Improved the OptimizerFactory. However, the optimizer.init() methods have to be called after all settings were applied to the certain optimizer.
This commit is contained in:
Andreas Dräger
parent
fe490f4b22
commit
2db50903f8
@ -1,3 +1,6 @@
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/*
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* Copyright (c) ZBiT, University of Tübingen, Germany
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*/
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package javaeva;
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import java.util.BitSet;
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@ -59,37 +62,47 @@ import javaeva.server.modules.GOParameters;
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* while (!terminator.isTerminated(mc.getPopulation())) mc.optimize();
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* </code>
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* </p>
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*
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*
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* @version 0.1
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* @since 2.0
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* @author mkron
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* @author Andreas Dräger <andreas.draeger@uni-tuebingen.de>
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* @date 17.04.2007 Copyright (c) ZBiT, University of Tübingen, Germany
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* Compiler: JDK 1.5.0_10
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* @date 17.04.2007
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*/
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public class OptimizerFactory {
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private static InterfaceTerminator term = null;
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public final static int STD_ES = 1;
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public final static int CMA_ES = 2;
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public final static int STD_GA = 3;
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public final static int PSO = 4;
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public final static int DE = 5;
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public final static int TRIBES = 6;
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public final static int RANDOM = 7;
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public final static int HILLCL = 8;
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public final static int CBN_ES = 9;
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public final static int CL_HILLCL = 10;
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public final static int defaultFitCalls = 10000;
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public final static int randSeed = 0;
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private static OptimizerRunnable lastRunnable = null;
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/**
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* Add an InterfaceTerminator to any new optimizer in a boolean combination.
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* The old and the given terminator will be combined as in (TOld && TNew) if
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* bAnd is true, and as in (TOld || TNew) if bAnd is false.
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*
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*
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* @param newTerm
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* a new InterfaceTerminator instance
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* @param bAnd
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@ -102,7 +115,7 @@ public class OptimizerFactory {
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bAnd));
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}
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public static GOParameters cbnES(AbstractOptimizationProblem problem) {
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public static final GOParameters cbnES(AbstractOptimizationProblem problem) {
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ClusterBasedNichingEA cbn = new ClusterBasedNichingEA();
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EvolutionStrategies es = new EvolutionStrategies();
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es.setMu(15);
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@ -112,7 +125,7 @@ public class OptimizerFactory {
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ClusteringDensityBased clustering = new ClusteringDensityBased(0.1);
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cbn.setConvergenceCA((ClusteringDensityBased) clustering.clone());
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cbn.setDifferentationCA(clustering);
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cbn.setShowCycle(0); // dont do graphical output
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cbn.setShowCycle(0); // don't do graphical output
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Population pop = new Population();
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pop.setPopulationSize(100);
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@ -121,7 +134,7 @@ public class OptimizerFactory {
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return makeParams(cbn, pop, problem, randSeed, defaultTerminator());
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}
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public static GOParameters clusteringHillClimbing(
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public static final GOParameters clusteringHillClimbing(
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AbstractOptimizationProblem problem) {
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ClusteringHillClimbing chc = new ClusteringHillClimbing();
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chc.SetProblem(problem);
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@ -138,35 +151,37 @@ public class OptimizerFactory {
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return makeParams(chc, pop, problem, randSeed, defaultTerminator());
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}
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public static GOParameters cmaES(AbstractOptimizationProblem problem) {
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public static final GOParameters cmaES(AbstractOptimizationProblem problem) {
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EvolutionStrategies es = new EvolutionStrategies();
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es.setMu(15);
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es.setLambda(50);
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es.setPlusStrategy(false);
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// TODO improve this by adding getEAIndividual to AbstractEAIndividual?
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AbstractEAIndividual indyTemplate = problem.getIndividualTemplate();
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if ((indyTemplate != null) && (indyTemplate instanceof InterfaceESIndividual)) {
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// TODO improve this by adding getEAIndividual to AbstractEAIndividual?
