251 lines
8.4 KiB
Java
251 lines
8.4 KiB
Java
package javaeva.server.modules;
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import javaeva.server.go.InterfaceGOParameters;
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import javaeva.server.go.InterfacePopulationChangedEventListener;
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import javaeva.server.go.InterfaceTerminator;
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import javaeva.server.go.operators.selection.InterfaceSelection;
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import javaeva.server.go.operators.terminators.EvaluationTerminator;
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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.InterfaceOptimizationProblem;
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import javaeva.server.go.strategies.DifferentialEvolution;
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import javaeva.server.go.strategies.GeneticAlgorithm;
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import javaeva.server.go.strategies.InterfaceOptimizer;
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import javaeva.tools.Serializer;
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import javaeva.tools.SelectedTag;
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import java.io.Serializable;
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/** The class gives access to all DE parameters for the JavaEvA
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* top level GUI.
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* Created by IntelliJ IDEA.
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* User: streiche
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* Date: 27.10.2004
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* Time: 13:49:09
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* To change this template use File | Settings | File Templates.
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*/
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public class DEParameters implements InterfaceGOParameters, Serializable {
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public static boolean TRACE = false;
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private String m_Name ="not defined";
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private long m_Seed = (long)100.0;
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// Opt. Algorithms and Parameters
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private InterfaceOptimizer m_Optimizer = new DifferentialEvolution();
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private InterfaceOptimizationProblem m_Problem = new F1Problem();
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//private int m_FunctionCalls = 1000;
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private InterfaceTerminator m_Terminator = new EvaluationTerminator();
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// private String m_OutputFileName = "none";
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transient private InterfacePopulationChangedEventListener m_Listener;
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/**
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*
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*/
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public static DEParameters getInstance() {
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if (TRACE) System.out.println("DEParameters getInstance 1");
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DEParameters Instance = (DEParameters) Serializer.loadObject("DEParameters.ser");
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if (TRACE) System.out.println("DEParameters getInstance 2");
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if (Instance == null) Instance = new DEParameters();
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return Instance;
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}
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/**
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*
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*/
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public void saveInstance() {
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Serializer.storeObject("DEParameters.ser",this);
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}
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/**
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*
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*/
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public DEParameters() {
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if (TRACE) System.out.println("DEParameters Constructor start");
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this.m_Name="Optimization parameters";
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this.m_Optimizer = new DifferentialEvolution();
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this.m_Problem = new F1Problem();
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//this.m_FunctionCalls = 1000;
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((EvaluationTerminator)this.m_Terminator).setFitnessCalls(1000);
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this.m_Optimizer.SetProblem(this.m_Problem);
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if (TRACE) System.out.println("DEParameters Constructor end");
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}
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/**
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*
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*/
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private DEParameters(DEParameters Source) {
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this.m_Name = Source.m_Name;
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this.m_Optimizer = Source.m_Optimizer;
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this.m_Problem = Source.m_Problem;
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this.m_Terminator = Source.m_Terminator;
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//this.m_FunctionCalls = Source.m_FunctionCalls;
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this.m_Optimizer.SetProblem(this.m_Problem);
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this.m_Seed = Source.m_Seed;
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}
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/**
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*
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*/
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public String getName() {
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return m_Name;
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}
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/**
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*
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*/
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public Object clone() {
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return new DEParameters(this);
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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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if (this.m_Optimizer != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
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}
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/**
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*
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*/
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public String toString() {
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String ret = "\r\nDE-Parameter:"+this.m_Problem.getStringRepresentationForProblem(this.m_Optimizer)+"\n"+this.m_Optimizer.getStringRepresentation();
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return ret;
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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 Differential Evolution optimization method, please limit DE to real-valued genotypes.";
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}
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/** This methods allow you to set and get the Seed for the Random Number Generator.
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* @param x Long seed.
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*/
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public void setSeed(long x) {
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m_Seed = x;
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}
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public long getSeed() {
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return m_Seed;
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}
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public String seedTipText() {
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return "Random number seed.";
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}
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/** This method allows you to set the current optimizing algorithm
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* @param optimizer The new optimizing algorithm
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*/
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public void setOptimizer(InterfaceOptimizer optimizer) {
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// i'm a Monte Carlo Search Algorithm
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// *pff* i'll ignore that!
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}
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public InterfaceOptimizer getOptimizer() {
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return this.m_Optimizer;
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}
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/** This method allows you to choose a termination criteria for the
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* evolutionary algorithm.
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* @param term The new terminator
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*/
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public void setTerminator(InterfaceTerminator term) {
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this.m_Terminator = term;
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}
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public InterfaceTerminator getTerminator() {
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return this.m_Terminator;
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}
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public String terminatorTipText() {
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return "Choose a termination criterion.";
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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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this.m_Optimizer.SetProblem(this.m_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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public String problemTipText() {
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return "Choose the problem that is to optimize and the EA individual parameters.";
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}
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/** This method will set the output filename
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* @param name
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*/
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// public void setOutputFileName (String name) {
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// this.m_OutputFileName = name;
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// }
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// public String getOutputFileName () {
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// return this.m_OutputFileName;
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// }
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// public String outputFileNameTipText() {
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// return "Set the name for the output file, if 'none' no output file will be created.";
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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 ((DifferentialEvolution)this.m_Optimizer).getPopulation();
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}
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public void setPopulation(Population pop){
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((DifferentialEvolution)this.m_Optimizer).setPopulation(pop);
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}
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public String populationTipText() {
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return "Edit the properties of the population used.";
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}
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/** This method will set the amplication factor f
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* @param f
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*/
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public void setF (double f) {
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((DifferentialEvolution)this.m_Optimizer).setF(f);
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}
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public double getF() {
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return ((DifferentialEvolution)this.m_Optimizer).getF();
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}
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public String fTipText() {
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return "F is a real and constant factor which controlls the ampllification of the differential variation.";
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}
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/** This method will set the crossover probability
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* @param k
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*/
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public void setK(double k) {
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((DifferentialEvolution)this.m_Optimizer).setK(k);
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}
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public double getK() {
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return ((DifferentialEvolution)this.m_Optimizer).getK();
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}
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public String kTipText() {
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return "Probability of alteration through DE1.";
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}
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/** This method will set greediness to move towards the best
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* @param l
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*/
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public void setLambda (double l) {
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((DifferentialEvolution)this.m_Optimizer).setLambda(l);
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}
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public double getLambda() {
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return ((DifferentialEvolution)this.m_Optimizer).getLambda();
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}
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public String lambdaTipText() {
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return "Enhance greediness through amplification of the differential vector to the best individual for DE2.";
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}
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/** This method allows you to choose the type of Differential Evolution.
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* @param s The type.
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*/
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public void setDEType(SelectedTag s) {
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((DifferentialEvolution)this.m_Optimizer).setDEType(s);
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}
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public SelectedTag getDEType() {
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return ((DifferentialEvolution)this.m_Optimizer).getDEType();
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}
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public String dETypeTipText() {
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return "Choose the type of Differential Evolution.";
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}
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} |