.m_XOffSet = b.m_XOffSet;
this.m_YOffSet = b.m_YOffSet;
this.m_UseTestConstraint = b.m_UseTestConstraint;
// this.m_XOffSet = b.m_XOffSet;
// this.m_YOffSet = b.m_YOffSet;
// this.m_UseTestConstraint = b.m_UseTestConstraint;
//FM0Problem
this.m_GlobalOpt = b.m_GlobalOpt;
this.m_Epsilon = b.m_Epsilon;
this.m_UseXCrit = b.m_UseXCrit;
this.m_UseYCrit = b.m_UseYCrit;
this.m_UseXCrit = b.m_UseXCrit;
if (b.m_Metric != null)
this.m_Metric = (InterfaceDistanceMetric)((InterfaceDistanceMetric)b.m_Metric).clone();
// this.m_UseXCrit = b.m_UseXCrit;
// this.m_UseYCrit = b.m_UseYCrit;
// this.m_UseXCrit = b.m_UseXCrit;
// if (m_Metric != null)
// this.m_Metric = (InterfaceDistanceMetric)((InterfaceDistanceMetric)b.m_Metric).clone();
if (b.m_Optima != null)
this.m_Optima = (Population)((Population)b.m_Optima).clone();
if (b.m_Extrema != null) {
@ -100,7 +103,7 @@ public class FM0Problem extends F1Problem implements Interface2DBorderProblem, I
public void initPopulation(Population population) {
AbstractEAIndividual tmpIndy;
this.m_OverallBest = null;
// this.m_OverallBest = null;
population.clear();
@ -128,7 +131,7 @@ public class FM0Problem extends F1Problem implements Interface2DBorderProblem, I
return result;
}
/** This method returns the unnormalized function value for an maximisation problem
/** This method returns the unnormalized function value for an maximization problem
* @param x The n-dimensional input vector
* @return The m-dimensional output vector.
*/
@ -197,8 +200,19 @@ public class FM0Problem extends F1Problem implements Interface2DBorderProblem, I
InterfaceDataTypeDouble tmpIndy;
tmpIndy = (InterfaceDataTypeDouble)((AbstractEAIndividual)this.m_Template).clone();
tmpIndy.SetDoubleDataLamarkian(point);
((AbstractEAIndividual)tmpIndy).SetFitness(this.evalUnnormalized(point));
this.m_GlobalOpt = Math.max(this.m_GlobalOpt, ((AbstractEAIndividual)tmpIndy).getFitness(0));
((AbstractEAIndividual)tmpIndy).SetFitness(evalUnnormalized(point));
if (((AbstractEAIndividual)tmpIndy).getFitness(0)>=m_GlobalOpt) {
m_GlobalOpt = ((AbstractEAIndividual)tmpIndy).getFitness(0);
double tmp=m_GlobalOpt;
double dx = 1e-30;
while (tmp==m_GlobalOpt) {
// this increases the optimum until there is a real difference.
// tries to avoid zero y-values which break the logarithmic plot
tmp+=dx;
dx *= 10;
}
m_GlobalOpt = tmp;
}
this.m_Optima.add(tmpIndy);
}
@ -220,70 +234,68 @@ public class FM0Problem extends F1Problem implements Interface2DBorderProblem, I
/** This method returns a list of all optima as population
* @return population
*/
public Population getAllOptima() {
public Population getRealOptima() {
return this.m_Optima;
}
/** This method returns the overall number of optima
* @return int
*/
public int getNumberOfOptima() {
return this.m_Optima.size();
}
/** This method returns the Number of Identified optima
* @param pop A population of possible solutions.
* @return int
*/
public int getNumberOfFoundOptima(Population pop) {
int result = 0;
AbstractEAIndividual posOpt, opt;
boolean[] found = new boolean[this.m_Optima.size()];
for (int i = 0; i < found.length; i++) found[i] = false;
for (int i = 0; i < pop.size(); i++) {
posOpt = (AbstractEAIndividual) pop.get(i);
for (int j = 0; j < this.m_Optima.size(); j++) {
opt = (AbstractEAIndividual) this.m_Optima.get(j);
if (!found[j]) {
if (this.m_Metric.distance(posOpt, opt) < this.m_Epsilon) found[j] = true;
}
}
}
for (int i = 0; i < found.length; i++) if (found[i]) result++;
return result;
List<AbstractEAIndividual> sols = PostProcess.getFoundOptima(pop, m_Optima, m_Epsilon, true);
return sols.size();
}
/** This method returns the Maximum Peak Ratio.
/**
* This method returns the maximum peak ratio, which is the ratio of found fitness values corresponding to
* known optima with the internal epsilon criterion and the sum of all fitness values seen as maximization.
* Thus, if all optima are perfectly found, 1 is returned. If no optimum is found, zero is returned.
* A return value of 0.5 may mean, e.g., that half of n similar optima have been found perfectly, or that 1 major
* optimum of equal weight than all the others has been found perfectly, or that all optima have been found
* with about 50% accuracy, etc.
*
* @param pop A population of possible solutions.
* @return double
*/
public double getMaximumPeakRatio(Population pop) {
double result = 0, sum = 0;
AbstractEAIndividual posOpt, opt;
boolean[] found = new boolean[this.m_Optima.size()];
for (int i = 0; i < found.length; i++) {
found[i] = false;
sum += ((AbstractEAIndividual)this.m_Optima.get(i)).getFitness(0) ;
//System.out.println("Optimum " + i + ".: " + (((AbstractEAIndividual)this.m_Optima.get(i)).getFitness(0)));
}
double optimaInvertedSum = 0, foundInvertedSum = 0;
AbstractEAIndividual[] optsFound = PostProcess.getFoundOptimaArray(pop, m_Optima, m_Epsilon, true);
for (int i = 0; i < pop.size(); i++) {
posOpt = (AbstractEAIndividual) pop.get(i);
for (int j = 0; j < this.m_Optima.size(); j++) {
if (!found[j]) {
opt = (AbstractEAIndividual) this.m_Optima.get(j);
if (this.m_Metric.distance(posOpt, opt) < this.m_Epsilon) {
found[j] = true;
result += this.m_GlobalOpt - posOpt.getFitness(0);
//System.out.println("Found Optimum " + j + ".: " + (this.m_GlobalOpt - posOpt.getFitness(0)));
}
}
}
}
return result/sum;
for (int i=0; i<m_Optima.size(); i++) {
// sum up known optimal fitness values
optimaInvertedSum += m_Optima.getEAIndividual(i).getFitness(0);
// sum up best found hits, with inverted fitness
if (optsFound[i] != null) foundInvertedSum += m_GlobalOpt - optsFound[i].getFitness(0);
}
return foundInvertedSum/optimaInvertedSum;
}
// public double getMaximumPeakRatio(Population pop) {
// double result = 0, sum = 0;
// AbstractEAIndividual posOpt, opt;
// boolean[] found = new boolean[this.m_Optima.size()];
// for (int i = 0; i < found.length; i++) {
// found[i] = false;
// sum += ((AbstractEAIndividual)this.m_Optima.get(i)).getFitness(0) ;
// //System.out.println("Optimum " + i + ".: " + (((AbstractEAIndividual)this.m_Optima.get(i)).getFitness(0)));
// }
//
// for (int i = 0; i < pop.size(); i++) {
// posOpt = (AbstractEAIndividual) pop.get(i);
// for (int j = 0; j < this.m_Optima.size(); j++) {
// if (!found[j]) {
// opt = (AbstractEAIndividual) this.m_Optima.get(j);
// if (this.m_Metric.distance(posOpt, opt) < this.m_Epsilon) {
// found[j] = true;
// result += this.m_GlobalOpt - posOpt.getFitness(0);
// //System.out.println("Found Optimum " + j + ".: " + (this.m_GlobalOpt - posOpt.getFitness(0)));
// }
// }
// }
// }
// return result/sum;
// }
/**********************************************************************************************************************
* These are for GUI
*/
@ -317,15 +329,15 @@ public class FM0Problem extends F1Problem implements Interface2DBorderProblem, I
public double functionValue(double[] point) {
return evalUnnormalized(point)[0];
}
/** This method returns this min and may fitness occuring
* @return double[]
*/
public double[] getExtrema() {
double[] range = new double[2];
range[0] = -5;
range[1] = 5;
return range;
}
// /** This method returns this min and may fitness occuring
// * @return double[]
// */
// public double[] getExtrema() {
// double[] range = new double[2];
// range[0] = -5;
// range[1] = 5;
// return range;
// }
/**
* @return the m_Epsilon
@ -344,4 +356,9 @@ public class FM0Problem extends F1Problem implements Interface2DBorderProblem, I
public String epsilonTipText() {
return "Epsilon criterion indicating whether an optimum was found";
}
@Override
public int getProblemDimension() {
return m_ProblemDimension;
}
}

52
src/javaeva/server/go/problems/InterfaceMultimodalProblemKnown.java Normal file
View File

@ -0,0 +1,52 @@
package javaeva.server.go.problems;
import javaeva.server.go.populations.Population;
/**
* A multimodal problem which has knowledge of its optima.
*
* User: streiche
* Date: 23.04.2003
* Time: 10:57:47
* To change this template use Options | File Templates.
*/
public interface InterfaceMultimodalProblemKnown extends InterfaceMultimodalProblem {
/**
* This method will prepare the problem to return a list of all optima
* if possible and to return quality measures like NumberOfOptimaFound and
* the MaximumPeakRatio. This method should be called by the user.
*/
public void initListOfOptima();
/**
* This method returns a list of all optima as population or null if
* the optima are unknown.
*
* @return population
*/
public Population getRealOptima();
/**
* Return the number of identified optima or -1 if
* the real optima are unknown.
* @param pop A population of possible solutions.
* @return int
*/
public int getNumberOfFoundOptima(Population pop);
/**
* This method returns the Maximum Peak Ratio.
* @param pop A population of possible solutions.
* @return double
*/
public double getMaximumPeakRatio(Population pop);
/**
* Return the maximum normed distance to a known optimum for which the
* optimum is considered found.
*
* @return
*/
public double getEpsilon();
}

15
src/javaeva/server/go/problems/InterfaceOptimizationProblem.java
View File

@ -50,13 +50,14 @@ public interface InterfaceOptimizationProblem extends InterfaceAdditionalPopulat
*/
public void evaluate(AbstractEAIndividual individual);
/** This method should be used to calculate the distance between two
* individuals
* @param indy1 The first individual.
* @param indy2 The second individual.
* @return The distance.
*/
public double distanceBetween(AbstractEAIndividual indy1, AbstractEAIndividual indy2);
// DEPRECATED, use PhenotypeMetric
//** This method should be used to calculate the distance between two
// * individuals
// * @param indy1 The first individual.
// * @param indy2 The second individual.
// * @return The distance.
// */
// public double distanceBetween(AbstractEAIndividual indy1, AbstractEAIndividual indy2);
/******************** Some output methods *******************************************/

6
src/javaeva/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
View File

@ -239,7 +239,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
*/
public String getStringRepresentation() {
String result = "";
result += "Genetic Algorithm:\n";
result += "CHC Adaptive Search Algorithm:\n";
result += "Optimization Problem: ";
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
result += this.m_Population.getStringRepresentation();
@ -294,6 +294,10 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
// /** This method will set the normation method that is to be used.
// * @param normation
// */