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AbstractEAIndividual indyTemplate = problem.getIndividualTemplate();
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if ((indyTemplate != null)
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&& (indyTemplate instanceof InterfaceESIndividual)) {
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// Set CMA operator for mutation
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AbstractEAIndividual indy = (AbstractEAIndividual)indyTemplate;
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AbstractEAIndividual indy = (AbstractEAIndividual) indyTemplate;
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MutateESCovarianceMartixAdaption cmaMut = new MutateESCovarianceMartixAdaption();
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cmaMut.setCheckConstraints(true);
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indy.setMutationOperator(cmaMut);
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indy.setCrossoverOperator(new CrossoverESDefault());
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} else {
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System.err.println("Error, CMA-ES is implemented for ES individuals only (requires double data types)");
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System.err
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.println("Error, CMA-ES is implemented for ES individuals only (requires double data types)");
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return null;
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}
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Population pop = new Population();
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pop.setPopulationSize(es.getLambda());
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return makeParams(es, pop, problem, randSeed, defaultTerminator());
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}
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/**
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* This method optimizes the given problem using differential evolution.
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*
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*
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* @param problem
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* @param popsize
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* @param f
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@ -204,7 +219,7 @@ public class OptimizerFactory {
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/**
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* This method performs the optimization using an Evolution strategy.
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*
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*
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* @param mu
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* @param lambda
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* @param plus
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@ -248,7 +263,7 @@ public class OptimizerFactory {
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/**
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* This method performs a Genetic Algorithm.
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*
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*
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* @param mut
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* @param pm
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* @param cross
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@ -288,7 +303,7 @@ public class OptimizerFactory {
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/**
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* This starts a Gradient Descent.
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*
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*
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* @param problem
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* @return An optimization algorithm that performs gradient descent.
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*/
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@ -312,7 +327,7 @@ public class OptimizerFactory {
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/**
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* This method performs a Hill Climber algorithm.
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*
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*
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* @param pop
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* The size of the population
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* @param problem
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@ -335,7 +350,6 @@ public class OptimizerFactory {
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tmpIndi.setCrossoverProbability(0);
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HillClimbing hc = new HillClimbing();
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// System.out.println(hc.getStringRepresentation());
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hc.getPopulation().setPopulationSize(pop);
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hc.addPopulationChangedEventListener(listener);
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hc.SetProblem(problem);
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@ -349,7 +363,7 @@ public class OptimizerFactory {
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/**
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* This method performs a Monte Carlo Search with the given number of
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* fitnesscalls.
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*
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*
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* @param problem
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* @param listener
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* @return An optimization procedure that performes the random walk.
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@ -377,7 +391,7 @@ public class OptimizerFactory {
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/**
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* This method performs a particle swarm optimization.
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*
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*
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* @param problem
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* @param mut
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* @param popsize
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@ -421,7 +435,7 @@ public class OptimizerFactory {
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* This method performs a Simulated Annealing Optimization and prints the
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* result as R output. It uses real valued individuals. The mutation
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* probability is always 1.0.
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*
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*
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* @param problem
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* @param popsize
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* @param alpha
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@ -467,35 +481,36 @@ public class OptimizerFactory {
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/**
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* The default Terminator finishes after n fitness calls, the default n is
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* returned here.
