225 lines
8.8 KiB
Java
225 lines
8.8 KiB
Java
package javaeva.server.go.strategies;
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import javaeva.server.go.InterfacePopulationChangedEventListener;
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import javaeva.server.go.individuals.AbstractEAIndividual;
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import javaeva.server.go.individuals.GAIndividualBinaryData;
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import javaeva.server.go.populations.Population;
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import javaeva.server.go.problems.B1Problem;
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import javaeva.server.go.problems.InterfaceOptimizationProblem;
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/** This is a Multi-Start Hill-Climber, here the population size gives the number of
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* multi-starts. Similar to the evolutionary programming strategy this strategy sets the
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* mutation rate temporarily to 1.0.
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* Copyright: Copyright (c) 2003
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* Company: University of Tuebingen, Computer Architecture
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* @author Felix Streichert
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* @version: $Revision: 307 $
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* $Date: 2007-12-04 14:31:47 +0100 (Tue, 04 Dec 2007) $
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* $Author: mkron $
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*/
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public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
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// These variables are necessary for the simple testcase
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private InterfaceOptimizationProblem m_Problem = new B1Problem();
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private int m_MultiRuns = 100;
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private int m_FitnessCalls = 100;
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private int m_FitnessCallsNeeded = 0;
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GAIndividualBinaryData m_Best, m_Test;
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// These variables are necessary for the more complex LectureGUI enviroment
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transient private String m_Identifier = "";
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transient private InterfacePopulationChangedEventListener m_Listener;
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private Population m_Population;
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public HillClimbing() {
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this.m_Population = new Population();
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this.m_Population.setPopulationSize(10);
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}
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public HillClimbing(HillClimbing a) {
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this.m_Population = (Population)a.m_Population.clone();
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this.m_Problem = (InterfaceOptimizationProblem)a.m_Problem.clone();
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}
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public Object clone() {
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return (Object) new HillClimbing(this);
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}
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/** This method will init the HillClimber
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*/
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public void init() {
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this.m_Problem.initPopulation(this.m_Population);
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this.m_Problem.evaluate(this.m_Population);
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this.firePropertyChangedEvent("NextGenerationPerformed");
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}
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/** This method will init the optimizer with a given population
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* @param pop The initial population
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* @param reset If true the population is reset.
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*/
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public void initByPopulation(Population pop, boolean reset) {
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this.m_Population = (Population)pop.clone();
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if (reset) this.m_Population.init();
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this.m_Problem.evaluate(this.m_Population);
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this.firePropertyChangedEvent("NextGenerationPerformed");
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}
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/** This method will optimize
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*/
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public void optimize() {
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AbstractEAIndividual indy;
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Population original = (Population)this.m_Population.clone();
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for (int i = 0; i < this.m_Population.size(); i++) {
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indy = ((AbstractEAIndividual) this.m_Population.get(i));
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double tmpD = indy.getMutationProbability();
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indy.setMutationProbability(1.0);
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indy.mutate();
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indy.setMutationProbability(tmpD);
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}
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this.m_Problem.evaluate(this.m_Population);
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for (int i = 0; i < this.m_Population.size(); i++) {
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if (((AbstractEAIndividual)original.get(i)).isDominatingDebConstraints(((AbstractEAIndividual)this.m_Population.get(i)))) {
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this.m_Population.remove(i);
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this.m_Population.add(i, original.get(i));
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}
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}
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this.m_Population.incrGeneration();
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// for (int i = 0; i < this.m_Population.size(); i++) {
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// indy1 = (AbstractEAIndividual) this.m_Population.get(i);
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// indy2 = (AbstractEAIndividual)(indy1).clone();
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// indy2.mutate();
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// this.m_Problem.evaluate((AbstractEAIndividual) indy2);
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// //indy2.SetFitness(0, indy2.evaulateAsMiniBits());
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// this.m_Population.incrFunctionCalls();
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// //if (indy2.getFitness(0) < indy1.getFitness(0)) {
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// if (indy2.isDominating(indy1)) {
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// this.m_Population.remove(i);
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// this.m_Population.add(i, indy2);
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// }
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// }
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// this.m_Population.incrGeneration();
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this.firePropertyChangedEvent("NextGenerationPerformed");
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}
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/** This method will set the problem that is to be optimized
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* @param problem
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*/
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public void SetProblem (InterfaceOptimizationProblem problem) {
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this.m_Problem = problem;
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}