218
src/javaeva/server/go/strategies/ClusterBasedNichingEA.java
View File

@ -1,5 +1,13 @@
package javaeva.server.go.strategies;
import java.util.ArrayList;
import javaeva.gui.BeanInspector;
import javaeva.gui.Chart2DDPointIconCircle;
import javaeva.gui.Chart2DDPointIconText;
import javaeva.gui.GraphPointSet;
import javaeva.gui.Plot;
import javaeva.gui.TopoPlot;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.InterfaceDataTypeDouble;
@ -7,18 +15,15 @@ import javaeva.server.go.operators.cluster.ClusteringDensityBased;
import javaeva.server.go.operators.cluster.InterfaceClustering;
import javaeva.server.go.operators.mutation.InterfaceMutation;
import javaeva.server.go.operators.mutation.MutateESGlobal;
import javaeva.server.go.operators.selection.InterfaceSelection;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.B1Problem;
import javaeva.server.go.problems.Interface2DBorderProblem;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.server.go.problems.TF1Problem;
import javaeva.server.go.tools.RandomNumberGenerator;
import javaeva.gui.*;
import java.util.ArrayList;
import wsi.ra.chart2d.DPointSet;
import wsi.ra.chart2d.DPoint;
import wsi.ra.chart2d.DPointIcon;
import wsi.ra.chart2d.DPointSet;
/** The infamuos clustering based niching EA, still under construction.
* It should be able to identify and track multiple global/local optima
@ -37,6 +42,7 @@ import wsi.ra.chart2d.DPointIcon;
public class ClusterBasedNichingEA implements InterfacePopulationChangedEventListener, InterfaceOptimizer, java.io.Serializable {
private Population m_Population = new Population();
private transient Population m_Archive = new Population();
public ArrayList<Population> m_Species = new ArrayList<Population>();
public Population m_Undifferentiated = new Population();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
@ -52,13 +58,15 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private int m_actSpecSize = 2;
private int m_minGroupSize = 3;
private boolean m_UseClearing = false;
private boolean m_UseArchive = true;
private boolean m_UseSpeciesDifferentation = true;
private boolean m_UseSpeciesConvergence = true;
private boolean m_UseHaltingWindow = true;
private int m_PopulationSize = 50;
private int convergedCnt = 0;
private boolean m_Debug = true;
private int m_ShowCycle = 10;
private static boolean TRACE = false;
private int m_ShowCycle = 100;
transient private TopoPlot m_Topology;
private int haltingWindow = 15;
private double muLambdaRatio = 0.5;
@ -98,8 +106,12 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.m_Undifferentiated.setPopulationSize(this.m_PopulationSize);
this.m_Species = new ArrayList<Population>();
this.m_Problem.initPopulation(this.m_Undifferentiated);
this.getPopulation().setUseHistory(true);
this.evaluatePopulation(this.m_Undifferentiated);
this.m_Optimizer.initByPopulation(m_Undifferentiated, false);
m_Archive = new Population();
convergedCnt = 0;
this.m_Undifferentiated = m_Optimizer.getPopulation(); // required for changes to the population by the optimizer
// this.getPopulation().setUseHistory(true); // TODO this makes no sense if getPopulation clones the pop.
this.firePropertyChangedEvent("FirstGenerationPerformed");
}
@ -114,7 +126,11 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
if (reset) this.m_Undifferentiated.init();
this.m_Undifferentiated.setPopulationSize(this.m_PopulationSize);
this.m_Species = new ArrayList<Population>();
m_Archive = new Population();
convergedCnt = 0;
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
this.firePropertyChangedEvent("FirstGenerationPerformed");
}
@ -195,29 +211,28 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
popRep.addDPoint(new DPoint(best.getDoubleData()[0], best.getDoubleData()[1]));
this.m_Topology.getFunctionArea().addDElement(popRep);
}
// popRep = new DPointSet();
// tmpIndy1 = (InterfaceDataTypeDouble)pop.getBestEAIndividual();
// popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1]));
// double d = Math.round(100*((AbstractEAIndividual)tmpIndy1).getFitness(0))/(double)100;
// DPointIcon icon = new Chart2DDPointIconText(""+d);
// ((Chart2DDPointIconText)icon).setIcon(new Chart2DDPointIconCircle());
// popRep.setIcon(icon);
// this.m_Topology.m_PlotArea.addDElement(popRep);
} else {
// this is an inactive species
popRep = new DPointSet();
tmpIndy1 = (InterfaceDataTypeDouble)pop.get(0);
popRep.addDPoint(new DPoint(tmpIndy1.getDoubleData()[0], tmpIndy1.getDoubleData()[1]));
double d = Math.round(100*((AbstractEAIndividual)tmpIndy1).getFitness(0))/(double)100;
DPointIcon icon = new Chart2DDPointIconText(""+d);
((Chart2DDPointIconText)icon).setIcon(new Chart2DDPointIconCircle());
popRep.setIcon(icon);
this.m_Topology.getFunctionArea().addDElement(popRep);
plotInactive((InterfaceDataTypeDouble)pop.get(0));
}
}
for (int i = 0; i < this.m_Archive.size(); i++) {
plotInactive((InterfaceDataTypeDouble)m_Archive.get(i));
}
}
}
private void plotInactive(InterfaceDataTypeDouble tmpIndy) {
DPointSet popRep;
popRep = new DPointSet();
popRep.addDPoint(new DPoint(tmpIndy.getDoubleData()[0], tmpIndy.getDoubleData()[1]));
double d = Math.round(100*((AbstractEAIndividual)tmpIndy).getFitness(0))/(double)100;
DPointIcon icon = new Chart2DDPointIconText(""+d);
((Chart2DDPointIconText)icon).setIcon(new Chart2DDPointIconCircle());
popRep.setIcon(icon);
this.m_Topology.getFunctionArea().addDElement(popRep);
}
/**
* This method is used to cap the mutation rate.
* For the global ES mutation, set the mutation rate to not more than cap.
@ -263,17 +278,19 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
ArrayList tmpA = pop.m_History;
int length = pop.m_History.size();
if (length <= haltingWindow) return false;
else {
if (length <= haltingWindow) {
// System.out.println("not long enough... gen " + pop.getGeneration());
return false;
} else {
AbstractEAIndividual historic = ((AbstractEAIndividual)pop.m_History.get(length-haltingWindow));
for (int i = 1; i < haltingWindow; i++) {
for (int i = 1; i < haltingWindow; i++) { // TODO
if (historic.getFitness(0) > ((AbstractEAIndividual)pop.m_History.get(length-haltingWindow+i)).getFitness(0)) {
//System.out.println("( " + historic.getFitness(0) + "/" + ((AbstractEAIndividual)pop.m_History.get(length-hw+i)).getFitness(0));
// System.out.println("( " + historic.getFitness(0) + "/" + ((AbstractEAIndividual)pop.m_History.get(length-haltingWindow+i)).getFitness(0));
return false;
}
}
}
if (this.m_Debug) {
if (TRACE) {
double[] a = new double[2];
a[0] = 0; a[1] = 0;
Plot plot = new Plot("HaltingWindow", "History", "Fitness", a, a);
@ -287,18 +304,24 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
private Population optimizeSpecies(Population species) {
m_Optimizer.setPopulation(species);
// m_Optimizer.initByPopulation(species, false);
if (m_Optimizer instanceof EvolutionStrategies) {
EvolutionStrategies es = (EvolutionStrategies)m_Optimizer;
int mu = (int)(muLambdaRatio*species.size());
if (mu < 1) mu = 1;
else if (mu >= species.size()) {
if (m_Debug) System.err.println("warning, muLambdaRatio produced mu >= lambda.. reducing to mu=lambda-1");
if (TRACE) System.err.println("warning, muLambdaRatio produced mu >= lambda.. reducing to mu=lambda-1");
mu = species.size() - 1;
}
es.setMyu(mu);
es.setLambda(species.size());
}
if (TRACE) {
System.out.println("spec size: " + species.size() + ", says its " + species.getPopulationSize());
System.out.println("Best bef: " + BeanInspector.toString(m_Optimizer.getPopulation().getBestFitness()));
}
this.m_Optimizer.optimize();
if (TRACE) System.out.println("Best aft: " + BeanInspector.toString(m_Optimizer.getPopulation().getBestFitness()));
return m_Optimizer.getPopulation();
}
@ -313,7 +336,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
}
// species evolution phase
if (this.m_Debug) {
if (TRACE) {
System.out.println("");
System.out.println("Optimizing Generation " + this.m_Undifferentiated.getGeneration());
}
@ -325,40 +348,53 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
m_Undifferentiated = optimizeSpecies(m_Undifferentiated);
} else {
this.m_Undifferentiated.incrGeneration();
//m_Undifferentiated.incrFunctionCallsby(m_Undifferentiated.size()); // TODO this is not optimal, an evaluation is wasted to keep N steps per gen.
}
// now the population is of max size
if (this.m_Debug) {
if (TRACE) {
System.out.println("-Undiff. size: " + this.m_Undifferentiated.size());
System.out.println("-Number of Demes: " + this.m_Species.size());
System.out.println("-Funcalls: "+m_Undifferentiated.getFunctionCalls());
}
int reinitCount;
Population curSpecies;
// optimize the clustered species
for (int i = 0; i < this.m_Species.size(); i++) {
if (this.m_Debug) System.out.println("-Deme " + i + " size: " + ((Population)this.m_Species.get(i)).size());
for (int i = this.m_Species.size() - 1; i >= 0; i--) {
if (TRACE) System.out.println("-Deme " + i + " size: " + ((Population)this.m_Species.get(i)).size());
curSpecies = ((Population)this.m_Species.get(i));
curSpecies.SetFunctionCalls(0);
curSpecies.setPopulationSize(curSpecies.size());
reinitCount = curSpecies.size()-1;
if (isActive(curSpecies)) {
if ((this.m_UseHaltingWindow) && (this.testSpeciesForConvergence(curSpecies))) {
///////////////////////////////////////////// Halting Window /////////////////////////////////////////////////
if (this.m_Debug) System.out.println("--Converged");
// if (this.m_Debug) {
// System.out.println("Undiff.Size: " + this.m_Undifferentiated.size() +"/"+this.m_Undifferentiated.getPopulationSize());
// System.out.println("Diff.Size : " + ((Population)this.m_Species.get(i)).size() +"/"+((Population)this.m_Species.get(i)).getPopulationSize());
// }
convergedCnt++;
if (TRACE) System.out.println("--Converged: "+convergedCnt);
// memorize the best one....
AbstractEAIndividual best = (AbstractEAIndividual)curSpecies.getBestEAIndividual().getClone();
// now reset the converged species to inactivity size = 1
curSpecies.setPopulationSize(1);
curSpecies.clear();
curSpecies.add(best);
int reinitCount = -1;
if (m_UseArchive) {
m_Archive.add(best);
m_Species.remove(i); //REMOVES the converged Species
reinitCount = curSpecies.size(); // add all as new
} else {
// now reset the converged species to inactivity size = 1
curSpecies.setPopulationSize(1);
// m_Undifferentiated.incrFunctionCallsby(1); // TODO not so good
curSpecies.clear();
curSpecies.add(best);
reinitCount = curSpecies.size()-1; // add all but one as new
}
// reinit the surplus individuals and add these new individuals to undifferentiated
this.m_Undifferentiated.addPopulation(this.initializeIndividuals(reinitCount));
this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()+reinitCount);
m_Undifferentiated.addPopulation(this.initializeIndividuals(reinitCount));
m_Undifferentiated.incrFunctionCallsby(reinitCount);
m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()+reinitCount);
// if (this.m_Debug) {
// System.out.println("Undiff.Size: " + this.m_Undifferentiated.size() +"/"+this.m_Undifferentiated.getPopulationSize());
// System.out.println("Diff.Size : " + ((Population)this.m_Species.get(i)).size() +"/"+((Population)this.m_Species.get(i)).getPopulationSize());
@ -376,20 +412,33 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
}
} else {
// a single individual species, this element is inactive
if (m_Debug) System.out.println("inactive species not optimized");
if (TRACE) System.out.println("inactive species not optimized");
// curSpecies.incrFunctionCallsby(curSpecies.size()); // was todo: not so good
}
// This is necessary to keep track to the function calls needed
// This is necessary to keep track of the function calls needed
this.m_Undifferentiated.SetFunctionCalls(this.m_Undifferentiated.getFunctionCalls() + curSpecies.getFunctionCalls());
if (TRACE) System.out.println("### funcalls: "+m_Undifferentiated.getFunctionCalls());
}
if (this.m_Debug) System.out.println("-Number of Demes: " + this.m_Species.size());
if (TRACE) System.out.println("-Number of Demes: " + this.m_Species.size());
//////////////////////
if (!isActive(m_Undifferentiated)) {
if (TRACE) System.out.println("Inactive Undiff-pop, adding " + m_Undifferentiated.size() + " fun calls...");
m_Undifferentiated.incrFunctionCallsby(m_Undifferentiated.size());
}
if (this.m_Undifferentiated.getFunctionCalls() % this.m_PopulationSize != 0) {
if (true) System.out.println("### mismatching number of funcalls, inactive species? Correcting by " + (m_PopulationSize - (m_Undifferentiated.getFunctionCalls() % m_PopulationSize)));
if (true) System.out.println("### undiff " + ((isActive(m_Undifferentiated)) ? "active!" : "inactive!"));
m_Undifferentiated.incrFunctionCallsby(m_PopulationSize - (m_Undifferentiated.getFunctionCalls() % m_PopulationSize));
} else if (TRACE) System.out.println("### undiff active: " + isActive(m_Undifferentiated));
// possible species differentation and convergence
if (this.m_Undifferentiated.getGeneration()%this.m_SpeciesCycle == 0) {
if (this.m_Debug) System.out.println("Species cycle:");
if (TRACE) System.out.println("Species cycle:");
if (this.m_UseSpeciesDifferentation) {
// species differentation phase
if (this.m_Debug) System.out.println("-Sepecies Differentation:");
if (TRACE) System.out.println("-Sepecies Differentation:");
Population[] ClusterResult;
ArrayList<Population> newSpecies = new ArrayList<Population>();
//cluster the undifferentiated population
@ -397,20 +446,23 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.m_Undifferentiated = ClusterResult[0];
for (int j = 1; j < ClusterResult.length; j++) {
ClusterResult[j].setUseHistory(true);
ClusterResult[j].setGenerationTo(0);
ClusterResult[j].m_History = new ArrayList();
newSpecies.add(ClusterResult[j]);
}
for (int i = 0; i < this.m_Species.size(); i++) {
if (isActive(this.m_Species.get(i))) {
curSpecies = this.m_Species.get(i);
if (isActive(curSpecies)) {
// only active populations are clustered
ClusterResult = this.m_CAForSpeciesDifferentation.cluster((Population)this.m_Species.get(i));
ClusterResult = this.m_CAForSpeciesDifferentation.cluster(curSpecies);
this.m_Undifferentiated.addPopulation(ClusterResult[0]);
ClusterResult[0].setUseHistory(true);
// ClusterResult[0].setUseHistory(true);
this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize() + ClusterResult[0].size());
for (int j = 1; j < ClusterResult.length; j++) {
ClusterResult[j].setPopulationSize(ClusterResult[j].size());
ClusterResult[j].setUseHistory(true);
if (ClusterResult.length > 2) ClusterResult[j].m_History = new ArrayList();
// if (ClusterResult.length > 2) ClusterResult[j].m_History = new ArrayList(); // mk: why min 3? Ill copy the history from the original pop for any j...
ClusterResult[j].m_History = (ArrayList) curSpecies.m_History.clone();
newSpecies.add(ClusterResult[j]);
}
} else {
@ -419,7 +471,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
}
}
this.m_Species = newSpecies;
if (this.m_Debug) {
if (TRACE) {
System.out.println("--Number of species: " + this.m_Species.size());
System.out.println("---Undiff size: " + this.m_Undifferentiated.size());
for (int i = 0; i < this.m_Species.size(); i++) {
@ -431,7 +483,10 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
if (this.m_UseSpeciesConvergence) {
//species convergence phase
if (this.m_Debug) System.out.println("-Species convergence:");
if (TRACE) {
System.out.println("-Species convergence:");
System.out.println("-Funcalls: " + m_Undifferentiated.getFunctionCalls());
}
// first test if loners belong to any species
boolean found = false;
@ -442,7 +497,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
curSpecies = (Population)this.m_Species.get(j);
AbstractEAIndividual tmpIndy = (AbstractEAIndividual)this.m_Undifferentiated.get(i);
if (this.m_CAForSpeciesConvergence.belongsToSpecies(tmpIndy, curSpecies)) {
if (this.m_Debug) System.out.println("--Adding loner to species "+j);
if (TRACE) System.out.println("--Adding loner to species "+j);
found = true;
this.m_Undifferentiated.remove(i);
if (isActive(curSpecies)) {
@ -452,6 +507,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
} else {
// the species is inactive, reinitialize the individual (MK: why?)
this.m_Undifferentiated.add(i, this.initializeIndividuals(1).get(0));
// m_Undifferentiated.incrFunctionCallsby(1);
}
}
j++;
@ -464,34 +520,36 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
for (int j = i+1; j < this.m_Species.size(); j++) {
spec2 = (Population)this.m_Species.get(j);
if (this.m_CAForSpeciesConvergence.convergingSpecies(spec1, spec2)) {
if (this.m_Debug) System.out.println("--------------------Merging species (" + i +", " +j +") ["+spec1.size()+"/"+spec2.size()+"]");
if (TRACE) System.out.println("--------------------Merging species (" + i +", " +j +") ["+spec1.size()+"/"+spec2.size()+"]");
// this.m_CAForSpeciesConvergence.convergingSpecies(spec1, spec2);
if (isActive(spec1) && isActive(spec2)) {
if (this.m_Debug) System.out.println("---Active merge");
if (TRACE) System.out.println("---Active merge");
spec1.addPopulation(spec2);
if (spec2.m_History.size() > spec1.m_History.size()) spec1.m_History = spec2.m_History;
this.m_Species.remove(j);
j--;
} else {
if (this.m_Debug) System.out.println("---Inactive merge");
if (TRACE) System.out.println("---Inactive merge");
// save best in singular species and reinit the rest of the individuals
spec1.addPopulation(spec2);
this.m_Species.remove(j);
j--;
AbstractEAIndividual best = (AbstractEAIndividual)spec1.getBestEAIndividual().getClone();
reinitCount = spec1.size()-1;
int reinitCount = spec1.size()-1;
// now reset the converged species to inactivity size = 0
spec1.setPopulationSize(1);
spec1.clear();
spec1.add(best);
// reinitialized individuals and add them to undifferentiated
this.m_Undifferentiated.addPopulation(this.initializeIndividuals(reinitCount));
// m_Undifferentiated.incrFunctionCallsby(reinitCount);
this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()+reinitCount);
}
}
}
}
if (this.m_Debug) System.out.println("--Number of species: " + this.m_Species.size());
if (TRACE) System.out.println("--Number of species: " + this.m_Species.size());
}
// if (this.m_UseClearing) {
@ -509,15 +567,17 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
// }
}
// output the result
if (this.m_Debug) System.out.println("-Number of species: " + this.m_Species.size());
if (TRACE) System.out.println("-Number of species: " + this.m_Species.size());
if (TRACE) System.out.println("-Funcalls: " + this.m_Undifferentiated.getFunctionCalls());
this.m_Population = (Population)this.m_Undifferentiated.clone();
m_Population.setUseHistory(true);
if (this.m_Debug) System.out.println("initing with " + this.m_Undifferentiated.size());
if (TRACE) System.out.println("initing with " + this.m_Undifferentiated.size());
for (int i = 0; i < this.m_Species.size(); i++) {
if (this.m_Debug) System.out.println("Adding deme " + i + " with size " + ((Population)this.m_Species.get(i)).size());
if (TRACE) System.out.println("Adding deme " + i + " with size " + ((Population)this.m_Species.get(i)).size());
this.m_Population.addPopulation((Population)this.m_Species.get(i));
}
if (this.m_Debug) System.out.println("Population size: " + this.m_Population.size());
if (TRACE) System.out.println("Population size: " + this.m_Population.size());
this.firePropertyChangedEvent("NextGenerationPerformed");
}
@ -607,26 +667,32 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return "CBN-EA";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
this.m_Population = (Population)m_Undifferentiated.clone();
// m_Population.addPopulation(this.m_Undifferentiated);
for (int i = 0; i < this.m_Species.size(); i++) this.m_Population.addPopulation((Population)this.m_Species.get(i));
m_Population.setPopulationSize(m_Population.size()); // set it to true value
return this.m_Population;
}
public void setPopulation(Population pop){
this.m_Undifferentiated = pop;
pop.setUseHistory(true);
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
/** This method allows you to set/get the switch that toggels the use
* of species differentation.
public Population getAllSolutions() {
// return inactive species
Population pop = (Population)m_Archive.clone();
pop.addPopulation(getPopulation());
pop.setPopulationSize(pop.size());
return pop;
}
/** This method allows you to set/get the switch that toggles the use
* of species differentiation.
* @return The current status of this flag
*/
public boolean getApplyDifferentation() {
@ -662,7 +728,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
this.m_UseHaltingWindow = b;
}
public String useHaltingWindowTipText() {
return "With a halting window converged species are freezed.";
return "With a halting window converged species are frozen.";
}
/** This method allows you to set/get the switch that toggels the use
@ -722,6 +788,16 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
return "The cluster algorithm on which the species convergence is based.";
}
public void setUseArchive(boolean v) {
m_UseArchive = v;
}
public boolean isUseArchive() {
return m_UseArchive;
}
public String useArchiveTipText() {
return "Toggle usage of an archive where converged species are savend and the individuals reinitialized.";
}
/** Determines how often species differentation/convergence is performed.
* @return This number gives the generations when specification is performed.
*/

