OptimizerFactory now contains several new methods.
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Andreas Dräger
parent
cbc6992ac5
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83cd026722
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@ -9,102 +9,136 @@ import javaeva.server.go.tools.RandomNumberGenerator;
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import java.util.BitSet;
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/**
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* Created by IntelliJ IDEA.
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* User: streiche
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* Date: 03.04.2003
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* Time: 10:34:17
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* To change this template use Options | File Templates.
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* Created by IntelliJ IDEA. User: streiche Date: 03.04.2003 Time: 10:34:17 To
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* change this template use Options | File Templates.
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*/
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public class CrossoverGADefault implements InterfaceCrossover, java.io.Serializable {
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private InterfaceOptimizationProblem m_OptimizationProblem;
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public class CrossoverGADefault implements InterfaceCrossover,
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java.io.Serializable {
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private InterfaceOptimizationProblem m_OptimizationProblem;
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public CrossoverGADefault() {
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public CrossoverGADefault() {
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}
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public CrossoverGADefault(CrossoverGADefault c) {
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this.m_OptimizationProblem = c.m_OptimizationProblem;
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}
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/** This method will enable you to clone a given mutation operator
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* @return The clone
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*/
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public Object clone() {
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return new CrossoverGADefault(this);
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}
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}
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/** This method performs crossover on two individuals. If the individuals do
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* not implement InterfaceGAIndividual, then nothing will happen.
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* @param indy1 The first individual
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* @param partners The second individual
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*/
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public AbstractEAIndividual[] mate(AbstractEAIndividual indy1, Population partners) {
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AbstractEAIndividual[] result = null;
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result = new AbstractEAIndividual[partners.size()+1];
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result[0] = (AbstractEAIndividual) (indy1).clone();
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for (int i = 0; i < partners.size(); i++) result[i+1] = (AbstractEAIndividual) ((AbstractEAIndividual)partners.get(i)).clone();
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//for (int i = 0; i < result.length; i++) System.out.println("Before Crossover: " +result[i].getSolutionRepresentationFor());
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if (partners.size() == 0) return result;
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if ((indy1 instanceof InterfaceGAIndividual) && (partners.get(0) instanceof InterfaceGAIndividual)) {
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// Currently we will only handle two parents
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int crossoverpoint = RandomNumberGenerator.randomInt(0,((InterfaceGAIndividual)indy1).getGenotypeLength()-1);
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boolean tmpValue;
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BitSet[] tmpBitSets = new BitSet[2];
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tmpBitSets[0] = ((InterfaceGAIndividual)result[0]).getBGenotype();
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tmpBitSets[1] = ((InterfaceGAIndividual)result[1]).getBGenotype();
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for (int i = crossoverpoint; i < ((InterfaceGAIndividual)result[0]).getGenotypeLength(); i++) {
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if (tmpBitSets[0].get(i)) tmpValue = true;
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else tmpValue = false;
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if (tmpBitSets[1].get(i)) tmpBitSets[0].set(i);
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else tmpBitSets[0].clear(i);
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if (tmpValue) tmpBitSets[1].set(i);
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else tmpBitSets[1].clear(i);
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}
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((InterfaceGAIndividual)result[0]).SetBGenotype(tmpBitSets[0]);
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((InterfaceGAIndividual)result[1]).SetBGenotype(tmpBitSets[1]);
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}
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//in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
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for (int i = 0; i < result.length; i++) result[i].getMutationOperator().crossoverOnStrategyParameters(indy1, partners);
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//for (int i = 0; i < result.length; i++) System.out.println("After Crossover: " +result[i].getSolutionRepresentationFor());
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return result;
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}
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public CrossoverGADefault(CrossoverGADefault c) {
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this.m_OptimizationProblem = c.m_OptimizationProblem;
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}
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/** This method allows you to evaluate wether two crossover operators
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* are actually the same.
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* @param crossover The other crossover operator
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*/
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public boolean equals(Object crossover) {
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if (crossover instanceof CrossoverGADefault) return true;
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else return false;
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}
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/**
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* This method will enable you to clone a given mutation operator
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*
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* @return The clone
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*/
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public Object clone() {
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return new CrossoverGADefault(this);
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}
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/** This method will allow the crossover operator to be initialized depending on the
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* individual and the optimization problem. The optimization problem is to be stored
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* since it is to be called during crossover to calculate the exogene parameters for
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* the offsprings.