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public InterfaceOptimizationProblem getProblem () {
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return this.m_Problem;
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}
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/** This method will init the HillClimber
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*/
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public void defaultInit() {
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this.m_FitnessCallsNeeded = 0;
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this.m_Best = new GAIndividualBinaryData();
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this.m_Best.defaultInit();
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}
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/** This method will optimize
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*/
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public void defaultOptimize() {
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for (int i = 0; i < m_FitnessCalls; i++) {
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this.m_Test = (GAIndividualBinaryData)((this.m_Best).clone());
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this.m_Test.defaultMutate();
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if (this.m_Test.defaultEvaulateAsMiniBits() < this.m_Best.defaultEvaulateAsMiniBits()) this.m_Best = this.m_Test;
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this.m_FitnessCallsNeeded = i;
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if (this.m_Best.defaultEvaulateAsMiniBits() == 0) i = this.m_FitnessCalls +1;
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}
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}
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/** This main method will start a simple hillclimber.
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* No arguments necessary.
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* @param args
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*/
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public static void main(String[] args) {
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HillClimbing program = new HillClimbing();
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int TmpMeanCalls = 0, TmpMeanFitness = 0;
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for (int i = 0; i < program.m_MultiRuns; i++) {
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program.defaultInit();
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program.defaultOptimize();
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TmpMeanCalls += program.m_FitnessCallsNeeded;
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TmpMeanFitness += program.m_Best.defaultEvaulateAsMiniBits();
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}
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TmpMeanCalls = TmpMeanCalls/program.m_MultiRuns;
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TmpMeanFitness = TmpMeanFitness/program.m_MultiRuns;
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System.out.println("("+program.m_MultiRuns+"/"+program.m_FitnessCalls+") Mean Fitness : " + TmpMeanFitness + " Mean Calls needed: " + TmpMeanCalls);
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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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*/
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public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) {
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this.m_Listener = ea;
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}
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/** Something has changed
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*/
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protected void firePropertyChangedEvent (String name) {
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if (this.m_Listener != null) this.m_Listener.registerPopulationStateChanged(this, name);
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}
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/** This method will return a string describing all properties of the optimizer
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* and the applied methods.
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* @return A descriptive string
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*/
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public String getStringRepresentation() {
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String result = "";
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if (this.m_Population.size() > 1) result += "Multi(" + this.m_Population.size() + ")-Start Hill Climbing:\n";
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else result += "Hill Climbing:\n";
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result += "Optimization Problem: ";
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result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
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result += this.m_Population.getStringRepresentation();
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return result;
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}
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/** This method allows you to set an identifier for the algorithm
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* @param name The indenifier
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*/
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public void SetIdentifier(String name) {
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this.m_Identifier = name;
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}
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public String getIdentifier() {
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return this.m_Identifier;
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}
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/** This method is required to free the memory on a RMIServer,
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* but there is nothing to implement.
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*/
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public void freeWilly() {
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}
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/**********************************************************************************************************************
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* These are for GUI
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*/
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/** This method returns a global info string
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* @return description
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*/
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public String globalInfo() {
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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.";
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}
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/** This method will return a naming String
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* @return The name of the algorithm
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*/
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public String getName() {
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return "MS-HC";
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}
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/** Assuming that all optimizer will store thier data in a population
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* we will allow acess to this population to query to current state
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* of the optimizer.
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* @return The population of current solutions to a given problem.
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*/
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public Population getPopulation() {
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return this.m_Population;
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}
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public void setPopulation(Population pop){
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this.m_Population = pop;
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}
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public String populationTipText() {
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return "Change the number of best individuals stored (MS-HC).";
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}
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} |