324
src/javaeva/server/go/strategies/ClusteringHillClimbing.java Normal file
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@ -0,0 +1,324 @@
package javaeva.server.go.strategies;
import java.io.Serializable;
import javaeva.gui.GenericObjectEditor;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.operators.mutation.MutateESFixedStepSize;
import javaeva.server.go.operators.postprocess.PostProcess;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.AbstractOptimizationProblem;
import javaeva.server.go.problems.F1Problem;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.tools.Pair;
public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener, InterfaceOptimizer, Serializable {
transient private InterfacePopulationChangedEventListener m_Listener;
transient private String m_Identifier = "";
private Population m_Population = new Population();
private transient Population archive = new Population();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
private int hcEvalCycle = 1000;
private int initialPopSize = 200;
private int loopCnt = 0;
// private int baseEvalCnt = 0;
private int notifyGuiEvery = 50;
private double sigma = 0.01;
private double minImprovement = 0.000001;
private double reinitForStepSize = 0.000001;
private double initialStepSize = 0.1;
private MutateESFixedStepSize mutator = new MutateESFixedStepSize(0.1);
public ClusteringHillClimbing() {
hideHideable();
}
public ClusteringHillClimbing(ClusteringHillClimbing other) {
hideHideable();
m_Population = (Population)other.m_Population.clone();
m_Problem = (InterfaceOptimizationProblem)other.m_Problem.clone();
hcEvalCycle = other.hcEvalCycle;
initialPopSize = other.initialPopSize;
loopCnt = 0;
}
public Object clone() {
return (Object) new ClusteringHillClimbing(this);
}
/**
* Hide the population.
*/
public void hideHideable() {
GenericObjectEditor.setHideProperty(getClass(), "population", true);
}
public void SetIdentifier(String name) {
this.m_Identifier = name;
}
public String getIdentifier() {
return this.m_Identifier;
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void SetProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
}
public InterfaceOptimizationProblem getProblem () {
return this.m_Problem;
}
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
}
public void init() {
loopCnt = 0;
mutator = new MutateESFixedStepSize(initialStepSize);
archive = new Population();
hideHideable();
m_Population.setPopulationSize(initialPopSize);
this.m_Problem.initPopulation(this.m_Population);
m_Population.addPopulationChangedEventListener(null);
this.m_Problem.evaluate(this.m_Population);
this.firePropertyChangedEvent("NextGenerationPerformed");
}
/** This method will init the optimizer with a given population
* @param pop The initial population
* @param reset If true the population is reset.
*/
public void initByPopulation(Population pop, boolean reset) {
loopCnt = 0;
this.m_Population = (Population)pop.clone();
m_Population.addPopulationChangedEventListener(null);
if (reset) this.m_Population.init();
this.m_Problem.evaluate(this.m_Population);
this.firePropertyChangedEvent("NextGenerationPerformed");
}
/** Something has changed
*/
protected void firePropertyChangedEvent (String name) {
if (this.m_Listener != null) this.m_Listener.registerPopulationStateChanged(this, name);
}
public void optimize() {
double improvement;
loopCnt++;
m_Population.addPopulationChangedEventListener(this);
m_Population.setNotifyEvalInterval(notifyGuiEvery);
Pair<Population, Double> popD = PostProcess.clusterHC(m_Population, (AbstractOptimizationProblem)m_Problem, sigma, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5, mutator);
improvement = popD.tail();
popD.head().setGenerationTo(m_Population.getGeneration()+1);
m_Population = popD.head();
if (improvement < minImprovement) {
System.out.println("improvement below " + minImprovement);
if (mutator.getSigma() < reinitForStepSize) { // reinit!
System.out.println("REINIT!!");
// store results
mutator.setSigma(initialStepSize);
archive.SetFunctionCalls(m_Population.getFunctionCalls());
archive.addPopulation(m_Population);
Population tmpPop = new Population();
tmpPop.setSameParams(m_Population);
tmpPop.addPopulationChangedEventListener(null);
tmpPop.setPopulationSize(initialPopSize);
this.m_Problem.initPopulation(tmpPop);
this.m_Problem.evaluate(tmpPop);
// reset population while keeping function calls etc.
m_Population.clear();
m_Population.addPopulation(tmpPop);
m_Population.incrFunctionCallsby(tmpPop.size());
} else { // decrease step size
mutator.setSigma(mutator.getSigma()/2);
System.out.println("halfed sigma to " + mutator.getSigma());
}
}
// System.out.println("funcalls: " + evalCnt);
// this.firePropertyChangedEvent("NextGenerationPerformed");
}
public void registerPopulationStateChanged(Object source, String name) {
// The events of the interim hill climbing population will be caught here
if (name.compareTo(Population.funCallIntervalReached) == 0) {
// if ((((Population)source).size() % 50) > 0) {
// System.out.println("bla");
// }
// set funcalls to real value
m_Population.SetFunctionCalls(((Population)source).getFunctionCalls());
// System.out.println("FunCallIntervalReached at " + (((Population)source).getFunctionCalls()));
this.firePropertyChangedEvent("NextGenerationPerformed");
}
// do not react to NextGenerationPerformed
//else System.err.println("ERROR, event was " + name);
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.m_Population;
}
public void setPopulation(Population pop){
this.m_Population = pop;
}
public String populationTipText() {
return "Change the number of starting individuals stored (Cluster-HC).";
}
public Population getAllSolutions() {
Population tmp = new Population();
tmp.addPopulation(archive);
tmp.addPopulation(m_Population);
// tmp = PostProcessInterim.clusterBest(tmp, sigma, 0, PostProcessInterim.KEEP_LONERS, PostProcessInterim.BEST_ONLY);
return tmp;
}
/** This method will return a string describing all properties of the optimizer
* and the applied methods.
* @return A descriptive string
*/
public String getStringRepresentation() {
StringBuffer sbuf = new StringBuffer("Clustering Hill Climbing");
sbuf.append(", initial pop size: ");
sbuf.append(getPopulation().getPopulationSize());
sbuf.append("Optimization Problem: ");
sbuf.append(this.m_Problem.getStringRepresentationForProblem(this));
sbuf.append(this.m_Population.getStringRepresentation());
return sbuf.toString();
}
public void freeWilly() {}
public String getName() {
return "Cluster-HC";
}
/**
* @return the hcEvalCycle
*/
public int getHcEvalCycle() {
return hcEvalCycle;
}
/**
* @param hcEvalCycle the hcEvalCycle to set
*/
public void setHcEvalCycle(int hcEvalCycle) {
this.hcEvalCycle = hcEvalCycle;
}
/**
* @return the initialPopSize
*/
public int getInitialPopSize() {
return initialPopSize;
}
/**
* @param initialPopSize the initialPopSize to set
*/
public void setInitialPopSize(int initialPopSize) {
this.initialPopSize = initialPopSize;
}
/**
* @return the sigma
*/
public double getSigma() {
return sigma;
}
/**
* @param sigma the sigma to set
*/
public void setSigma(double sigma) {
this.sigma = sigma;
}
public String sigmaTipText() {
return "Defines the sigma distance parameter for density based clustering.";
}
/**
* @return the notifyGuiEvery
*/
public int getNotifyGuiEvery() {
return notifyGuiEvery;
}
/**
* @param notifyGuiEvery the notifyGuiEvery to set
*/
public void setNotifyGuiEvery(int notifyGuiEvery) {
this.notifyGuiEvery = notifyGuiEvery;
}
/**
* @return the minImprovement
*/
public double getMinImprovement() {
return minImprovement;
}
/**
* @param minImprovement the minImprovement to set
*/
public void setMinImprovement(double minImprovement) {
this.minImprovement = minImprovement;
}
/**
* @return the reinitForStepSize
*/
public double getReinitForStepSize() {
return reinitForStepSize;
}
/**
* @param reinitForStepSize the reinitForStepSize to set
*/
public void setReinitForStepSize(double reinitForStepSize) {
this.reinitForStepSize = reinitForStepSize;
}
/**
* @return the initialStepSize
*/
public double getInitialStepSize() {
return initialStepSize;
}
/**
* @param initialStepSize the initialStepSize to set
*/
public void setInitialStepSize(double initialStepSize) {
this.initialStepSize = initialStepSize;
}
// /**
// * @return the mutator
// */
// public InterfaceMutation getMutator() {
// return mutator;
// }
//
// /**
// * @param mutator the mutator to set
// */
// public void setMutator(InterfaceMutation mutator) {
// this.mutator = mutator;
// }
}

