Cleaned up PF strategy.
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@@ -1,9 +1,6 @@
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package eva2.server.go.strategies;
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import java.util.ArrayList;
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import eva2.gui.GenericObjectEditor;
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import eva2.gui.Plot;
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import eva2.server.go.InterfacePopulationChangedEventListener;
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import eva2.server.go.individuals.AbstractEAIndividual;
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@@ -11,14 +8,15 @@ import eva2.server.go.individuals.InterfaceDataTypeDouble;
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import eva2.server.go.individuals.InterfaceESIndividual;
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import eva2.server.go.operators.mutation.MutateESFixedStepSize;
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import eva2.server.go.operators.selection.InterfaceSelection;
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import eva2.server.go.operators.selection.SelectXProbRouletteWheel;
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import eva2.server.go.operators.selection.SelectParticleWheel;
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import eva2.server.go.populations.Population;
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import eva2.server.go.problems.F1Problem;
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import eva2.server.go.problems.InterfaceOptimizationProblem;
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/** This is a Particel Filter implemented by Frank Senke, only some documentation
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* here and not throughfully checked, whether this works on arbitrary problem
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* instances.
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/** This is a Particle Filter implemented by Frank Senke, only some documentation
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* here and not completely checked whether this works on arbitrary problem
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* instances. MK did some adaptations, this should work on real valued problems now.
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*
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* This is a implementation of Genetic Algorithms.
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* Copyright: Copyright (c) 2003
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* Company: University of Tuebingen, Computer Architecture
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@@ -36,8 +34,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
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private static final long serialVersionUID = 1L;
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private Population m_Population = new Population();
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private InterfaceOptimizationProblem m_Problem = new F1Problem();
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private InterfaceSelection m_ParentSelection = new SelectXProbRouletteWheel();//new SelectParticleWheel();
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private ArrayList tmpA;
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private InterfaceSelection m_ParentSelection = new SelectParticleWheel();
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//private boolean m_UseElitism = true;
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private String m_Identifier = "";
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@@ -51,8 +48,6 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
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transient Plot myPlot = null;
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public ParticleFilterOptimization() {
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tmpA = new ArrayList(100);
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for (int i = 0; i < 100; i++) tmpA.add(new EvolutionStrategies());
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if (withShow) setWithShow(true);
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}
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@@ -165,18 +160,18 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
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m_Population = evaluatePopulation(nextGeneration);
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collectStatistics(m_Population);
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// collectStatistics(m_Population);
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this.firePropertyChangedEvent("NextGenerationPerformed");
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if (sleepTime > 0 ) try { Thread.sleep(sleepTime); } catch(Exception e) {}
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}
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protected void collectStatistics(Population population) {
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// TODO Auto-generated method stub
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int tMax = 5;
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}
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// protected void collectStatistics(Population population) {
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// // TODO Auto-generated method stub
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// int tMax = 5;
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//
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// }
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/** This method allows you to add the LectureGUI as listener to the Optimizer
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* @param ea
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@@ -287,11 +282,6 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
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**/
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public void setWithShow(boolean wShow) {
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this.withShow = wShow;
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if (!wShow) {
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GenericObjectEditor.setHideProperty(this.getClass(), "mutationSigma", true);
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} else {
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GenericObjectEditor.setHideProperty(this.getClass(), "mutationSigma", false);
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}
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if (!withShow) myPlot = null;
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else {
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double[][] range;
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@@ -335,17 +325,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
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this.mutationSigma = mutationSigma;
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}
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// /** This method will set the problem that is to be optimized
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// * @param elitism
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// */
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// public void setElitism (boolean elitism) {
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// this.m_UseElitism = elitism;
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// }
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// public boolean getElitism() {
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// return this.m_UseElitism;
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// }
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// public String elitismTipText() {
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// return "Enable/disable elitism.";
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// }
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public String mutationSigmaTipText() {
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return "The mutation step for the gaussian motion model";
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}
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}
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