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*
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*
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* @return the default number of fitness call done before termination
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*/
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public static int getDefaultFitCalls() {
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public static final int getDefaultFitCalls() {
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return defaultFitCalls;
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}
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///////////////////////////// constructing a default OptimizerRunnable
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public static GOParameters getParams(final int optType, AbstractOptimizationProblem problem) {
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// /////////////////////////// constructing a default OptimizerRunnable
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public static GOParameters getParams(final int optType,
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AbstractOptimizationProblem problem) {
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switch (optType) {
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case STD_ES:
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return standardES(problem);
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return standardES(problem);
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case CMA_ES:
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return cmaES(problem);
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return cmaES(problem);
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case STD_GA:
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return standardGA(problem);
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return standardGA(problem);
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case PSO:
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return standardPSO(problem);
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case DE:
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return standardDE(problem);
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return standardDE(problem);
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case TRIBES:
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return tribes(problem);
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return tribes(problem);
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case RANDOM:
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return monteCarlo(problem);
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return monteCarlo(problem);
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case HILLCL:
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return hillClimbing(problem);
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return hillClimbing(problem);
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case CBN_ES:
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return cbnES(problem);
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return cbnES(problem);
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case CL_HILLCL:
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return clusteringHillClimbing(problem);
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default:
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@ -503,13 +518,15 @@ public class OptimizerFactory {
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return null;
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}
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}
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public static OptimizerRunnable getOptRunnable(final int optType, AbstractOptimizationProblem problem, int fitCalls, String outputFilePrefix) {
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public static OptimizerRunnable getOptRunnable(final int optType,
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AbstractOptimizationProblem problem, int fitCalls, String outputFilePrefix) {
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OptimizerRunnable opt = null;
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GOParameters params = getParams(optType, problem);
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if (params != null) {
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opt = new OptimizerRunnable(params, outputFilePrefix);
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if (fitCalls != defaultFitCalls) opt.getGOParams().setTerminator(new EvaluationTerminator(fitCalls));
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if (fitCalls != defaultFitCalls)
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opt.getGOParams().setTerminator(new EvaluationTerminator(fitCalls));
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}
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return opt;
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}
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@ -524,7 +541,8 @@ public class OptimizerFactory {
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return OptimizerFactory.term;
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}
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public static GOParameters hillClimbing(AbstractOptimizationProblem problem) {
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public static final GOParameters hillClimbing(
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AbstractOptimizationProblem problem) {
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HillClimbing hc = new HillClimbing();
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hc.SetProblem(problem);
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Population pop = new Population();
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@ -534,9 +552,7 @@ public class OptimizerFactory {
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}
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public static int lastEvalsPerformed() {
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if (lastRunnable != null)
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return lastRunnable.getProgress();
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else return -1;
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return (lastRunnable != null) ? lastRunnable.getProgress() : -1;
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}
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// /////////////////////// Creating default strategies
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@ -552,7 +568,8 @@ public class OptimizerFactory {
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return params;
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}
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public static GOParameters monteCarlo(AbstractOptimizationProblem problem) {
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public static final GOParameters monteCarlo(
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AbstractOptimizationProblem problem) {
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MonteCarloSearch mc = new MonteCarloSearch();
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Population pop = new Population();
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pop.setPopulationSize(50);
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@ -567,11 +584,10 @@ public class OptimizerFactory {
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}
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public static OptimizerRunnable optimize(OptimizerRunnable runnable) {
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if (runnable != null) {
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new Thread(runnable).run();
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lastRunnable = runnable;
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return runnable;
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} else return null;
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if (runnable == null) return null;
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new Thread(runnable).run();
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lastRunnable = runnable;
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return runnable;
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}
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/**
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@ -579,7 +595,7 @@ public class OptimizerFactory {
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* thread. The Runnable will notify waiting threads and set the isFinished
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* flag when the optimization is complete. If the optType is invalid, null
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* will be returned.
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*
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*
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* @param optType
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* @param problem
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* @param outputFilePrefix
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@ -605,17 +621,13 @@ public class OptimizerFactory {
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public static BitSet optimizeToBinary(final int optType,
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AbstractOptimizationProblem problem, String outputFilePrefix) {
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OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
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if (runnable != null)
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return runnable.getBinarySolution();
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else return null;
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return (runnable != null) ? runnable.getBinarySolution() : null;
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}
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// ///////////////////////////// Optimize a given runnable
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public static BitSet optimizeToBinary(OptimizerRunnable runnable) {
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optimize(runnable);
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if (runnable != null)
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return runnable.getBinarySolution();
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else return null;
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return (runnable != null) ? runnable.getBinarySolution() : null;
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}
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public static double[] optimizeToDouble(GOParameters params,