4
src/javaeva/server/go/strategies/DifferentialEvolution.java
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@ -477,6 +477,10 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
/** F is a real and constant factor which controls the amplification of the differential variation
* @param f
*/

3
src/javaeva/server/go/strategies/EvolutionStrategies.java
View File

@ -359,6 +359,9 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
// /** This method will set the normation method that is to be used.
// * @param normation
// */

4
src/javaeva/server/go/strategies/EvolutionaryProgramming.java
View File

@ -198,6 +198,10 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
public String populationTipText() {
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
/** Choose a method for selecting the reduced population.
* @param selection
*/

4
src/javaeva/server/go/strategies/FloodAlgorithm.java
View File

@ -233,6 +233,10 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
return "Change the number of best individuals stored (MS-FA).";
}
public Population getAllSolutions() {
return getPopulation();
}
/** This methods allow you to set/get the temperatur of the flood
* algorithm procedure
* @return The initial flood level.

3
src/javaeva/server/go/strategies/GeneticAlgorithm.java
View File

@ -239,6 +239,9 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
// /** This method will set the normation method that is to be used.
// * @param normation
// */

4
src/javaeva/server/go/strategies/GradientDescentAlgorithm.java
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@ -257,6 +257,10 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
return this.m_Population;
}
public Population getAllSolutions() {
return getPopulation();
}
public void setPopulation(Population pop) {
Hashtable newindyhash = new Hashtable();
for (int i = 0; i < pop.size(); i++) {

36
src/javaeva/server/go/strategies/HillClimbing.java
View File

@ -2,7 +2,7 @@ package javaeva.server.go.strategies;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.individuals.GAIndividualBinaryData;
import javaeva.server.go.operators.mutation.InterfaceMutation;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.B1Problem;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
@ -22,10 +22,11 @@ import javaeva.server.go.problems.InterfaceOptimizationProblem;
public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
// These variables are necessary for the simple testcase
private InterfaceOptimizationProblem m_Problem = new B1Problem();
private InterfaceMutation mutator = null;
// private int m_MultiRuns = 100;
// private int m_FitnessCalls = 100;
// private int m_FitnessCallsNeeded = 0;
GAIndividualBinaryData m_Best, m_Test;
// GAIndividualBinaryData m_Best, m_Test;
// These variables are necessary for the more complex LectureGUI enviroment
transient private String m_Identifier = "";
@ -70,12 +71,15 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
public void optimize() {
AbstractEAIndividual indy;
Population original = (Population)this.m_Population.clone();
double tmpD;
InterfaceMutation tmpMut;
for (int i = 0; i < this.m_Population.size(); i++) {
indy = ((AbstractEAIndividual) this.m_Population.get(i));
double tmpD = indy.getMutationProbability();
tmpD = indy.getMutationProbability();
indy.setMutationProbability(1.0);
indy.mutate();
if (mutator == null) indy.mutate();
else mutator.mutate(indy);
indy.setMutationProbability(tmpD);
}
this.m_Problem.evaluate(this.m_Population);
@ -103,6 +107,19 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
this.firePropertyChangedEvent("NextGenerationPerformed");
}
public InterfaceMutation getMutationOperator() {
return mutator;
}
/**
* Allows to set a desired mutator by hand, which is used instead of the one in the individuals.
* Set it to null to use the one in the individuals, which is the default.
*
* @param mute
*/
public void SetMutationOperator(InterfaceMutation mute) {
mutator = mute;
}
/** This method will set the problem that is to be optimized
* @param problem
@ -200,7 +217,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
* @return description
*/
public String globalInfo() {
return "The Hill CLimber uses the default EA mutation and init operators. If the population size is bigger than one a multi-start Hill Climber is performed.";
return "The Hill Climber uses the default EA mutation and initializing operators. If the population size is bigger than one a multi-start Hill Climber is performed.";
}
/** This method will return a naming String
* @return The name of the algorithm
@ -208,17 +225,16 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
public String getName() {
return "MS-HC";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
* @return The population of current solutions to a given problem.
*/
public Population getPopulation() {
return this.m_Population;
}
public void setPopulation(Population pop){
this.m_Population = pop;
}
public Population getAllSolutions() {
return getPopulation();
}
public String populationTipText() {
return "Change the number of best individuals stored (MS-HC).";
}

19
src/javaeva/server/go/strategies/InterfaceOptimizer.java
View File

@ -1,6 +1,9 @@
package javaeva.server.go.strategies;
import java.util.List;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
@ -49,16 +52,24 @@ public interface InterfaceOptimizer {
*/
public void optimize();
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
/** Assuming that all optimizer will store their data in a population
* we will allow access to this population to query to current state
* of the optimizer.
* @return The population of current solutions to a given problem.
*/
public Population getPopulation();
public void setPopulation(Population pop);
/** This method allows you to set an identifier for the algorithm
* @param name The indenifier
/**
* Return all found solutions (local optima) if they are not contained in the last population.
*
* @return A population of found solutions or null if they are contained in the population.
*/
public Population getAllSolutions();
/**
* This method allows you to set an identifier for the algorithm
* @param name The identifier
*/
public void SetIdentifier(String name);
public String getIdentifier();

4
src/javaeva/server/go/strategies/IslandModelEA.java
View File

@ -518,6 +518,10 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
return "(Defunct)";
}
public Population getAllSolutions() {
return getPopulation();
}
/** This method allows you to set the number of processors in local mode
* @param n Number of processors.
*/

3
src/javaeva/server/go/strategies/MemeticAlgorithm.java
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@ -236,6 +236,9 @@ public class MemeticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
/** Choose the global optimization strategy to use
* @param m_GlobalOptimizer
*/

4
src/javaeva/server/go/strategies/MonteCarloSearch.java
View File

@ -199,4 +199,8 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
public String populationTipText() {
return "Change the number of best individuals stored.";
}
public Population getAllSolutions() {
return getPopulation();
}
}

4
src/javaeva/server/go/strategies/MultiObjectiveEA.java
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@ -227,6 +227,10 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
return "Edit the properties of the Population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
/** This method allows you to set/get the optimizing technique to use.
* @return The current optimizing method
*/

3
src/javaeva/server/go/strategies/MultiObjectiveMemeticAlgorithmII.java
View File