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* @param individual The individual that will be mutated.
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* @param opt The optimization problem.
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*/
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public void init(AbstractEAIndividual individual, InterfaceOptimizationProblem opt) {
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this.m_OptimizationProblem = opt;
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}
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/**
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* This method performs crossover on two individuals. If the individuals do
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* not implement InterfaceGAIndividual, then nothing will happen.
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*
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* @param indy1
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* The first individual
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* @param partners
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* The second individual
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*/
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public AbstractEAIndividual[] mate(AbstractEAIndividual indy1,
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Population partners) {
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AbstractEAIndividual[] result = null;
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result = new AbstractEAIndividual[partners.size() + 1];
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result[0] = (AbstractEAIndividual) (indy1).clone();
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for (int i = 0; i < partners.size(); i++)
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result[i + 1] = (AbstractEAIndividual) ((AbstractEAIndividual) partners
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.get(i)).clone();
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// for (int i = 0; i < result.length; i++) System.out.println("Before
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// Crossover: " +result[i].getSolutionRepresentationFor());
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if (partners.size() == 0) return result;
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if ((indy1 instanceof InterfaceGAIndividual)
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&& (partners.get(0) instanceof InterfaceGAIndividual)) {
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// Currently we will only handle two parents
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int crossoverpoint = RandomNumberGenerator.randomInt(0,
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((InterfaceGAIndividual) indy1).getGenotypeLength() - 1);
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boolean tmpValue;
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BitSet[] tmpBitSets = new BitSet[2];
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tmpBitSets[0] = ((InterfaceGAIndividual) result[0]).getBGenotype();
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tmpBitSets[1] = ((InterfaceGAIndividual) result[1]).getBGenotype();
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for (int i = crossoverpoint; i < ((InterfaceGAIndividual) result[0])
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.getGenotypeLength(); i++) {
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if (tmpBitSets[0].get(i))
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tmpValue = true;
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else tmpValue = false;
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if (tmpBitSets[1].get(i))
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tmpBitSets[0].set(i);
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else tmpBitSets[0].clear(i);
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if (tmpValue)
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tmpBitSets[1].set(i);
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else tmpBitSets[1].clear(i);
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}
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((InterfaceGAIndividual) result[0]).SetBGenotype(tmpBitSets[0]);
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((InterfaceGAIndividual) result[1]).SetBGenotype(tmpBitSets[1]);
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}
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// in case the crossover was successfull lets give the mutation operators a
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// chance to mate the strategy parameters
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for (int i = 0; i < result.length; i++)
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result[i].getMutationOperator().crossoverOnStrategyParameters(indy1,
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partners);
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// for (int i = 0; i < result.length; i++) System.out.println("After
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// Crossover: " +result[i].getSolutionRepresentationFor());
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return result;
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}
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public String getStringRepresentation() {
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return this.getName();
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}
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/**
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* This method allows you to evaluate wether two crossover operators are
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* actually the same.
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*
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* @param crossover
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* The other crossover operator
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*/
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public boolean equals(Object crossover) {
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if (crossover instanceof CrossoverGADefault)
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return true;
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else return false;
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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 allows the CommonJavaObjectEditorPanel to read the
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* name to the current object.
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* @return The name.
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*/
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public String getName() {
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return "GA default crossover";
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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 "This is a one-point crossover between two individuals.";
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}
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/**
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* This method will allow the crossover operator to be initialized depending
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* on the individual and the optimization problem. The optimization problem is
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* to be stored since it is to be called during crossover to calculate the
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* exogene parameters for the offsprings.
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*
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* @param individual
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* The individual that will be mutated.
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* @param opt
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* The optimization problem.
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*/
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public void init(AbstractEAIndividual individual,
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InterfaceOptimizationProblem opt) {
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this.m_OptimizationProblem = opt;
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}
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public String getStringRepresentation() {
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return this.getName();
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}
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/*****************************************************************************
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* These are for GUI
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*/
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/**
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* This method allows the CommonJavaObjectEditorPanel to read the name to the
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* current object.
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*
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* @return The name.
