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Improved the OptimizerFactory. However, the optimizer.init() methods have to be called after all settings were applied to the certain optimizer.

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Andreas Dräger 2008-03-27 15:55:39 +00:00
parent fe490f4b22
commit 2db50903f8
1 changed files with 88 additions and 88 deletions
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176
src/javaeva/OptimizerFactory.java
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@ -1,3 +1,6 @@
/*
* Copyright (c) ZBiT, University of Tübingen, Germany
*/
package javaeva; package javaeva;
import java.util.BitSet; import java.util.BitSet;
@ -59,37 +62,47 @@ import javaeva.server.modules.GOParameters;
* while (!terminator.isTerminated(mc.getPopulation())) mc.optimize(); * while (!terminator.isTerminated(mc.getPopulation())) mc.optimize();
* </code> * </code>
* </p> * </p>
* *
* @version 0.1 * @version 0.1
* @since 2.0 * @since 2.0
* @author mkron * @author mkron
* @author Andreas Dr&auml;ger <andreas.draeger@uni-tuebingen.de> * @author Andreas Dr&auml;ger <andreas.draeger@uni-tuebingen.de>
* @date 17.04.2007 Copyright (c) ZBiT, University of T&uuml;bingen, Germany * @date 17.04.2007
* Compiler: JDK 1.5.0_10
*/ */
public class OptimizerFactory { public class OptimizerFactory {
private static InterfaceTerminator term = null; private static InterfaceTerminator term = null;
public final static int STD_ES = 1; public final static int STD_ES = 1;
public final static int CMA_ES = 2; public final static int CMA_ES = 2;
public final static int STD_GA = 3; public final static int STD_GA = 3;
public final static int PSO = 4; public final static int PSO = 4;
public final static int DE = 5; public final static int DE = 5;
public final static int TRIBES = 6; public final static int TRIBES = 6;
public final static int RANDOM = 7; public final static int RANDOM = 7;
public final static int HILLCL = 8; public final static int HILLCL = 8;
public final static int CBN_ES = 9; public final static int CBN_ES = 9;
public final static int CL_HILLCL = 10; public final static int CL_HILLCL = 10;
public final static int defaultFitCalls = 10000; public final static int defaultFitCalls = 10000;
public final static int randSeed = 0; public final static int randSeed = 0;
private static OptimizerRunnable lastRunnable = null; private static OptimizerRunnable lastRunnable = null;
/** /**
* Add an InterfaceTerminator to any new optimizer in a boolean combination. * Add an InterfaceTerminator to any new optimizer in a boolean combination.
* The old and the given terminator will be combined as in (TOld && TNew) if * The old and the given terminator will be combined as in (TOld && TNew) if
* bAnd is true, and as in (TOld || TNew) if bAnd is false. * bAnd is true, and as in (TOld || TNew) if bAnd is false.
* *
* @param newTerm * @param newTerm
* a new InterfaceTerminator instance * a new InterfaceTerminator instance
* @param bAnd * @param bAnd
@ -102,7 +115,7 @@ public class OptimizerFactory {
bAnd)); bAnd));
} }
public static GOParameters cbnES(AbstractOptimizationProblem problem) { public static final GOParameters cbnES(AbstractOptimizationProblem problem) {
ClusterBasedNichingEA cbn = new ClusterBasedNichingEA(); ClusterBasedNichingEA cbn = new ClusterBasedNichingEA();
EvolutionStrategies es = new EvolutionStrategies(); EvolutionStrategies es = new EvolutionStrategies();
es.setMu(15); es.setMu(15);
@ -112,7 +125,7 @@ public class OptimizerFactory {
ClusteringDensityBased clustering = new ClusteringDensityBased(0.1); ClusteringDensityBased clustering = new ClusteringDensityBased(0.1);
cbn.setConvergenceCA((ClusteringDensityBased) clustering.clone()); cbn.setConvergenceCA((ClusteringDensityBased) clustering.clone());
cbn.setDifferentationCA(clustering); cbn.setDifferentationCA(clustering);
cbn.setShowCycle(0); // dont do graphical output cbn.setShowCycle(0); // don't do graphical output
Population pop = new Population(); Population pop = new Population();
pop.setPopulationSize(100); pop.setPopulationSize(100);
@ -121,7 +134,7 @@ public class OptimizerFactory {
return makeParams(cbn, pop, problem, randSeed, defaultTerminator()); return makeParams(cbn, pop, problem, randSeed, defaultTerminator());
} }
public static GOParameters clusteringHillClimbing( public static final GOParameters clusteringHillClimbing(
AbstractOptimizationProblem problem) { AbstractOptimizationProblem problem) {
ClusteringHillClimbing chc = new ClusteringHillClimbing(); ClusteringHillClimbing chc = new ClusteringHillClimbing();
chc.SetProblem(problem); chc.SetProblem(problem);
@ -138,35 +151,37 @@ public class OptimizerFactory {
return makeParams(chc, pop, problem, randSeed, defaultTerminator()); return makeParams(chc, pop, problem, randSeed, defaultTerminator());
} }
public static GOParameters cmaES(AbstractOptimizationProblem problem) { public static final GOParameters cmaES(AbstractOptimizationProblem problem) {
EvolutionStrategies es = new EvolutionStrategies(); EvolutionStrategies es = new EvolutionStrategies();
es.setMu(15); es.setMu(15);
es.setLambda(50); es.setLambda(50);
es.setPlusStrategy(false); es.setPlusStrategy(false);
// TODO improve this by adding getEAIndividual to AbstractEAIndividual? // TODO improve this by adding getEAIndividual to AbstractEAIndividual?