@ -163,6 +163,9 @@ public class MultiObjectiveMemeticAlgorithmII implements InterfaceOptimizer, jav
return "Edit the properties of the Population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
/** This method allows you to set/get the optimizing technique to use.
* @return The current optimizing method
*/

3
src/javaeva/server/go/strategies/ParticleFilterOptimization.java
View File

@ -258,6 +258,9 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
/** This method will set the selection method that is to be used
* @param selection
*/

28
src/javaeva/server/go/strategies/ParticleSwarmOptimization.java
View File

@ -7,6 +7,7 @@ import wsi.ra.chart2d.DPoint;
import wsi.ra.chart2d.DPointSet;
import wsi.ra.math.Jama.Matrix;
import javaeva.gui.BeanInspector;
import javaeva.gui.GenericObjectEditor;
import javaeva.gui.TopoPlot;
import javaeva.server.go.InterfacePopulationChangedEventListener;
@ -97,6 +98,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
private int emaPeriods = 0;
// for debugging only
transient private static boolean TRACE = false;
transient protected boolean m_Show = false;
transient protected javaeva.gui.Plot m_Plot;
@ -124,7 +126,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
this.m_SpeedLimit = a.m_SpeedLimit;
this.m_Phi1 = a.m_Phi1;
this.m_Phi2 = a.m_Phi2;
this.m_InertnessOrChi = a.m_InertnessOrChi;
this.m_InertnessOrChi = a.m_InertnessOrChi;
this.m_TopologyRange = a.m_TopologyRange;
//this.setCheckSpeedLimit(a.isCheckSpeedLimit());
}
@ -430,6 +432,9 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
neighbourPos = ((InterfaceESIndividual)neighbourIndy).getDGenotype();
}
if (neighbourFit == null || attractorFit == null) {
System.err.println("error!");
}
// if (fit[0] >= tmpFit[0]) { // now multi-obj.
if (AbstractEAIndividual.isDominatingFitness(neighbourFit, attractorFit)) { // if the remembered fitness dominates the given one, overwrite the given one
// replace best fitness and position with current best
@ -1250,6 +1255,15 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
*
*/
protected void startOptimize() {
if (TRACE) {
for (int i=0; i<m_Population.size(); i++) {
AbstractEAIndividual indy = m_Population.getEAIndividual(i);
System.out.println(BeanInspector.toString(indy.getData(partTypeKey)));
System.out.println(BeanInspector.toString(indy.getData(partBestPosKey)));
System.out.println(BeanInspector.toString(indy.getData(partBestFitKey)));
System.out.println(BeanInspector.toString(indy.getData(partVelKey)));
}
}
if (this.m_Show) this.show();
sortedPop = null;// to make sure that the last sorted version is not reused
}
@ -1509,11 +1523,23 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
}
public void setPopulation(Population pop){
this.m_Population = pop;
for (int i=0; i<pop.size(); i++) {
AbstractEAIndividual indy = pop.getEAIndividual(i);
if (indy.getData(partTypeKey) == null) {
initIndividualDefaults(indy);
initIndividualMemory(indy);
indy.SetData(indexKey, i);
if (TRACE) System.err.println("init indy " + i + " " + AbstractEAIndividual.getDefaultDataString(indy));
}
}
}
public String populationTipText() {
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
/** This method will set the initial velocity
* @param f
*/

3
src/javaeva/server/go/strategies/PopulationBasedIncrementalLearning.java
View File

@ -196,6 +196,9 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
return "Edit the properties of the PBIL population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
// /** This method will set the normation method that is to be used.
// * @param normation
// */

3
src/javaeva/server/go/strategies/SimulatedAnnealing.java
View File

@ -238,6 +238,9 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
return "Change the number of best individuals stored (MS-SA)).";
}
public Population getAllSolutions() {
return getPopulation();
}
/** Set the initial temperature
* @return The initial temperature.
*/

3
src/javaeva/server/go/strategies/SteadyStateGA.java
View File

@ -191,6 +191,9 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
return "Edit the properties of the population used.";
}
public Population getAllSolutions() {
return getPopulation();
}
/** This method will set the parent selection method that is to be used
* @param selection
*/

3
src/javaeva/server/go/strategies/ThresholdAlgorithm.java
View File

@ -230,6 +230,9 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
return "Change the number of best individuals stored (MS-TA).";
}
public Population getAllSolutions() {
return getPopulation();
}
/** Set the initial threshold
* @return The initial temperature.
*/

33
src/javaeva/server/go/strategies/Tribes.java
View File

@ -1,5 +1,8 @@
package javaeva.server.go.strategies;
import java.util.Iterator;
import java.util.List;
import javaeva.gui.GenericObjectEditor;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.individuals.AbstractEAIndividual;
@ -653,7 +656,35 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
return population;
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
/**
* Return a Population of TribesExplorers (AbstractEAIndividuals) of which some where
* memory particles, thus the returned population is larger than the current population.
*
* @return a population of possible solutions.
*/
public Population getAllSolutions() {
// return population and memories?
Population all = (Population)population.clone();
List<TribesPosition> mems = swarm.collectMem();
for (Iterator<TribesPosition> iterator = mems.iterator(); iterator.hasNext();) {
TribesPosition tp = iterator.next();
all.add(positionToExplorer(tp));
}
//all.addPopulation(pop);
return all;
}
protected TribesExplorer positionToExplorer(TribesPosition pos) {
TribesExplorer tmp = (TribesExplorer)population.get(0);
if (tmp == null) System.err.println("Error in Tribes::positionToExplorer!");
TribesExplorer indy = tmp.clone();
indy.clearPosVel();
indy.SetDoubleDataLamarkian(pos.getPos());
indy.SetFitness(pos.getFitness());
return indy;
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {

11
src/javaeva/server/go/strategies/WingedMultiObjectiveEA.java
View File

@ -14,12 +14,12 @@ import javaeva.server.go.problems.FM0Problem;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.gui.PropertyDoubleArray;
/** The winged MOEA was a nice idea, which didn't really worked out.
/** The winged MOEA was a nice idea, which didn't really work out.
* Here a standard MOEA is assisted by n additional local searchers, each
* optimizing just one objective. The idea was that these local optimizers
* would span the search space and would allow the MOEA to converge faster.
* But in the end the performance of this algorithm strongly depends on the
* optimizaiton problem.
* optimization problem.
* Created by IntelliJ IDEA.
* User: streiche
* Date: 16.02.2005
@ -271,8 +271,8 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
return "EMO-LS";
}
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
/** Assuming that all optimizer will store their data in a population
* we will allow access to this population to query to current state
* of the optimizer.
* @return The population of current solutions to a given problem.
*/
@ -286,6 +286,9 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
return "(Defunct)";
}
public Population getAllSolutions() {
return getPopulation();
}
/** This method allows you to set/get the optimizing technique to use.
* @return The current optimizing method
*/

7
src/javaeva/server/go/strategies/tribes/TribesExplorer.java
View File

@ -121,6 +121,13 @@ public class TribesExplorer extends AbstractEAIndividual implements InterfaceDat
return new TribesExplorer(this);
}
/**
* Resets all positions and velocity.
*/
public void clearPosVel() {
initPositions(position.getMaxDimension());
}
//
// public int generateExplorer(TribesParam pb,
// TribesSwarm swarm, TribesPosition center, double radius,

18
src/javaeva/server/go/strategies/tribes/TribesSwarm.java
View File

@ -1,5 +1,8 @@
package javaeva.server.go.strategies.tribes;
import java.util.ArrayList;
import java.util.List;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.server.go.strategies.Tribes;
@ -195,6 +198,21 @@ public class TribesSwarm implements java.io.Serializable{
return found;
}
/**
* Returns al memory particles as double vectors.
* @return
*/
public List<TribesPosition> collectMem() {
ArrayList<TribesPosition> bestList = new ArrayList<TribesPosition>();
for (int n = 0; n < tribeNb; n++) {
for (int m = 0; m < tribes[n].memoryNb; m++) {
bestList.add(tribes[n].memory[m].getPos());
}
}
return bestList;
}
/**
* This searches for the best memory, and also sets the bestMem member of the swarm.
*

187
src/javaeva/server/modules/AbstractGOParameters.java Normal file
View File

@ -0,0 +1,187 @@
package javaeva.server.modules;
import java.io.Serializable;
import javaeva.gui.BeanInspector;
import javaeva.server.go.InterfaceGOParameters;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.InterfaceTerminator;
import javaeva.server.go.operators.postprocess.InterfacePostProcessParams;
import javaeva.server.go.operators.postprocess.PostProcessParams;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.server.go.strategies.InterfaceOptimizer;
public abstract class AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
protected long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
protected InterfaceOptimizer m_Optimizer;
protected InterfaceOptimizationProblem m_Problem ;
protected InterfaceTerminator m_Terminator;
protected InterfacePostProcessParams m_PostProc = new PostProcessParams(false);
// protected int hcPostProcessSteps = 5000;
transient protected InterfacePopulationChangedEventListener m_Listener;
// private boolean postProcess = false;
// protected double postProcessClusterSigma = 0.05;
protected AbstractGOParameters() {
}
protected AbstractGOParameters(AbstractGOParameters Source) {
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
this.m_PostProc = Source.m_PostProc;
// this.hcPostProcessSteps = Source.hcPostProcessSteps;
}
protected AbstractGOParameters(InterfaceOptimizer opt, InterfaceOptimizationProblem prob, InterfaceTerminator term) {
m_Optimizer = opt;
m_Problem = prob;
m_Terminator = term;
m_PostProc = new PostProcessParams(false);
opt.SetProblem(prob);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
public String toString() {
// String ret = "\r\nParameters:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
// return ret;
StringBuffer sb = new StringBuffer(getName());
sb.append("\n");
sb.append("seed=");
sb.append(m_Seed);
sb.append("\nProblem: ");
sb.append(BeanInspector.toString(m_Problem));
sb.append("\nOptimizer: ");
sb.append(BeanInspector.toString(m_Optimizer));
sb.append("\nTerminator: ");
sb.append(BeanInspector.toString(m_Terminator));
sb.append("\n");
// sb.append(m_N)
return sb.toString();
}
public void setOptimizer(InterfaceOptimizer optimizer) {
this.m_Optimizer = optimizer;
this.m_Optimizer.SetProblem(this.m_Problem);
if (this.m_Listener != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
public String optimizerTipText() {
return "Choose an optimizing strategy.";
}
public String getName() {
return "Optimization parameters";
}
// public String postProcessStepsTipText() {
// return "The number of HC post processing steps in fitness evaluations.";
// }
// public int getPostProcessSteps() {
// return hcPostProcessSteps;
// }
// public void setPostProcessSteps(int ppSteps) {
// hcPostProcessSteps = ppSteps;
// }
/**
* This method will set the problem that is to be optimized.
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed, set to zero to use current system time.";
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
// /**
// * @return the postProcess
// */
// public boolean isPostProcess() {
// return postProcess;
// }
// /**
// * @param postProcess the postProcess to set
// */
// public void setPostProcess(boolean postProcess) {
// this.postProcess = postProcess;
// GenericObjectEditor.setShowProperty(this.getClass(), "postProcessSteps", postProcess);
// GenericObjectEditor.setShowProperty(this.getClass(), "postProcessClusterSigma", postProcess);
// }
// public String postProcessTipText() {
// return "Toggle post processing of the solutions.";
// }
// /**
// * @return the postProcessClusterSigma
// */
// public double getPostProcessClusterSigma() {
// return postProcessClusterSigma;
// }
// /**
// * @param postProcessClusterSigma the postProcessClusterSigma to set
// */
// public void setPostProcessClusterSigma(double postProcessClusterSigma) {
// this.postProcessClusterSigma = postProcessClusterSigma;
// }
// public String postProcessClusterSigmaTipText() {
// return "Set the sigma parameter for clustering during post processing. Set to zero for no clustering.";
// }
public InterfacePostProcessParams getPostProcessParams() {
return m_PostProc;
}
public void setPostProcessParams(InterfacePostProcessParams ppp) {
m_PostProc = ppp;
}
public String postProcessParamsTipText() {
return "Parameters for the post processing step";
}
public void setDoPostProcessing(boolean doPP){
m_PostProc.setDoPostProcessing(doPP);
}
}