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*/
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public String getName() {
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return "GA default crossover";
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}
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/**
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* This method returns a global info string
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*
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* @return description
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*/
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public String globalInfo() {
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return "This is a one-point crossover between two individuals.";
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}
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}
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@ -1,311 +1,397 @@
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package javaeva.server.go.strategies;
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import java.util.Hashtable;
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import javaeva.server.go.InterfacePopulationChangedEventListener;
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import javaeva.server.go.PopulationInterface;
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import javaeva.server.go.individuals.AbstractEAIndividual;
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import javaeva.server.go.operators.selection.InterfaceSelection;
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import javaeva.server.go.operators.selection.SelectBest;
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import javaeva.server.go.operators.selection.SelectBestIndividuals;
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import javaeva.server.go.populations.Population;
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import javaeva.server.go.problems.F1Problem;
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import javaeva.server.go.problems.InterfaceLocalSearchable;
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import javaeva.server.go.problems.InterfaceOptimizationProblem;
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import java.util.Hashtable;
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/** A memetic algorithm by hannes planatscher. The local search strategy can only be applied
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* to problems which implement the InterfaceLocalSearchable else the local search will not be
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* activated at all.
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* <p>Title: The JavaEvA</p>
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* <p>Description: </p>
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* <p>Copyright: Copyright (c) 2003</p>
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* <p>Company: </p>
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/**
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* A memetic algorithm by hannes planatscher. The local search strategy can only
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* be applied to problems which implement the InterfaceLocalSearchable else the
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* local search will not be activated at all.
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* <p>
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* Title: The JavaEvA
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* </p>
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* <p>
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* Description:
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* </p>
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* <p>
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* Copyright: Copyright (c) 2003
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* </p>
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* <p>
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* Company:
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* </p>
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*
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* @author not attributable
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* @version 1.0
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*/
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public class MemeticAlgorithm implements InterfaceOptimizer, java.io.Serializable {
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public class MemeticAlgorithm implements InterfaceOptimizer,
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java.io.Serializable {
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private int localSearchSteps = 1;
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private int subsetsize = 5;
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private int globalSearchSteps = 1;
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private boolean lamarckism = true;
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// int counter = 0; !?
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// int maxfunctioncalls = 1000; !?
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/**
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* serial version uid.
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*/
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private static final long serialVersionUID = -1730086430763348568L;
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private boolean TRACE = false;
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private String m_Identifier = "";
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private InterfaceOptimizationProblem m_Problem = new F1Problem();
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private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm();
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private InterfaceSelection selectorPlug = new SelectBestIndividuals();
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transient private InterfacePopulationChangedEventListener m_Listener;
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private int localSearchSteps = 1;
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private int subsetsize = 5;
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public MemeticAlgorithm() {
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private int globalSearchSteps = 1;
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}
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private boolean lamarckism = true;
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public MemeticAlgorithm(MemeticAlgorithm a) {
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// this.m_Population = (Population)a.m_Population.clone();
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this.m_Problem = (InterfaceLocalSearchable)a.m_Problem.clone();
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this.m_GlobalOptimizer = (InterfaceOptimizer)a.m_GlobalOptimizer;
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this.selectorPlug = (InterfaceSelection)a.selectorPlug;
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this.m_Identifier = a.m_Identifier;
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this.localSearchSteps = a.localSearchSteps;
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this.subsetsize = a.subsetsize;
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this.globalSearchSteps = a.globalSearchSteps;
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this.lamarckism = a.lamarckism;
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}
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// int counter = 0; !?
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// int maxfunctioncalls = 1000; !?
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public Object clone() {
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return (Object) new MemeticAlgorithm(this);
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}
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private boolean TRACE = false;
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public void initByPopulation(Population pop, boolean reset) {
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this.setPopulation((Population) pop.clone());
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if (reset)this.getPopulation().init();
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this.m_Problem.evaluate(this.getPopulation());
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this.firePropertyChangedEvent("NextGenerationPerformed");
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}
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private String m_Identifier = "";
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public void init() {
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//counter = 0;
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this.m_GlobalOptimizer.SetProblem(this.m_Problem);
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this.m_GlobalOptimizer.init();
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this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation());
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this.firePropertyChangedEvent("NextGenerationPerformed");
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}
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private InterfaceOptimizationProblem m_Problem = new F1Problem();
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/** This method will evaluate the current population using the
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* given problem.