AbstractEAIndividual indyTemplate = problem.getIndividualTemplate(); AbstractEAIndividual indyTemplate = problem.getIndividualTemplate();
if ((indyTemplate != null) && (indyTemplate instanceof InterfaceESIndividual)) { if ((indyTemplate != null)
&& (indyTemplate instanceof InterfaceESIndividual)) {
// Set CMA operator for mutation // Set CMA operator for mutation
AbstractEAIndividual indy = (AbstractEAIndividual)indyTemplate; AbstractEAIndividual indy = (AbstractEAIndividual) indyTemplate;
MutateESCovarianceMartixAdaption cmaMut = new MutateESCovarianceMartixAdaption(); MutateESCovarianceMartixAdaption cmaMut = new MutateESCovarianceMartixAdaption();
cmaMut.setCheckConstraints(true); cmaMut.setCheckConstraints(true);
indy.setMutationOperator(cmaMut); indy.setMutationOperator(cmaMut);
indy.setCrossoverOperator(new CrossoverESDefault()); indy.setCrossoverOperator(new CrossoverESDefault());
} else { } else {
System.err.println("Error, CMA-ES is implemented for ES individuals only (requires double data types)"); System.err
.println("Error, CMA-ES is implemented for ES individuals only (requires double data types)");
return null; return null;
} }
Population pop = new Population(); Population pop = new Population();
pop.setPopulationSize(es.getLambda()); pop.setPopulationSize(es.getLambda());
return makeParams(es, pop, problem, randSeed, defaultTerminator()); return makeParams(es, pop, problem, randSeed, defaultTerminator());
} }
/** /**
* This method optimizes the given problem using differential evolution. * This method optimizes the given problem using differential evolution.
* *
* @param problem * @param problem
* @param popsize * @param popsize
* @param f * @param f
@ -204,7 +219,7 @@ public class OptimizerFactory {
/** /**
* This method performs the optimization using an Evolution strategy. * This method performs the optimization using an Evolution strategy.
* *
* @param mu * @param mu
* @param lambda * @param lambda
* @param plus * @param plus
@ -248,7 +263,7 @@ public class OptimizerFactory {
/** /**
* This method performs a Genetic Algorithm. * This method performs a Genetic Algorithm.
* *
* @param mut * @param mut
* @param pm * @param pm
* @param cross * @param cross
@ -288,7 +303,7 @@ public class OptimizerFactory {
/** /**
* This starts a Gradient Descent. * This starts a Gradient Descent.
* *
* @param problem * @param problem
* @return An optimization algorithm that performs gradient descent. * @return An optimization algorithm that performs gradient descent.
*/ */
@ -312,7 +327,7 @@ public class OptimizerFactory {
/** /**
* This method performs a Hill Climber algorithm. * This method performs a Hill Climber algorithm.
* *
* @param pop * @param pop
* The size of the population * The size of the population
* @param problem * @param problem
@ -335,7 +350,6 @@ public class OptimizerFactory {
tmpIndi.setCrossoverProbability(0); tmpIndi.setCrossoverProbability(0);
HillClimbing hc = new HillClimbing(); HillClimbing hc = new HillClimbing();
// System.out.println(hc.getStringRepresentation());
hc.getPopulation().setPopulationSize(pop); hc.getPopulation().setPopulationSize(pop);
hc.addPopulationChangedEventListener(listener); hc.addPopulationChangedEventListener(listener);
hc.SetProblem(problem); hc.SetProblem(problem);
@ -349,7 +363,7 @@ public class OptimizerFactory {
/** /**
* This method performs a Monte Carlo Search with the given number of * This method performs a Monte Carlo Search with the given number of
* fitnesscalls. * fitnesscalls.
