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Cleaned up PF strategy.

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This commit is contained in:
Marcel Kronfeld
2008-06-13 15:18:25 +00:00
parent 763f2a45af
commit 23207c6240
1 changed files with 16 additions and 36 deletions
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52
src/eva2/server/go/strategies/ParticleFilterOptimization.java
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@@ -1,9 +1,6 @@
package eva2.server.go.strategies;
import java.util.ArrayList;
import eva2.gui.GenericObjectEditor;
import eva2.gui.Plot;
import eva2.server.go.InterfacePopulationChangedEventListener;
import eva2.server.go.individuals.AbstractEAIndividual;
@@ -11,14 +8,15 @@ import eva2.server.go.individuals.InterfaceDataTypeDouble;
import eva2.server.go.individuals.InterfaceESIndividual;
import eva2.server.go.operators.mutation.MutateESFixedStepSize;
import eva2.server.go.operators.selection.InterfaceSelection;
import eva2.server.go.operators.selection.SelectXProbRouletteWheel;
import eva2.server.go.operators.selection.SelectParticleWheel;
import eva2.server.go.populations.Population;
import eva2.server.go.problems.F1Problem;
import eva2.server.go.problems.InterfaceOptimizationProblem;
/** This is a Particel Filter implemented by Frank Senke, only some documentation
* here and not throughfully checked, whether this works on arbitrary problem
* instances.
/** This is a Particle Filter implemented by Frank Senke, only some documentation
* here and not completely checked whether this works on arbitrary problem
* instances. MK did some adaptations, this should work on real valued problems now.
*
* This is a implementation of Genetic Algorithms.
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
@@ -36,8 +34,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
private static final long serialVersionUID = 1L;
private Population m_Population = new Population();
private InterfaceOptimizationProblem m_Problem = new F1Problem();
private InterfaceSelection m_ParentSelection = new SelectXProbRouletteWheel();//new SelectParticleWheel();
private ArrayList tmpA;
private InterfaceSelection m_ParentSelection = new SelectParticleWheel();
//private boolean m_UseElitism = true;
private String m_Identifier = "";
@@ -51,8 +48,6 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
transient Plot myPlot = null;
public ParticleFilterOptimization() {
tmpA = new ArrayList(100);
for (int i = 0; i < 100; i++) tmpA.add(new EvolutionStrategies());
if (withShow) setWithShow(true);
}
@@ -165,18 +160,18 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
m_Population = evaluatePopulation(nextGeneration);
collectStatistics(m_Population);
// collectStatistics(m_Population);
this.firePropertyChangedEvent("NextGenerationPerformed");
if (sleepTime > 0 ) try { Thread.sleep(sleepTime); } catch(Exception e) {}
}
protected void collectStatistics(Population population) {
// TODO Auto-generated method stub
int tMax = 5;
}
// protected void collectStatistics(Population population) {
// // TODO Auto-generated method stub
// int tMax = 5;
//
// }
/** This method allows you to add the LectureGUI as listener to the Optimizer
* @param ea
@@ -287,11 +282,6 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
**/
public void setWithShow(boolean wShow) {
this.withShow = wShow;
if (!wShow) {
GenericObjectEditor.setHideProperty(this.getClass(), "mutationSigma", true);
} else {
GenericObjectEditor.setHideProperty(this.getClass(), "mutationSigma", false);
}
if (!withShow) myPlot = null;
else {
double[][] range;
@@ -334,18 +324,8 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
public void setMutationSigma(double mutationSigma) {
this.mutationSigma = mutationSigma;
}
// /** This method will set the problem that is to be optimized
// * @param elitism
// */
// public void setElitism (boolean elitism) {
// this.m_UseElitism = elitism;
// }
// public boolean getElitism() {
// return this.m_UseElitism;
// }
// public String elitismTipText() {
// return "Enable/disable elitism.";
// }
public String mutationSigmaTipText() {
return "The mutation step for the gaussian motion model";
}
}