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* @param population The population that is to be evaluated
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*/
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private void evaluatePopulation(Population population) {
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this.m_Problem.evaluate(population);
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population.incrGeneration();
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}
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private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm();
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public void optimize() {
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private InterfaceSelection selectorPlug = new SelectBestIndividuals();
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if (TRACE) System.out.println("global search");
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this.m_GlobalOptimizer.optimize();
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transient private InterfacePopulationChangedEventListener m_Listener;
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if ((this.m_GlobalOptimizer.getPopulation().getGeneration()%this.globalSearchSteps == 0)
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&& (this.localSearchSteps != 0)
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&& (this.m_Problem instanceof InterfaceLocalSearchable)){
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// here the local search is performed
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if (TRACE) System.out.println("Performing local search on " +subsetsize+ " individuals.");
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Population gop = this.m_GlobalOptimizer.getPopulation();
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Population subset = selectorPlug.selectFrom(gop, subsetsize);
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Population subsetclone = new Population();
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for (int i = 0; i < subset.size(); i++) {
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subsetclone.add(((AbstractEAIndividual)subset.get(i)).clone());
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}
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if (subset.size() != subsetsize) System.out.println("ALERT! identic individual instances in subset");
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Hashtable antilamarckismcache = new Hashtable();
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if (!this.lamarckism) {
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for (int i = 0; i < subset.size(); i++) {
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AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i);
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AbstractEAIndividual indyclone = (AbstractEAIndividual) subsetclone.get(i);
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antilamarckismcache.put(indy, indyclone);
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}
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}
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public MemeticAlgorithm() {
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//int dosearchsteps = this.localSearchSteps;
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double cost = ((InterfaceLocalSearchable)this.m_Problem).getLocalSearchStepFunctionCallEquivalent();
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//int calls = gop.getFunctionCalls() + (int) Math.round(localSearchSteps * cost * subset.size());
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// nett aber total unn<EFBFBD>tig-falsch man kann nicht davon ausgehen, dass man einen Fitnesscall Terminator hat..
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// if (calls > this.maxfunctioncalls) {
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// int remainingfunctioncalls = this.maxfunctioncalls - gop.getFunctionCalls();
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// dosearchsteps = (int)Math.floor(((double) remainingfunctioncalls) / (cost * subsetsize));
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// stopit = true;
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// }
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for (int i = 0; i < localSearchSteps ; i++) {
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((InterfaceLocalSearchable)this.m_Problem).doLocalSearch(subsetclone);
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}
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this.m_Problem.evaluate(subsetclone);
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if (this.lamarckism) {
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gop.removeAll(subset);
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gop.addPopulation(subsetclone);
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} else {
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for (int i = 0; i < subset.size(); i++) {
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AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i);
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try {
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AbstractEAIndividual newindy = (AbstractEAIndividual) antilamarckismcache.get(indy);