* *
* @param problem * @param problem
* @param listener * @param listener
* @return An optimization procedure that performes the random walk. * @return An optimization procedure that performes the random walk.
@ -377,7 +391,7 @@ public class OptimizerFactory {
/** /**
* This method performs a particle swarm optimization. * This method performs a particle swarm optimization.
* *
* @param problem * @param problem
* @param mut * @param mut
* @param popsize * @param popsize
@ -421,7 +435,7 @@ public class OptimizerFactory {
* This method performs a Simulated Annealing Optimization and prints the * This method performs a Simulated Annealing Optimization and prints the
* result as R output. It uses real valued individuals. The mutation * result as R output. It uses real valued individuals. The mutation
* probability is always 1.0. * probability is always 1.0.
* *
* @param problem * @param problem
* @param popsize * @param popsize
* @param alpha * @param alpha
@ -467,35 +481,36 @@ public class OptimizerFactory {
/** /**
* The default Terminator finishes after n fitness calls, the default n is * The default Terminator finishes after n fitness calls, the default n is
* returned here. * returned here.
* *
* @return the default number of fitness call done before termination * @return the default number of fitness call done before termination
*/ */
public static int getDefaultFitCalls() { public static final int getDefaultFitCalls() {
return defaultFitCalls; return defaultFitCalls;
} }
///////////////////////////// constructing a default OptimizerRunnable
public static GOParameters getParams(final int optType, AbstractOptimizationProblem problem) { // /////////////////////////// constructing a default OptimizerRunnable
public static GOParameters getParams(final int optType,
AbstractOptimizationProblem problem) {
switch (optType) { switch (optType) {
case STD_ES: case STD_ES:
return standardES(problem); return standardES(problem);
case CMA_ES: case CMA_ES:
return cmaES(problem); return cmaES(problem);
case STD_GA: case STD_GA:
return standardGA(problem); return standardGA(problem);
case PSO: case PSO:
return standardPSO(problem); return standardPSO(problem);
case DE: case DE:
return standardDE(problem); return standardDE(problem);
case TRIBES: case TRIBES:
return tribes(problem); return tribes(problem);
case RANDOM: case RANDOM:
return monteCarlo(problem); return monteCarlo(problem);
case HILLCL: case HILLCL:
return hillClimbing(problem); return hillClimbing(problem);
case CBN_ES: case CBN_ES:
return cbnES(problem); return cbnES(problem);
case CL_HILLCL: case CL_HILLCL:
return clusteringHillClimbing(problem); return clusteringHillClimbing(problem);
default: default:
@ -503,13 +518,15 @@ public class OptimizerFactory {
return null; return null;
} }
} }
public static OptimizerRunnable getOptRunnable(final int optType, AbstractOptimizationProblem problem, int fitCalls, String outputFilePrefix) { public static OptimizerRunnable getOptRunnable(final int optType,
AbstractOptimizationProblem problem, int fitCalls, String outputFilePrefix) {
OptimizerRunnable opt = null; OptimizerRunnable opt = null;
GOParameters params = getParams(optType, problem); GOParameters params = getParams(optType, problem);
if (params != null) { if (params != null) {
opt = new OptimizerRunnable(params, outputFilePrefix); opt = new OptimizerRunnable(params, outputFilePrefix);
if (fitCalls != defaultFitCalls) opt.getGOParams().setTerminator(new EvaluationTerminator(fitCalls)); if (fitCalls != defaultFitCalls)
opt.getGOParams().setTerminator(new EvaluationTerminator(fitCalls));
} }
return opt; return opt;
} }
@ -524,7 +541,8 @@ public class OptimizerFactory {
return OptimizerFactory.term; return OptimizerFactory.term;
} }
public static GOParameters hillClimbing(AbstractOptimizationProblem problem) { public static final GOParameters hillClimbing(
AbstractOptimizationProblem problem) {
HillClimbing hc = new HillClimbing(); HillClimbing hc = new HillClimbing();
hc.SetProblem(problem); hc.SetProblem(problem);
Population pop = new Population(); Population pop = new Population();
@ -534,9 +552,7 @@ public class OptimizerFactory {
} }
public static int lastEvalsPerformed() { public static int lastEvalsPerformed() {
if (lastRunnable != null) return (lastRunnable != null) ? lastRunnable.getProgress() : -1;
return lastRunnable.getProgress();
else return -1;
} }
// /////////////////////// Creating default strategies // /////////////////////// Creating default strategies
@ -552,7 +568,8 @@ public class OptimizerFactory {
return params; return params;
} }
public static GOParameters monteCarlo(AbstractOptimizationProblem problem) { public static final GOParameters monteCarlo(
AbstractOptimizationProblem problem) {
MonteCarloSearch mc = new MonteCarloSearch(); MonteCarloSearch mc = new MonteCarloSearch();
Population pop = new Population(); Population pop = new Population();
pop.setPopulationSize(50); pop.setPopulationSize(50);
@ -567,11 +584,10 @@ public class OptimizerFactory {
} }
public static OptimizerRunnable optimize(OptimizerRunnable runnable) { public static OptimizerRunnable optimize(OptimizerRunnable runnable) {
if (runnable != null) { if (runnable == null) return null;
new Thread(runnable).run(); new Thread(runnable).run();
lastRunnable = runnable; lastRunnable = runnable;
return runnable; return runnable;
} else return null;
} }
/** /**
@ -579,7 +595,7 @@ public class OptimizerFactory {
* thread. The Runnable will notify waiting threads and set the isFinished * thread. The Runnable will notify waiting threads and set the isFinished
* flag when the optimization is complete. If the optType is invalid, null * flag when the optimization is complete. If the optType is invalid, null
* will be returned. * will be returned.