116
src/javaeva/server/modules/DEParameters.java
View File

@ -24,19 +24,9 @@ import java.io.Serializable;
* Time: 13:49:09
* To change this template use File | Settings | File Templates.
*/
public class DEParameters implements InterfaceGOParameters, Serializable {
public class DEParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new DifferentialEvolution();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
@ -59,57 +49,17 @@ public class DEParameters implements InterfaceGOParameters, Serializable {
*
*/
public DEParameters() {
if (TRACE) System.out.println("DEParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new DifferentialEvolution();
this.m_Problem = new F1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("DEParameters Constructor end");
super(new DifferentialEvolution(), new F1Problem(), new EvaluationTerminator(1000));
}
/**
*
*/
private DEParameters(DEParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
super(Source);
}
/**
*
*/
public String getName() {
return m_Name;
}
/**
*
*/
public Object clone() {
return new DEParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nDE-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -117,70 +67,12 @@ public class DEParameters implements InterfaceGOParameters, Serializable {
return "This is a Differential Evolution optimization method, please limit DE to real-valued genotypes.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
/** This method will set the output filename
* @param name
*/
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

111
src/javaeva/server/modules/EPParameters.java
View File

@ -22,19 +22,9 @@ import java.io.Serializable;
* Time: 13:49:09
* To change this template use File | Settings | File Templates.
*/
public class EPParameters implements InterfaceGOParameters, Serializable {
public class EPParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new EvolutionaryProgramming();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
@ -57,34 +47,17 @@ public class EPParameters implements InterfaceGOParameters, Serializable {
*
*/
public EPParameters() {
super(new EvolutionaryProgramming(), new F1Problem(), new EvaluationTerminator());
if (TRACE) System.out.println("EPParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new EvolutionaryProgramming();
this.m_Problem = new F1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("EPParameters Constructor end");
}
/**
*
*/
private EPParameters(EPParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
}
/**
*
*/
public String getName() {
return m_Name;
super(Source);
}
/**
*
*/
@ -92,22 +65,6 @@ public class EPParameters implements InterfaceGOParameters, Serializable {
return new EPParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nEP-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -115,70 +72,10 @@ public class EPParameters implements InterfaceGOParameters, Serializable {
return "This is a Evolutionary Programming optimization method, please limit EP to mutation operators only.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

116
src/javaeva/server/modules/GAParameters.java
View File

@ -23,23 +23,10 @@ import java.io.Serializable;
* Time: 21:29:34
* To change this template use File | Settings | File Templates.
*/
public class GAParameters implements InterfaceGOParameters, Serializable {
public class GAParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
*/
public static GAParameters getInstance() {
if (TRACE) System.out.println("GAParameters getInstance 1");
GAParameters Instance = (GAParameters) Serializer.loadObject("GAParameters.ser");
@ -58,57 +45,22 @@ public class GAParameters implements InterfaceGOParameters, Serializable {
*
*/
public GAParameters() {
if (TRACE) System.out.println("GAParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new GeneticAlgorithm();
this.m_Problem = new B1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("GAParameters Constructor end");
super(new GeneticAlgorithm(), new B1Problem(), new EvaluationTerminator());
}
/**
*
*/
private GAParameters(GAParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
}
/**
*
*/
public String getName() {
return m_Name;
super(Source);
}
/**
*
*/
public Object clone() {
return new GAParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nGO-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -116,70 +68,10 @@ public class GAParameters implements InterfaceGOParameters, Serializable {
return "This is a Genetic Algorithm, which transforms into a GP or GE if the appropriate problem and genotype is used.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

149
src/javaeva/server/modules/GOParameters.java
View File

@ -1,21 +1,15 @@
package javaeva.server.modules;
import java.io.Serializable;
import javaeva.gui.BeanInspector;
import javaeva.server.go.InterfaceGOParameters;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.InterfaceTerminator;
import javaeva.server.go.operators.terminators.EvaluationTerminator;
import javaeva.server.go.problems.B1Problem;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.server.go.strategies.GeneticAlgorithm;
import javaeva.server.go.strategies.HillClimbing;
import javaeva.server.go.strategies.InterfaceOptimizer;
import javaeva.tools.Serializer;
import java.io.Serializable;
import java.io.BufferedWriter;
import java.util.ArrayList;
/**
* Created by IntelliJ IDEA.
* Copyright: Copyright (c) 2003
@ -25,19 +19,9 @@ import java.util.ArrayList;
* $Date: 2007-12-04 14:22:52 +0100 (Tue, 04 Dec 2007) $
* $Author: mkron $
*/
public class GOParameters implements InterfaceGOParameters, Serializable {
public class GOParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new GeneticAlgorithm();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
@ -50,22 +34,6 @@ public class GOParameters implements InterfaceGOParameters, Serializable {
return Instance;
}
public String toString() {
StringBuffer sb = new StringBuffer(m_Name);
sb.append("\n");
sb.append("seed=");
sb.append(m_Seed);
sb.append("\nProblem: ");
sb.append(BeanInspector.toString(m_Problem));
sb.append("\nOptimizer: ");
sb.append(BeanInspector.toString(m_Optimizer));
sb.append("\nTerminator: ");
sb.append(BeanInspector.toString(m_Terminator));
sb.append("\n");
// sb.append(m_N)
return sb.toString();
}
/**
*
*/
@ -76,33 +44,20 @@ public class GOParameters implements InterfaceGOParameters, Serializable {
*
*/
public GOParameters() {
if (TRACE) System.out.println("GOParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new GeneticAlgorithm();
this.m_Problem = new B1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("GOParameters Constructor end");
super(new GeneticAlgorithm(), new B1Problem(), new EvaluationTerminator(1000));
}
/**
*
*/
private GOParameters(GOParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
super(Source);
}
/**
*
*/
public String getName() {
return m_Name;
return "Optimization parameters";
}
/**
*
@ -110,102 +65,10 @@ public class GOParameters implements InterfaceGOParameters, Serializable {
public Object clone() {
return new GOParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
// /**
// *
// */
// public String toString() {
// String ret = "";//"\r\nGO-Parameter:"+this.m_Problem.getStringRepresentationForProblem()+"\n"+this.m_Optimizer.getStringRepresentation();
// return ret;
// }
/** This method returns a global info string
* @return description
*/
public String globalInfo() {
return "Select the optimization parameters.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed, set to zero to use current system time.";
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
// /** This method allows you to set the current optimizing algorithm
// * @param optimizer The new optimizing algorithm
// */
// public void SetOptimizer(InterfaceOptimizer optimizer) {
// this.m_Optimizer = optimizer;
// this.m_Optimizer.SetProblem(this.m_Problem);
// if (this.m_Listener != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
// }
public void setOptimizer(InterfaceOptimizer optimizer) {
this.m_Optimizer = optimizer;
this.m_Optimizer.SetProblem(this.m_Problem);
if (this.m_Listener != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
public String optimizerTipText() {
return "Choose an optimizing strategy.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
}

121
src/javaeva/server/modules/HCParameters.java
View File

@ -1,5 +1,7 @@
package javaeva.server.modules;
import java.io.Serializable;
import javaeva.server.go.InterfaceGOParameters;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.InterfaceTerminator;
@ -7,14 +9,10 @@ import javaeva.server.go.operators.terminators.EvaluationTerminator;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.B1Problem;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.server.go.strategies.GeneticAlgorithm;
import javaeva.server.go.strategies.HillClimbing;
import javaeva.server.go.strategies.InterfaceOptimizer;
import javaeva.server.go.strategies.MonteCarloSearch;
import javaeva.tools.Serializer;
import java.io.Serializable;
/** The class gives access to all HC parameters for the JavaEvA
* top level GUI.
* Created by IntelliJ IDEA.
@ -23,20 +21,7 @@ import java.io.Serializable;
* Time: 21:19:20
* To change this template use File | Settings | File Templates.
*/
public class HCParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new HillClimbing();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
public class HCParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
/**
*
*/
@ -58,33 +43,14 @@ public class HCParameters implements InterfaceGOParameters, Serializable {
*
*/
public HCParameters() {
if (TRACE) System.out.println("HCParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new HillClimbing();
this.m_Problem = new B1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("HCParameters Constructor end");
super(new HillClimbing(), new B1Problem(), new EvaluationTerminator());
}
/**
*
*/
private HCParameters(HCParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
}
/**
*
*/
public String getName() {
return m_Name;
super(Source);
}
/**
*
@ -92,23 +58,6 @@ public class HCParameters implements InterfaceGOParameters, Serializable {
public Object clone() {
return new HCParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nGO-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -116,70 +65,10 @@ public class HCParameters implements InterfaceGOParameters, Serializable {
return "This is a Hill-Climber, use a population size > 1 for a Multi-Start Hill-Climber.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

116
src/javaeva/server/modules/MCParameters.java
View File

@ -22,19 +22,8 @@ import java.io.Serializable;
* Time: 21:07:40
* To change this template use File | Settings | File Templates.
*/
public class MCParameters implements InterfaceGOParameters, Serializable {
public class MCParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new MonteCarloSearch();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
@ -57,34 +46,13 @@ public class MCParameters implements InterfaceGOParameters, Serializable {
*
*/
public MCParameters() {
if (TRACE) System.out.println("MCParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new MonteCarloSearch();
this.m_Problem = new B1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("GOParameters Constructor end");
super(new MonteCarloSearch(), new B1Problem(), new EvaluationTerminator());
}
/**
*
*/
private MCParameters(MCParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
}
/**
*
*/
public String getName() {
return m_Name;
super(Source);
}
/**
*
*/
@ -92,22 +60,6 @@ public class MCParameters implements InterfaceGOParameters, Serializable {
return new MCParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nGO-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -115,70 +67,10 @@ public class MCParameters implements InterfaceGOParameters, Serializable {
return "This is a simple Monte-Carlo Search, please use big populations sizes for faster drawing.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

125
src/javaeva/server/modules/MOEAParameters.java
View File

@ -26,23 +26,10 @@ import java.io.Serializable;
* Time: 13:49:09
* To change this template use File | Settings | File Templates.
*/
public class MOEAParameters implements InterfaceGOParameters, Serializable {
public class MOEAParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new MultiObjectiveEA();
private InterfaceOptimizationProblem m_Problem = new TF1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
*/
public static MOEAParameters getInstance() {
if (TRACE) System.out.println("MOEAParameters getInstance 1");
MOEAParameters Instance = (MOEAParameters) Serializer.loadObject("MOEAParameters.ser");
@ -51,67 +38,22 @@ public class MOEAParameters implements InterfaceGOParameters, Serializable {
return Instance;
}
/**
*
*/
public void saveInstance() {
Serializer.storeObject("MOEAParameters.ser",this);
}
/**
*
*/
public MOEAParameters() {
if (TRACE) System.out.println("MOEAParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new MultiObjectiveEA();
this.m_Problem = new TF1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("MOEAParameters Constructor end");
super(new MultiObjectiveEA(), new TF1Problem(), new EvaluationTerminator(1000));
}
/**
*
*/
private MOEAParameters(MOEAParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
super(Source);
}
/**
*
*/
public String getName() {
return m_Name;
}
/**
*
*/
public Object clone() {
return new MOEAParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nMOEA-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -119,60 +61,6 @@ public class MOEAParameters implements InterfaceGOParameters, Serializable {
return "This is a multi-objective evoluationary algorithm, please limit MOEA to multi-objective problems (due to the given framework only the fitness of objective one will be plotted).";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
((MultiObjectiveEA)this.m_Optimizer).SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state
* of the optimizer.
@ -194,9 +82,6 @@ public class MOEAParameters implements InterfaceGOParameters, Serializable {
public InterfaceOptimizer getOptimizer() {
return ((MultiObjectiveEA)this.m_Optimizer).getOptimizer();
}
// public void SetOptimizer(InterfaceOptimizer b){
// // i'm a MOEA i'll ignore that
// }
public void setOptimizer(InterfaceOptimizer b){
((MultiObjectiveEA)this.m_Optimizer).setOptimizer(b);
}