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indy.SetFitness(newindy.getFitness());
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}
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catch (Exception ex) {
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System.out.println("indy not found in antilamarckismcache");
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}
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}
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}
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// eigentlich muss hier noch subsetsize drauf, aber lassen wir das
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gop.SetFunctionCalls(gop.getFunctionCalls() + (int) Math.round(localSearchSteps * cost * subset.size()));
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}
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if (TRACE) System.out.println("Population size after local search:" + gop.size());
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public MemeticAlgorithm(MemeticAlgorithm a) {
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// this.m_Population = (Population)a.m_Population.clone();
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this.m_Problem = (InterfaceLocalSearchable) a.m_Problem.clone();
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this.m_GlobalOptimizer = (InterfaceOptimizer) a.m_GlobalOptimizer;
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this.selectorPlug = (InterfaceSelection) a.selectorPlug;
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this.m_Identifier = a.m_Identifier;
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this.localSearchSteps = a.localSearchSteps;
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this.subsetsize = a.subsetsize;
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this.globalSearchSteps = a.globalSearchSteps;
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this.lamarckism = a.lamarckism;
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}
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this.setPopulation(gop);
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}
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@Override
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public Object clone() {
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return new MemeticAlgorithm(this);
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}
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if (TRACE) System.out.println("function calls" + this.m_GlobalOptimizer.getPopulation().getFunctionCalls());
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this.firePropertyChangedEvent("NextGenerationPerformed");
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}
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public void initByPopulation(Population pop, boolean reset) {
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this.setPopulation((Population) pop.clone());
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if (reset) this.getPopulation().init();
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this.m_Problem.evaluate(this.getPopulation());
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this.firePropertyChangedEvent("NextGenerationPerformed");
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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) {
|
||||
if (TRACE) System.out.println("firePropertyChangedEvent MA");
|
||||
this.m_Listener.registerPopulationStateChanged(this, name);
|
||||
}
|
||||
}
|
||||
public void init() {
|
||||
// counter = 0;
|
||||
this.m_GlobalOptimizer.SetProblem(this.m_Problem);
|
||||
this.m_GlobalOptimizer.init();
|
||||
this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation());
|
||||
this.firePropertyChangedEvent("NextGenerationPerformed");
|
||||
}
|
||||
|
||||
/** This method will set the problem that is to be optimized
|
||||
* @param problem
|
||||
*/
|
||||
public void SetProblem (InterfaceOptimizationProblem problem) {
|
||||
this.m_Problem = problem;
|
||||
this.m_GlobalOptimizer.SetProblem(this.m_Problem);
|
||||
}
|
||||
public InterfaceOptimizationProblem getProblem () {
|
||||
return this.m_Problem;
|
||||
}
|
||||
/**
|
||||
* This method will evaluate the current population using the given problem.
|
||||
*
|
||||
* @param population
|
||||
* The population that is to be evaluated
|
||||
*/
|
||||
private void evaluatePopulation(Population population) {
|
||||
this.m_Problem.evaluate(population);
|
||||
population.incrGeneration();
|
||||
}
|
||||
|
||||
/** This method will return a string describing all properties of the optimizer
|
||||
* and the applied methods.
|
||||
* @return A descriptive string
|
||||
*/
|
||||
public String getStringRepresentation() {
|
||||
String result = "";
|
||||
result += "Memetic Algorithm:\n";
|
||||
result += "Optimization Problem: ";
|
||||
result += this.m_Problem.getStringRepresentationForProblem(this) +"\n";
|
||||
result += this.m_GlobalOptimizer.getStringRepresentation();
|
||||
return result;
|
||||
}
|
||||
/** This method allows you to set an identifier for the algorithm
|
||||
* @param name The indenifier
|
||||
*/
|
||||
public void SetIdentifier(String name) {
|
||||
this.m_Identifier = name;
|
||||
}
|
||||
public String getIdentifier() {
|
||||
return this.m_Identifier;
|
||||
}
|
||||
public void optimize() {
|
||||
|
||||