* *
* @param optType * @param optType
* @param problem * @param problem
* @param outputFilePrefix * @param outputFilePrefix
@ -605,17 +621,13 @@ public class OptimizerFactory {
public static BitSet optimizeToBinary(final int optType, public static BitSet optimizeToBinary(final int optType,
AbstractOptimizationProblem problem, String outputFilePrefix) { AbstractOptimizationProblem problem, String outputFilePrefix) {
OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix); OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
if (runnable != null) return (runnable != null) ? runnable.getBinarySolution() : null;
return runnable.getBinarySolution();
else return null;
} }
// ///////////////////////////// Optimize a given runnable // ///////////////////////////// Optimize a given runnable
public static BitSet optimizeToBinary(OptimizerRunnable runnable) { public static BitSet optimizeToBinary(OptimizerRunnable runnable) {
optimize(runnable); optimize(runnable);
if (runnable != null) return (runnable != null) ? runnable.getBinarySolution() : null;
return runnable.getBinarySolution();
else return null;
} }
public static double[] optimizeToDouble(GOParameters params, public static double[] optimizeToDouble(GOParameters params,
@ -628,16 +640,12 @@ public class OptimizerFactory {
public static double[] optimizeToDouble(final int optType, public static double[] optimizeToDouble(final int optType,
AbstractOptimizationProblem problem, String outputFilePrefix) { AbstractOptimizationProblem problem, String outputFilePrefix) {
OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix); OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
if (runnable != null) return (runnable != null) ? runnable.getDoubleSolution() : null;
return runnable.getDoubleSolution();
else return null;
} }
public static double[] optimizeToDouble(OptimizerRunnable runnable) { public static double[] optimizeToDouble(OptimizerRunnable runnable) {
optimize(runnable); optimize(runnable);
if (runnable != null) return (runnable != null) ? runnable.getDoubleSolution() : null;
return runnable.getDoubleSolution();
else return null;
} }
public static IndividualInterface optimizeToInd(GOParameters params, public static IndividualInterface optimizeToInd(GOParameters params,
@ -650,16 +658,12 @@ public class OptimizerFactory {
public static IndividualInterface optimizeToInd(final int optType, public static IndividualInterface optimizeToInd(final int optType,
AbstractOptimizationProblem problem, String outputFilePrefix) { AbstractOptimizationProblem problem, String outputFilePrefix) {
OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix); OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
if (runnable != null) return (runnable != null) ? runnable.getResult() : null;
return runnable.getResult();
else return null;
} }
public static IndividualInterface optimizeToInd(OptimizerRunnable runnable) { public static IndividualInterface optimizeToInd(OptimizerRunnable runnable) {
optimize(runnable); optimize(runnable);
if (runnable != null) return (runnable != null) ? runnable.getResult() : null;
return runnable.getResult();
else return null;
} }
public static Population optimizeToPop(GOParameters params, public static Population optimizeToPop(GOParameters params,
@ -672,16 +676,12 @@ public class OptimizerFactory {
public static Population optimizeToPop(final int optType, public static Population optimizeToPop(final int optType,
AbstractOptimizationProblem problem, String outputFilePrefix) { AbstractOptimizationProblem problem, String outputFilePrefix) {
OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix); OptimizerRunnable runnable = optimize(optType, problem, outputFilePrefix);
if (runnable != null) return (runnable != null) ? runnable.getSolutionSet() : null;
return runnable.getSolutionSet();
else return null;
} }
public static Population optimizeToPop(OptimizerRunnable runnable) { public static Population optimizeToPop(OptimizerRunnable runnable) {
optimize(runnable); optimize(runnable);
if (runnable != null) return (runnable != null) ? runnable.getSolutionSet() : null;
return runnable.getSolutionSet();
else return null;
} }
public static Population postProcess(int steps, double sigma, int nBest) { public static Population postProcess(int steps, double sigma, int nBest) {
@ -746,9 +746,7 @@ public class OptimizerFactory {
public static Vector<double[]> postProcessDblVec( public static Vector<double[]> postProcessDblVec(
InterfacePostProcessParams ppp) { InterfacePostProcessParams ppp) {
if (lastRunnable != null) return (lastRunnable != null) ? postProcessDblVec(lastRunnable, ppp) : null;
return postProcessDblVec(lastRunnable, ppp);
else return null;
} }
public static Vector<double[]> postProcessDblVec(OptimizerRunnable runnable, public static Vector<double[]> postProcessDblVec(OptimizerRunnable runnable,
@ -817,7 +815,7 @@ public class OptimizerFactory {
/** /**
* Return a simple String showing the accessible optimizers. For external * Return a simple String showing the accessible optimizers. For external
* access." * access."