134
src/javaeva/server/modules/PBILParameters.java
View File

@ -1,20 +1,17 @@
package javaeva.server.modules;
import java.io.Serializable;
import javaeva.server.go.InterfaceGOParameters;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.InterfaceTerminator;
import javaeva.server.go.operators.selection.InterfaceSelection;
import javaeva.server.go.operators.terminators.EvaluationTerminator;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.B1Problem;
import javaeva.server.go.problems.InterfaceOptimizationProblem;
import javaeva.server.go.strategies.InterfaceOptimizer;
import javaeva.server.go.strategies.MonteCarloSearch;
import javaeva.server.go.strategies.PopulationBasedIncrementalLearning;
import javaeva.tools.Serializer;
import java.io.Serializable;
/** The class gives access to all PBIL parameters for the JavaEvA
* top level GUI.
* Created by IntelliJ IDEA.
@ -23,23 +20,10 @@ import java.io.Serializable;
* Time: 21:53:29
* To change this template use File | Settings | File Templates.
*/
public class PBILParameters implements InterfaceGOParameters, Serializable {
public class PBILParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new PopulationBasedIncrementalLearning();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
*/
public static PBILParameters getInstance() {
if (TRACE) System.out.println("PBILParameters getInstance 1");
PBILParameters Instance = (PBILParameters) Serializer.loadObject("PBILParameters.ser");
@ -48,67 +32,21 @@ public class PBILParameters implements InterfaceGOParameters, Serializable {
return Instance;
}
/**
*
*/
public void saveInstance() {
Serializer.storeObject("PBILParameters.ser",this);
}
/**
*
*/
public PBILParameters() {
if (TRACE) System.out.println("PBILParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new PopulationBasedIncrementalLearning();
this.m_Problem = new B1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("PBILParameters Constructor end");
super(new PopulationBasedIncrementalLearning(), new B1Problem(), new EvaluationTerminator(1000));
}
/**
*
*/
private PBILParameters(PBILParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
super(Source);
}
/**
*
*/
public String getName() {
return m_Name;
}
/**
*
*/
public Object clone() {
return new PBILParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nGO-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -116,70 +54,10 @@ public class PBILParameters implements InterfaceGOParameters, Serializable {
return ((PopulationBasedIncrementalLearning)this.m_Optimizer).globalInfo();
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

119
src/javaeva/server/modules/PSOParameters.java
View File

@ -26,19 +26,9 @@ import java.io.Serializable;
* Time: 17:58:37
* To change this template use File | Settings | File Templates.
*/
public class PSOParameters implements InterfaceGOParameters, Serializable {
public class PSOParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new ParticleSwarmOptimization();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
@ -61,57 +51,17 @@ public class PSOParameters implements InterfaceGOParameters, Serializable {
*
*/
public PSOParameters() {
if (TRACE) System.out.println("PSOParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new ParticleSwarmOptimization();
this.m_Problem = new F1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("PSOParameters Constructor end");
super(new ParticleSwarmOptimization(), new F1Problem(), new EvaluationTerminator());
}
/**
*
*/
private PSOParameters(PSOParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
super(Source);
}
/**
*
*/
public String getName() {
return m_Name;
}
/**
*
*/
public Object clone() {
return new PSOParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nGO-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -128,70 +78,9 @@ public class PSOParameters implements InterfaceGOParameters, Serializable {
setTopology(getTopology());
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed, set to zero to use current time";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

59
src/javaeva/server/modules/Processor.java
View File

@ -5,23 +5,27 @@ import javaeva.server.go.InterfaceGOParameters;
import javaeva.server.go.InterfacePopulationChangedEventListener;
import javaeva.server.go.InterfaceProcessor;
import javaeva.server.go.PopulationInterface;
import javaeva.server.go.operators.postprocess.PostProcess;
import javaeva.server.go.operators.terminators.EvaluationTerminator;
import javaeva.server.go.populations.Population;
import javaeva.server.go.problems.InterfaceMultimodalProblem;
import javaeva.server.go.problems.AbstractOptimizationProblem;
import javaeva.server.go.tools.RandomNumberGenerator;
import javaeva.server.stat.InterfaceStatistics;
import javaeva.server.stat.InterfaceTextListener;
import wsi.ra.jproxy.RemoteStateListener;
public class Processor extends Thread implements InterfaceProcessor, InterfacePopulationChangedEventListener {
public static final boolean TRACE=false;
private static final boolean TRACE=false;
private volatile boolean m_optRunning;
public volatile boolean m_doRunScript;
public InterfaceStatistics m_Statistics;
public InterfaceGOParameters goParams;
public boolean m_createInitialPopulations=true;
// private volatile boolean m_doRunScript;
private InterfaceStatistics m_Statistics;
private InterfaceGOParameters goParams;
private boolean m_createInitialPopulations=true;
private boolean saveParams = true;
private RemoteStateListener m_ListenerModule;
private boolean wasRestarted = false;
// private int postProcessSteps = 0;
private int runCounter = 0;
// transient private String m_OutputPath = "";
@ -55,6 +59,15 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
m_optRunning = bRun;
}
/**
* If set to true, before every run the parameters will be stored to a file.
*
* @param doSave
*/
public void setSaveParams(boolean doSave) {
saveParams = doSave;
}
// public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
// listener = ea;
//// hier weiter! TODO
@ -94,16 +107,16 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
public void stopOpt() { // this means user break
if (TRACE) System.out.println("called StopOpt");
setOptRunning(false);
m_doRunScript = false;
// m_doRunScript = false;
if (TRACE) System.out.println("m_doRunScript = false ");
}
/**
*
*/
public void runScript() {
m_doRunScript = true;
}
// /**
// *
// */
// public void runScript() {
// m_doRunScript = true;
// }
/**
*
@ -124,12 +137,11 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
try {
while (isOptRunning()) {
setPriority(3);
goParams.saveInstance();
if (saveParams) goParams.saveInstance();
optimize("Run");
setPriority(1);
}
} catch (Exception e) {
// TODO Das hier so machen, daß trotz Exception eine neue optimierung ohne Neustart möglich ist!
System.err.println("Caught exception in Processor: "+e.getMessage());
e.printStackTrace();
//m_Statistics.stopOptPerformed(false);
@ -202,12 +214,19 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
} while (isOptRunning() && !this.goParams.getTerminator().isTerminated(this.goParams.getOptimizer().getPopulation()));
runCounter++;
if (goParams.getProblem() instanceof InterfaceMultimodalProblem) {
// TODO improve this?
InterfaceMultimodalProblem mmProb = (InterfaceMultimodalProblem)goParams.getProblem();
System.out.println("no optima found: " + mmProb.getNumberOfFoundOptima(goParams.getOptimizer().getPopulation()));
}
//////////////// Default stats
m_Statistics.stopOptPerformed(isOptRunning()); // stop is "normal" if opt wasnt set false by the user
//////////////// PP
PostProcess.postProcess(goParams.getPostProcessParams(), goParams.getOptimizer().getAllSolutions(), (AbstractOptimizationProblem)goParams.getProblem(), (InterfaceTextListener)m_Statistics);
// moved to PostProcess
// if ((goParams.getProblem() instanceof InterfaceMultimodalProblem)) {
// InterfaceMultimodalProblem mmProb = (InterfaceMultimodalProblem)goParams.getProblem();
// PostProcessInterim.outputResult((AbstractOptimizationProblem)mmProb, goParams.getOptimizer().getAllSolutions(), 0.01, System.out, 0, 2000, 20, goParams.getPostProcessSteps());
//// PostProcessInterim.postProcess(mmProb, goParams.getOptimizer().getPopulation(), 0.01, System.out, 0, 2000, 20);
// if ((goParams.getProblem() instanceof InterfaceMultimodalProblemKnown)) {
// PostProcessInterim.outputResultKnown((InterfaceMultimodalProblemKnown)goParams.getProblem(), goParams.getOptimizer().getAllSolutions(), 0.01, System.out, 0., 2000., 20);
// }
// }
}
setOptRunning(false); // normal finish
if (m_ListenerModule!=null) m_ListenerModule.performedStop(); // is only needed in client server mode

110
src/javaeva/server/modules/SAParameters.java
View File

@ -23,11 +23,9 @@ import java.io.Serializable;
* Time: 21:25:12
* To change this template use File | Settings | File Templates.
*/
public class SAParameters implements InterfaceGOParameters, Serializable {
public class SAParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new SimulatedAnnealing();
@ -58,57 +56,16 @@ public class SAParameters implements InterfaceGOParameters, Serializable {
*
*/
public SAParameters() {
if (TRACE) System.out.println("SAParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new SimulatedAnnealing();
this.m_Problem = new B1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("SAParameters Constructor end");
super(new SimulatedAnnealing(),new B1Problem(),new EvaluationTerminator());
}
/**
*
*/
private SAParameters(SAParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
super(Source);
}
/**
*
*/
public String getName() {
return m_Name;
}
/**
*
*/
public Object clone() {
return new SAParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nGO-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -116,70 +73,9 @@ public class SAParameters implements InterfaceGOParameters, Serializable {
return "This is a simple Simulated Annealing Algorithm.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

122
src/javaeva/server/modules/SSGAParameters.java
View File

@ -23,19 +23,9 @@ import java.io.Serializable;
* Time: 15:44:34
* To change this template use File | Settings | File Templates.
*/
public class SSGAParameters implements InterfaceGOParameters, Serializable {
public class SSGAParameters extends AbstractGOParameters implements InterfaceGOParameters, Serializable {
public static boolean TRACE = false;
private String m_Name ="not defined";
private long m_Seed = (long)100.0;
// Opt. Algorithms and Parameters
private InterfaceOptimizer m_Optimizer = new SteadyStateGA();
private InterfaceOptimizationProblem m_Problem = new B1Problem();
//private int m_FunctionCalls = 1000;
private InterfaceTerminator m_Terminator = new EvaluationTerminator();
// private String m_OutputFileName = "none";
transient private InterfacePopulationChangedEventListener m_Listener;
/**
*
@ -58,57 +48,16 @@ public class SSGAParameters implements InterfaceGOParameters, Serializable {
*
*/
public SSGAParameters() {
if (TRACE) System.out.println("SSGAParameters Constructor start");
this.m_Name="Optimization parameters";
this.m_Optimizer = new SteadyStateGA();
this.m_Problem = new B1Problem();
//this.m_FunctionCalls = 1000;
((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
this.m_Optimizer.SetProblem(this.m_Problem);
if (TRACE) System.out.println("SSGAParameters Constructor end");
super(new SteadyStateGA(), new B1Problem(), new EvaluationTerminator());
}
private SSGAParameters(SSGAParameters Source) {
super(Source);
}
/**
*
*/
private SSGAParameters(SSGAParameters Source) {
this.m_Name = Source.m_Name;
this.m_Optimizer = Source.m_Optimizer;
this.m_Problem = Source.m_Problem;
this.m_Terminator = Source.m_Terminator;
//this.m_FunctionCalls = Source.m_FunctionCalls;
this.m_Optimizer.SetProblem(this.m_Problem);
this.m_Seed = Source.m_Seed;
}
/**
*
*/
public String getName() {
return m_Name;
}
/**
*
*/
public Object clone() {
return new SSGAParameters(this);
}
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
*/
public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
this.m_Listener = ea;
if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
}
/**
*
*/
public String toString() {
String ret = "\r\nGO-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
return ret;
}
/** This method returns a global info string
* @return description
*/
@ -116,70 +65,9 @@ public class SSGAParameters implements InterfaceGOParameters, Serializable {
return "This is a steady-state GA.";
}
/** This methods allow you to set and get the Seed for the Random Number Generator.
* @param x Long seed.
*/
public void setSeed(long x) {
m_Seed = x;
}
public long getSeed() {
return m_Seed;
}
public String seedTipText() {
return "Random number seed.";
}
/** This method allows you to set the current optimizing algorithm
* @param optimizer The new optimizing algorithm
*/
public void setOptimizer(InterfaceOptimizer optimizer) {
// i'm a Monte Carlo Search Algorithm
// *pff* i'll ignore that!
}
public InterfaceOptimizer getOptimizer() {
return this.m_Optimizer;
}
/** This method allows you to choose a termination criteria for the
* evolutionary algorithm.
* @param term The new terminator
*/
public void setTerminator(InterfaceTerminator term) {
this.m_Terminator = term;
}
public InterfaceTerminator getTerminator() {
return this.m_Terminator;
}
public String terminatorTipText() {
return "Choose a termination criterion.";
}
/** This method will set the problem that is to be optimized
* @param problem
*/
public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem);
}
public InterfaceOptimizationProblem getProblem() {
return this.m_Problem;
}
public String problemTipText() {
return "Choose the problem that is to optimize and the EA individual parameters.";
}
// /** This method will set the output filename
// * @param name
// */
// public void setOutputFileName (String name) {
// this.m_OutputFileName = name;
// }
// public String getOutputFileName () {
// return this.m_OutputFileName;
// }
// public String outputFileNameTipText() {
// return "Set the name for the output file, if 'none' no output file will be created.";
// }
/** Assuming that all optimizer will store thier data in a population
* we will allow acess to this population to query to current state