/** This method is required to free the memory on a RMIServer,
|
||||
* but there is nothing to implement.
|
||||
*/
|
||||
public void freeWilly() {
|
||||
if (TRACE) System.out.println("global search");
|
||||
this.m_GlobalOptimizer.optimize();
|
||||
|
||||
}
|
||||
/**********************************************************************************************************************
|
||||
* These are for GUI
|
||||
*/
|
||||
/** This method returns a global info string
|
||||
* @return description
|
||||
*/
|
||||
public String globalInfo() {
|
||||
return "This is a basic generational Memetic Algorithm.";
|
||||
}
|
||||
/** This method will return a naming String
|
||||
* @return The name of the algorithm
|
||||
*/
|
||||
public String getName() {
|
||||
return "Memetic-Algorithm";
|
||||
}
|
||||
if ((this.m_GlobalOptimizer.getPopulation().getGeneration()
|
||||
% this.globalSearchSteps == 0)
|
||||
&& (this.localSearchSteps != 0)
|
||||
&& (this.m_Problem instanceof InterfaceLocalSearchable)) {
|
||||
// here the local search is performed
|
||||
if (TRACE)
|
||||
System.out.println("Performing local search on " + subsetsize
|
||||
+ " individuals.");
|
||||
Population gop = this.m_GlobalOptimizer.getPopulation();
|
||||
Population subset = selectorPlug.selectFrom(gop, subsetsize);
|
||||
Population subsetclone = new Population();
|
||||
for (int i = 0; i < subset.size(); i++) {
|
||||
subsetclone.add(((AbstractEAIndividual) subset.get(i)).clone());
|
||||
}
|
||||
if (subset.size() != subsetsize)
|
||||
System.err.println("ALERT! identical individual instances in subset");
|
||||
Hashtable antilamarckismcache = new Hashtable();
|
||||
if (!this.lamarckism) {
|
||||
for (int i = 0; i < subset.size(); i++) {
|
||||
AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i);
|
||||
AbstractEAIndividual indyclone = (AbstractEAIndividual) subsetclone
|
||||
.get(i);
|
||||
antilamarckismcache.put(indy, indyclone);
|
||||
}
|
||||
}
|
||||
|
||||
/** 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_GlobalOptimizer.getPopulation();
|
||||
}
|
||||
public void setPopulation(Population pop){
|
||||
this.m_GlobalOptimizer.setPopulation(pop);
|
||||
}
|
||||
public String populationTipText() {
|
||||
return "Edit the properties of the population used.";
|
||||
}
|
||||
// int dosearchsteps = this.localSearchSteps;
|
||||
double cost = ((InterfaceLocalSearchable) this.m_Problem)
|
||||
.getLocalSearchStepFunctionCallEquivalent();
|
||||
// int calls = gop.getFunctionCalls() + (int) Math.round(localSearchSteps
|
||||
// * cost * subset.size());
|
||||
// nett aber total unn<EFBFBD>tig-falsch man kann nicht davon ausgehen, dass man
|
||||
// einen Fitnesscall Terminator hat..
|
||||
// if (calls > this.maxfunctioncalls) {
|
||||
// int remainingfunctioncalls = this.maxfunctioncalls -
|
||||
// gop.getFunctionCalls();
|
||||
// dosearchsteps = (int)Math.floor(((double) remainingfunctioncalls) /
|
||||
// (cost * subsetsize));
|
||||
// stopit = true;
|
||||
// }
|
||||
for (int i = 0; i < localSearchSteps; i++) {
|
||||
((InterfaceLocalSearchable) this.m_Problem).doLocalSearch(subsetclone);
|
||||
}
|
||||
this.m_Problem.evaluate(subsetclone);
|
||||
if (this.lamarckism) {
|
||||
gop.removeAll(subset);
|
||||
gop.addPopulation(subsetclone);
|
||||
} else {
|
||||
for (int i = 0; i < subset.size(); i++) {
|
||||
AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i);
|
||||
try {
|
||||
AbstractEAIndividual newindy = (AbstractEAIndividual) antilamarckismcache
|
||||
.get(indy);
|
||||
indy.SetFitness(newindy.getFitness());
|
||||
} catch (Exception ex) {
|
||||
System.err.println("individual not found in antilamarckismcache");
|
||||
}
|
||||
}
|
||||
}
|
||||
// eigentlich muss hier noch subsetsize drauf, aber lassen wir das
|
||||
gop.SetFunctionCalls(gop.getFunctionCalls()
|
||||
+ (int) Math.round(localSearchSteps * cost * subset.size()));
|
||||
|
||||
if (TRACE)
|
||||
System.out.println("Population size after local search:" + gop.size());
|
||||
|
||||
this.setPopulation(gop);
|
||||
}
|
||||
|
||||
if (TRACE)
|
||||
System.out.println("function calls"
|
||||
+ this.m_GlobalOptimizer.getPopulation().getFunctionCalls());
|
||||
this.firePropertyChangedEvent("NextGenerationPerformed");
|
||||
}
|
||||
|
||||
/**
|
||||
* This method allows you to add the LectureGUI as listener to the Optimizer
|
||||
*
|
||||
* @param ea
|
||||
*/
|
||||
public void addPopulationChangedEventListener(
|
||||
InterfacePopulationChangedEventListener ea) {
|
||||
this.m_Listener = ea;
|
||||
}
|
||||
|
||||
/**
|
||||
* Something has changed
|
||||
*/
|
||||
protected void firePropertyChangedEvent(String name) {
|
||||
if (this.m_Listener != null) {
|
||||
if (TRACE) System.out.println("firePropertyChangedEvent MA");
|
||||
this.m_Listener.registerPopulationStateChanged(this, name);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* This method will set the problem that is to be optimized
|
||||
*
|
||||
* @param problem
|
||||
*/
|
||||
public void SetProblem(InterfaceOptimizationProblem problem) {
|
||||
this.m_Problem = problem;
|
||||
this.m_GlobalOptimizer.SetProblem(this.m_Problem);
|
||||
}
|
||||
|
||||
public InterfaceOptimizationProblem getProblem() {
|
||||
return this.m_Problem;
|
||||
}
|
||||
|
||||
/**
|
||||
* This method will return a string describing all properties of the optimizer
|
||||
* and the applied methods.