* *
* @return a String listing the accessible optimizers * @return a String listing the accessible optimizers
*/ */
public static String showOptimizers() { public static String showOptimizers() {
@ -825,7 +823,8 @@ public class OptimizerFactory {
+ "\n8: Hill-Climbing \n9: Cluster-based niching ES \n10: Clustering Hill-Climbing"; + "\n8: Hill-Climbing \n9: Cluster-based niching ES \n10: Clustering Hill-Climbing";
} }
public static GOParameters standardDE(AbstractOptimizationProblem problem) { public static final GOParameters standardDE(
AbstractOptimizationProblem problem) {
DifferentialEvolution de = new DifferentialEvolution(); DifferentialEvolution de = new DifferentialEvolution();
Population pop = new Population(); Population pop = new Population();
pop.setPopulationSize(50); pop.setPopulationSize(50);
@ -839,7 +838,8 @@ public class OptimizerFactory {
return makeParams(de, pop, problem, randSeed, defaultTerminator()); return makeParams(de, pop, problem, randSeed, defaultTerminator());
} }
public static GOParameters standardES(AbstractOptimizationProblem problem) { public static final GOParameters standardES(
AbstractOptimizationProblem problem) {
EvolutionStrategies es = new EvolutionStrategies(); EvolutionStrategies es = new EvolutionStrategies();
es.setMu(15); es.setMu(15);
es.setLambda(50); es.setLambda(50);
@ -863,7 +863,8 @@ public class OptimizerFactory {
return makeParams(es, pop, problem, randSeed, defaultTerminator()); return makeParams(es, pop, problem, randSeed, defaultTerminator());
} }
public static GOParameters standardGA(AbstractOptimizationProblem problem) { public static final GOParameters standardGA(
AbstractOptimizationProblem problem) {
GeneticAlgorithm ga = new GeneticAlgorithm(); GeneticAlgorithm ga = new GeneticAlgorithm();
Population pop = new Population(); Population pop = new Population();
pop.setPopulationSize(100); pop.setPopulationSize(100);
@ -873,7 +874,8 @@ public class OptimizerFactory {
return makeParams(ga, pop, problem, randSeed, defaultTerminator()); return makeParams(ga, pop, problem, randSeed, defaultTerminator());
} }
public static GOParameters standardPSO(AbstractOptimizationProblem problem) { public static final GOParameters standardPSO(
AbstractOptimizationProblem problem) {
ParticleSwarmOptimization pso = new ParticleSwarmOptimization(); ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
Population pop = new Population(); Population pop = new Population();
pop.setPopulationSize(30); pop.setPopulationSize(30);
@ -884,12 +886,10 @@ public class OptimizerFactory {
} }
public static String terminatedBecause() { public static String terminatedBecause() {
if (lastRunnable != null) return (lastRunnable != null) ? lastRunnable.terminatedBecause() : null;
return lastRunnable.terminatedBecause();
else return null;
} }
public static GOParameters tribes(AbstractOptimizationProblem problem) { public static final GOParameters tribes(AbstractOptimizationProblem problem) {
Tribes tr = new Tribes(); Tribes tr = new Tribes();
Population pop = new Population(); Population pop = new Population();
pop.setPopulationSize(1); // only for init pop.setPopulationSize(1); // only for init