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@ -628,16 +640,12 @@ public class OptimizerFactory {
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public static double[] optimizeToDouble(final int optType,
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AbstractOptimizationProblem problem, String outputFilePrefix) {
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OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
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if (runnable != null)
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return runnable.getDoubleSolution();
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else return null;
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return (runnable != null) ? runnable.getDoubleSolution() : null;
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}
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public static double[] optimizeToDouble(OptimizerRunnable runnable) {
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optimize(runnable);
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if (runnable != null)
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return runnable.getDoubleSolution();
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else return null;
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return (runnable != null) ? runnable.getDoubleSolution() : null;
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}
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public static IndividualInterface optimizeToInd(GOParameters params,
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@ -650,16 +658,12 @@ public class OptimizerFactory {
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public static IndividualInterface optimizeToInd(final int optType,
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AbstractOptimizationProblem problem, String outputFilePrefix) {
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OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
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if (runnable != null)
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return runnable.getResult();
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else return null;
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return (runnable != null) ? runnable.getResult() : null;
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}
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public static IndividualInterface optimizeToInd(OptimizerRunnable runnable) {
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optimize(runnable);
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if (runnable != null)
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return runnable.getResult();
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else return null;
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return (runnable != null) ? runnable.getResult() : null;
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}
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public static Population optimizeToPop(GOParameters params,
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@ -672,16 +676,12 @@ public class OptimizerFactory {
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public static Population optimizeToPop(final int optType,
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AbstractOptimizationProblem problem, String outputFilePrefix) {
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OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
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if (runnable != null)
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return runnable.getSolutionSet();
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else return null;
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return (runnable != null) ? runnable.getSolutionSet() : null;
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}
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public static Population optimizeToPop(OptimizerRunnable runnable) {
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optimize(runnable);
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if (runnable != null)
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return runnable.getSolutionSet();
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else return null;
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return (runnable != null) ? runnable.getSolutionSet() : null;
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}
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public static Population postProcess(int steps, double sigma, int nBest) {
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@ -746,9 +746,7 @@ public class OptimizerFactory {
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public static Vector<double[]> postProcessDblVec(
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InterfacePostProcessParams ppp) {
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if (lastRunnable != null)
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return postProcessDblVec(lastRunnable, ppp);
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else return null;
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return (lastRunnable != null) ? postProcessDblVec(lastRunnable, ppp) : null;
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}
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public static Vector<double[]> postProcessDblVec(OptimizerRunnable runnable,
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@ -817,7 +815,7 @@ public class OptimizerFactory {
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/**
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* Return a simple String showing the accessible optimizers. For external
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* access."
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*
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*
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* @return a String listing the accessible optimizers
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*/
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public static String showOptimizers() {
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@ -825,7 +823,8 @@ public class OptimizerFactory {
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+ "\n8: Hill-Climbing \n9: Cluster-based niching ES \n10: Clustering Hill-Climbing";
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}
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public static GOParameters standardDE(AbstractOptimizationProblem problem) {
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public static final GOParameters standardDE(
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AbstractOptimizationProblem problem) {
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DifferentialEvolution de = new DifferentialEvolution();
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Population pop = new Population();
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pop.setPopulationSize(50);
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@ -839,7 +838,8 @@ public class OptimizerFactory {
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return makeParams(de, pop, problem, randSeed, defaultTerminator());
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}
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public static GOParameters standardES(AbstractOptimizationProblem problem) {
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public static final GOParameters standardES(
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AbstractOptimizationProblem problem) {
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EvolutionStrategies es = new EvolutionStrategies();
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es.setMu(15);
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es.setLambda(50);
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@ -863,7 +863,8 @@ public class OptimizerFactory {
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return makeParams(es, pop, problem, randSeed, defaultTerminator());
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}
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public static GOParameters standardGA(AbstractOptimizationProblem problem) {
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public static final GOParameters standardGA(
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AbstractOptimizationProblem problem) {
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GeneticAlgorithm ga = new GeneticAlgorithm();
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Population pop = new Population();
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pop.setPopulationSize(100);
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@ -873,7 +874,8 @@ public class OptimizerFactory {
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return makeParams(ga, pop, problem, randSeed, defaultTerminator());
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}
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public static GOParameters standardPSO(AbstractOptimizationProblem problem) {
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public static final GOParameters standardPSO(
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AbstractOptimizationProblem problem) {
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ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
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Population pop = new Population();
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pop.setPopulationSize(30);
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@ -884,12 +886,10 @@ public class OptimizerFactory {
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}
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public static String terminatedBecause() {
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if (lastRunnable != null)
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return lastRunnable.terminatedBecause();
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else return null;
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return (lastRunnable != null) ? lastRunnable.terminatedBecause() : null;
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}
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public static GOParameters tribes(AbstractOptimizationProblem problem) {
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public static final GOParameters tribes(AbstractOptimizationProblem problem) {
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Tribes tr = new Tribes();
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Population pop = new Population();
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pop.setPopulationSize(1); // only for init
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Block a user