|
||||
*
|
||||
* @return A descriptive string
|
||||
*/
|
||||
public String getStringRepresentation() {
|
||||
String result = "";
|
||||
result += "Memetic Algorithm:\n";
|
||||
result += "Optimization Problem: ";
|
||||
result += this.m_Problem.getStringRepresentationForProblem(this) + "\n";
|
||||
result += this.m_GlobalOptimizer.getStringRepresentation();
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* This method allows you to set an identifier for the algorithm
|
||||
*
|
||||
* @param name
|
||||
* The indenifier
|
||||
*/
|
||||
public void SetIdentifier(String name) {
|
||||
this.m_Identifier = name;
|
||||
}
|
||||
|
||||
public String getIdentifier() {
|
||||
return this.m_Identifier;
|
||||
}
|
||||
|
||||
/**
|
||||
* This method is required to free the memory on a RMIServer, but there is
|
||||
* nothing to implement.
|
||||
*/
|
||||
public void freeWilly() {
|
||||
|
||||
}
|
||||
|
||||
/*
|
||||
* ========================================================================================
|
||||
* These are for GUI
|
||||
*/
|
||||
/**
|
||||
* This method returns a global info string
|
||||
*
|
||||
* @return description
|
||||
*/
|
||||
public String globalInfo() {
|
||||
return "This is a basic generational Memetic Algorithm.";
|
||||
}
|
||||
|
||||
/**
|
||||
* This method will return a naming String
|
||||
*
|
||||
* @return The name of the algorithm
|
||||
*/
|
||||
public String getName() {
|
||||
return "Memetic-Algorithm";
|
||||
}
|
||||
|
||||
/**
|
||||
* 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_GlobalOptimizer.getPopulation();
|
||||
}
|
||||
|
||||
public void setPopulation(Population pop) {
|
||||
this.m_GlobalOptimizer.setPopulation(pop);
|
||||
}
|
||||
|
||||
public String populationTipText() {
|
||||
return "Edit the properties of the population used.";
|
||||
}
|
||||
|
||||
/**
|
||||
* Choose the global optimization strategy to use
|
||||
*
|
||||
* @param m_GlobalOptimizer
|
||||
*/
|
||||
public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) {
|
||||
this.m_GlobalOptimizer = m_GlobalOptimizer;
|
||||
this.m_GlobalOptimizer.SetProblem(this.getProblem());
|
||||
this.init();
|
||||
}
|
||||
|
||||
public InterfaceOptimizer getGlobalOptimizer() {
|
||||
return m_GlobalOptimizer;
|
||||
}
|
||||
|
||||
public String globalOptimizerTipText() {
|
||||
return "Choose the global optimization strategy to use.";
|
||||
}
|
||||
|
||||
/**
|
||||
* Choose the number of local search steps to perform per selected individual
|
||||
*
|
||||
* @param localSearchSteps
|
||||
*/
|
||||
public void setLocalSearchSteps(int localSearchSteps) {
|
||||
this.localSearchSteps = localSearchSteps;
|
||||
}
|
||||
|
||||
public int getLocalSearchSteps() {
|
||||
return localSearchSteps;
|
||||
}
|
||||
|
||||
public String localSearchStepsTipText() {
|
||||
return "Choose the number of local search steps to perform per selected individual.";
|
||||
}
|
||||
|
||||
/**
|
||||
* Choose the interval between the application of the local search
|
||||
*
|
||||
* @param globalSearchSteps
|
||||
*/
|
||||
public void setGlobalSearchSteps(int globalSearchSteps) {
|
||||
this.globalSearchSteps = globalSearchSteps;
|
||||
}
|
||||
|
||||
public int getGlobalSearchSteps() {
|
||||
return globalSearchSteps;
|
||||
}
|
||||
|
||||
public String globalSearchStepsTipText() {
|
||||
return "Choose the interval between the application of the local search.";
|