20
src/javaeva/server/stat/AbstractStatistics.java
View File

@ -14,7 +14,7 @@ import javaeva.server.go.individuals.AbstractEAIndividual;
import javaeva.server.go.problems.InterfaceAdditionalPopulationInformer;
import wsi.ra.tool.StatisticUtils;
public abstract class AbstractStatistics implements InterfaceStatistics {
public abstract class AbstractStatistics implements InterfaceTextListener, InterfaceStatistics {
private PrintWriter resultOut;
public final static boolean TRACE = false;
protected StatisticsParameter m_StatisticsParameter;
@ -133,6 +133,16 @@ public abstract class AbstractStatistics implements InterfaceStatistics {
}
}
////////////// InterfaceTextListener
public void print(String str) {
printToTextListener(str);
}
////////////// InterfaceTextListener
public void println(String str) {
printToTextListener(str);
printToTextListener("\n");
}
public StatisticsParameter getStatisticsParameter() {
return m_StatisticsParameter;
}
@ -218,6 +228,14 @@ public abstract class AbstractStatistics implements InterfaceStatistics {
if (doTextOutput()) printToTextListener(getOutputHeader(informer, pop)+'\n');
firstPlot = false;
}
// System.out.println("pop fcalls: " + pop.getFunctionCalls());
// if ((pop.getFunctionCalls() % 100) != 0 ) {
// System.err.println("error: pop fcalls: " + pop.getFunctionCalls());
// double [] bla= new double[1];
// bla[10]=0;
// //TODO
//
// }
if (pop.getSpecificData() != null) {
plotSpecificData(pop, informer);
return;

30
src/javaeva/server/stat/InterfaceStatistics.java
View File

@ -16,20 +16,26 @@ import javaeva.server.go.IndividualInterface;
import javaeva.server.go.PopulationInterface;
import javaeva.server.go.problems.InterfaceAdditionalPopulationInformer;
/*==========================================================================*
* INTERFACE DECLARATION
*==========================================================================*/
* INTERFACE DECLARATION
*==========================================================================*/
/**
*
*/
public interface InterfaceStatistics {
public void startOptPerformed(String InfoString,int runnumber); // called from processor
public void stopOptPerformed(boolean normal); // called from processor
public void addTextListener(InterfaceTextListener listener);
public boolean removeTextListener(InterfaceTextListener listener);
public void printToTextListener(String s);
public void createNextGenerationPerformed(PopulationInterface Pop, InterfaceAdditionalPopulationInformer informer);
public void createNextGenerationPerformed(double[] bestfit,double[] worstfit,int calls);
public StatisticsParameter getStatisticsParameter(); // called from moduleadapter
public IndividualInterface getBestSolution(); // returns the best overall solution
public double[] getBestFitness(); // returns the best overall fitness
/**
* Initialize statistics computations.
*/
public void startOptPerformed(String InfoString,int runnumber); // called from processor
/**
* Finalize statistics computations.
*/
public void stopOptPerformed(boolean normal); // called from processor
public void addTextListener(InterfaceTextListener listener);
public boolean removeTextListener(InterfaceTextListener listener);
public void printToTextListener(String s);
public void createNextGenerationPerformed(PopulationInterface Pop, InterfaceAdditionalPopulationInformer informer);
public void createNextGenerationPerformed(double[] bestfit,double[] worstfit,int calls);
public StatisticsParameter getStatisticsParameter(); // called from moduleadapter
public IndividualInterface getBestSolution(); // returns the best overall solution
public double[] getBestFitness(); // returns the best overall fitness
}

1
src/javaeva/server/stat/InterfaceTextListener.java
View File

@ -2,4 +2,5 @@ package javaeva.server.stat;
public interface InterfaceTextListener {
public void print(String str);
public void println(String str);
}

3
src/javaeva/server/stat/StatisticsParameterImpl.java
View File

@ -186,7 +186,8 @@ public class StatisticsParameterImpl implements StatisticsParameter, Serializabl
*
*/
public void setResultFileName(String x) {
m_ResultFileName = x;
if (x==null) m_ResultFileName = "";
else m_ResultFileName = x;
}
/**

7
src/javaeva/server/stat/StatisticsStandalone.java
View File

@ -110,10 +110,6 @@ public class StatisticsStandalone extends AbstractStatistics implements Interfac
((ArrayList[]) m_Result.get(i))[optRunsPerformed] = new ArrayList<double[]>();
}
/**
*
*/
public void startOptPerformed(String infoString, int runNumber) {
super.startOptPerformed(infoString, runNumber);
if (runNumber == 0) {
@ -127,9 +123,6 @@ public class StatisticsStandalone extends AbstractStatistics implements Interfac
m_InfoString = infoString;
}
/**
*
*/
public void stopOptPerformed(boolean normal) {
super.stopOptPerformed(normal);

33
src/javaeva/tools/Pair.java Normal file
View File

@ -0,0 +1,33 @@
package javaeva.tools;
/**
* Simple pair structure of two types, Scheme style, but typed.
*
* @author mkron
*
*/
public class Pair<S, T> {
public S head;
public T tail;
public Pair(S head, T tail) {
this.head = head;
this.tail = tail;
}
public S car() {
return head;
}
public T cdr() {
return tail;
}
public S head() {
return head;
}
public T tail() {
return tail;
}
}

40
src/wsi/ra/chart2d/DArea.java
View File

@ -12,19 +12,23 @@
package wsi.ra.chart2d;
import java.awt.Color;
import java.awt.Dimension;
import java.awt.FontMetrics;
import java.awt.Graphics;
import java.awt.Point;
import java.awt.print.PageFormat;
import java.awt.print.Printable;
import javax.swing.JComponent;
import javax.swing.border.Border;
import wsi.ra.print.PagePrinter;
/*==========================================================================*
* IMPORTS
*==========================================================================*/
import java.awt.*;
import java.awt.image.BufferedImage;
import javax.swing.*;
import javax.swing.border.Border;
import java.util.Vector;
// zum Drucken:
import java.awt.print.*;
import wsi.ra.print.PagePrinter;
/*==========================================================================*
* CLASS DECLARATION
@ -37,7 +41,7 @@ import wsi.ra.print.PagePrinter;
*/
public class DArea extends JComponent implements DParent, Printable
{
private static final boolean under_construction = false;
private static final boolean TRACE = false;
/**
* the default minimal rectangle which is shown
@ -157,7 +161,7 @@ public class DArea extends JComponent implements DParent, Printable
* @param rect the visible <code>DRectangle</code> in DArea coordinates
*/
public void setVisibleRectangle( DRectangle rect ){
if( under_construction )System.out.println("DArea.setVisibleRectangle(DRectangle)");
if( TRACE )System.out.println("DArea.setVisibleRectangle(DRectangle)");
if( rect.isEmpty() ) throw
new IllegalArgumentException(
"You shopuld never try to set an empty rectangle\n"
@ -380,7 +384,7 @@ public class DArea extends JComponent implements DParent, Printable
* @param g the java.awt.Graphics object
*/
public void paint( Graphics g ){
if( under_construction ) System.out.println("DArea.paint(Graphics)");
if( TRACE ) System.out.println("DArea.paint(Graphics)");
if( auto_focus ) {
container.restore();
visible_rect = (DRectangle)container.getRectangle().clone();
@ -400,7 +404,7 @@ public class DArea extends JComponent implements DParent, Printable
* @param r the rectangle to repaint
*/
public void repaint( DRectangle r ){
if( under_construction ) System.out.println("DArea.repaint(DRectangle)"+r);
if( TRACE ) System.out.println("DArea.repaint(DRectangle)"+r);
if( r == null ) throw
new IllegalArgumentException("Cannot repaint a null DRectangle");
if( r.isAll() || auto_focus ) repaint();
@ -471,7 +475,7 @@ public class DArea extends JComponent implements DParent, Printable
* @param aFlag visible or not
*/
public void setGridVisible( boolean aFlag ){
if( under_construction ) System.out.println("DArea.setGridVisisble: "+aFlag);
if( TRACE ) System.out.println("DArea.setGridVisisble: "+aFlag);
grid.rectangle = getDRectangle();
grid.setVisible( aFlag );
}
@ -572,7 +576,7 @@ public class DArea extends JComponent implements DParent, Printable
* @return int @see java.awt.print.Printable
*/
public int print( Graphics g, PageFormat pf, int pi ){
if( under_construction ) System.out.println("DArea.print(...)");
if( TRACE ) System.out.println("DArea.print(...)");
if( pi > 0 ) return Printable.NO_SUCH_PAGE;
Border sb = getBorder();
@ -644,7 +648,7 @@ public class DArea extends JComponent implements DParent, Printable
public void restoreBorder(){
dborder = container.getDBorder();
if( under_construction ) System.out.println("DArea.restoreBorder -> "+dborder);
if( TRACE ) System.out.println("DArea.restoreBorder -> "+dborder);
}
/**
@ -653,7 +657,7 @@ public class DArea extends JComponent implements DParent, Printable
* <code>ScaledBorder</code> or not and on the auto_grid option
*/
private void paintGrid( DMeasures m ){
if( under_construction ) System.out.println("DArea.paintGrid(DMeasures)");
if( TRACE ) System.out.println("DArea.paintGrid(DMeasures)");
grid.rectangle = getDRectangle();
if( auto_grid ){
Border b = getBorder();
@ -679,7 +683,7 @@ public class DArea extends JComponent implements DParent, Printable
* @param m the measures of the area
*/
private void paintGrid(ScaledBorder sb, DMeasures m){
if( under_construction ) System.out.println("DArea.paintGrid(ScaledBorder, DMeasures)");
if( TRACE ) System.out.println("DArea.paintGrid(ScaledBorder, DMeasures)");
Dimension d = getSize();
FontMetrics fm = m.getGraphics().getFontMetrics();
grid.hor_dist = sb.getSrcdX(fm, d);

11
src/wsi/ra/chart2d/DPointSet.java
View File

@ -214,6 +214,15 @@ public class DPointSet extends DComponent
return stroke;
}
/**
* Return the minimum y value in the point set.
*
* @return the minimum y value in the point set
*/
public double getMinYVal() {
return y.getMinImageValue();
}
public void setConnected( boolean aFlag ){
boolean changed = !( aFlag == connected );
connected = aFlag;
@ -274,7 +283,7 @@ public class DPointSet extends DComponent
// if( size != y.getSize() ) throw
// new ArrayStoreException(
// "The number of x-values is not equal to the number of y-values.\n"
// +"The size of the DPointSet isn´t clear."
// +"The size of the DPointSet isn<EFBFBD>t clear."
// );
// return size;
// }