||||
}
|
||||
|
||||
/**
|
||||
* Choose the number of individual to be locally optimized
|
||||
*
|
||||
* @param subsetsize
|
||||
*/
|
||||
public void setSubsetsize(int subsetsize) {
|
||||
this.subsetsize = subsetsize;
|
||||
}
|
||||
|
||||
public Population getAllSolutions() {
|
||||
return getPopulation();
|
||||
}
|
||||
/** Choose the global optimization strategy to use
|
||||
* @param m_GlobalOptimizer
|
||||
*/
|
||||
public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) {
|
||||
this.m_GlobalOptimizer = m_GlobalOptimizer;
|
||||
this.m_GlobalOptimizer.SetProblem(this.getProblem());
|
||||
this.init();
|
||||
}
|
||||
public InterfaceOptimizer getGlobalOptimizer() {
|
||||
return m_GlobalOptimizer;
|
||||
}
|
||||
public String globalOptimizerTipText() {
|
||||
return "Choose the global optimization strategy to use.";
|
||||
}
|
||||
public int getSubsetsize() {
|
||||
return subsetsize;
|
||||
}
|
||||
|
||||
/** Choose the number of local search steps to perform per selected individual
|
||||
* @param localSearchSteps
|
||||
*/
|
||||
public void setLocalSearchSteps(int localSearchSteps) {
|
||||
this.localSearchSteps = localSearchSteps;
|
||||
}
|
||||
public int getLocalSearchSteps() {
|
||||
return localSearchSteps;
|
||||
}
|
||||
public String localSearchStepsTipText() {
|
||||
return "Choose the number of local search steps to perform per selected individual.";
|
||||
}
|
||||
public String subsetsizeTipText() {
|
||||
return "Choose the number of individual to be locally optimized.";
|
||||
}
|
||||
|
||||
/** Choose the interval between the application of the local search
|
||||
* @param globalSearchSteps
|
||||
*/
|
||||
public void setGlobalSearchSteps(int globalSearchSteps) {
|
||||
this.globalSearchSteps = globalSearchSteps;
|
||||
}
|
||||
public int getGlobalSearchSteps() {
|
||||
return globalSearchSteps;
|
||||
}
|
||||
public String globalSearchStepsTipText() {
|
||||
return "Choose the interval between the application of the local search.";
|
||||
}
|
||||
/**
|
||||
* Toggel between Lamarcksim and the Baldwin Effect
|
||||
*
|
||||
* @param lamarckism
|
||||
*/
|
||||
public void setLamarckism(boolean lamarckism) {
|
||||
this.lamarckism = lamarckism;
|
||||
}
|
||||
|
||||
/** Choose the number of individual to be locally optimized
|
||||
* @param subsetsize
|
||||
*/
|
||||
public void setSubsetsize(int subsetsize) {
|
||||
this.subsetsize = subsetsize;
|
||||
}
|
||||
public int getSubsetsize() {
|
||||
return subsetsize;
|
||||
}
|
||||
public String subsetsizeTipText() {
|
||||
return "Choose the number of individual to be locally optimized.";
|
||||
}
|
||||
public boolean getLamarckism() {
|
||||
return this.lamarckism;
|
||||
}
|
||||
|
||||
/** Toggel between Lamarcksim and the Baldwin Effect
|
||||
* @param lamarckism
|
||||
*/
|
||||
public void setLamarckism(boolean lamarckism) {
|
||||
this.lamarckism = lamarckism;
|
||||
}
|
||||
public boolean getLamarckism() {
|
||||
return this.lamarckism;
|
||||
}
|
||||
public String lamarckismTipText() {
|
||||
return "Toggel between Lamarcksim and the Baldwin Effect.";
|
||||
}
|
||||
public boolean isLamarckism() {
|
||||
return lamarckism;
|
||||
}
|
||||
public String lamarckismTipText() {
|
||||
return "Toggel between Lamarcksim and the Baldwin Effect.";
|
||||
}
|
||||
|
||||
public boolean isLamarckism() {
|
||||
return lamarckism;
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user