halfdan/eva2
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Updating the optimizer factory

Browse Source
This commit is contained in:
Marcel Kronfeld 2011-05-03 14:05:37 +00:00
parent 7ae981098c
commit 3e8c2b14dc
1 changed files with 155 additions and 31 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

186
src/eva2/OptimizerFactory.java
View File

@ -45,6 +45,8 @@ import eva2.server.go.problems.AbstractOptimizationProblem;
import eva2.server.go.strategies.ClusterBasedNichingEA;
import eva2.server.go.strategies.ClusteringHillClimbing;
import eva2.server.go.strategies.DifferentialEvolution;
import eva2.server.go.strategies.EsDpiNiching;
import eva2.server.go.strategies.EsDpiNichingCma;
import eva2.server.go.strategies.EvolutionStrategies;
import eva2.server.go.strategies.EvolutionStrategyIPOP;
import eva2.server.go.strategies.GeneticAlgorithm;
@ -362,7 +364,7 @@ public class OptimizerFactory {
}
/**
* This method performs a Hill Climber algorithm.
* This method creates a Hill Climber algorithm with a default fixed-size mutation.
*
* @param pop
* The size of the population
@ -371,16 +373,33 @@ public class OptimizerFactory {
* @param listener
* @return An optimization procedure that performes hill climbing.
*/
public static final HillClimbing createHillClimber(int pop,
public static final HillClimbing createHillClimber(int popSize,
AbstractOptimizationProblem problem,
InterfacePopulationChangedEventListener listener) {
return createHillClimber(popSize, new MutateESFixedStepSize(0.2), problem, listener);
}
/**
* This method creates a Hill Climber algorithm.
*
* @param pop
* The size of the population
* @param problem
* The problem to be optimized
* @param listener
* @return An optimization procedure that performes hill climbing.
*/
public static final HillClimbing createHillClimber(int popSize, InterfaceMutation mutator,
AbstractOptimizationProblem problem,
InterfacePopulationChangedEventListener listener) {
problem.initProblem();
setTemplateOperators(problem, new MutateESFixedStepSize(0.2), 1., new NoCrossover(), 0);
setTemplateOperators(problem, mutator, 1., new NoCrossover(), 0);
HillClimbing hc = new HillClimbing();
hc.getPopulation().setTargetSize(pop);
hc.SetIdentifier("-"+popSize+"-"+mutator.getStringRepresentation());
hc.getPopulation().setTargetSize(popSize);
hc.addPopulationChangedEventListener(listener);
hc.SetProblem(problem);
hc.init();
@ -672,8 +691,9 @@ public class OptimizerFactory {
AbstractOptimizationProblem problem, String outputFilePrefix) {
return getOptRunnable(optType, problem, getTerminator(), outputFilePrefix);
}
/**
* Return the current user-defined or, if none was set, the default terminator.
* Return the current user-defined terminator or, if none was set, the default terminator.
*
* @return the current default terminator
*/
@ -702,7 +722,7 @@ public class OptimizerFactory {
*/
public static GOParameters makeESParams(EvolutionStrategies es,
AbstractOptimizationProblem problem) {
return makeParams(es, es.getLambda(), problem, randSeed, makeDefaultTerminator());
return makeParams(es, es.getLambda(), problem, randSeed, getTerminator());
}
/**
@ -715,7 +735,7 @@ public class OptimizerFactory {
* @return
*/
public static GOParameters makeParams(InterfaceOptimizer opt, int popSize, AbstractOptimizationProblem problem) {
return makeParams(opt, popSize, problem, randSeed, makeDefaultTerminator());
return makeParams(opt, popSize, problem, randSeed, getTerminator());
}
/**
@ -852,7 +872,7 @@ public class OptimizerFactory {
outputFilePrefix);
return (runnable != null) ? runnable.getBinarySolution() : null;
}
// ///////////////////////////// Optimize a given runnable
public static BitSet optimizeToBinary(OptimizerRunnable runnable) {
optimize(runnable);
@ -1175,12 +1195,12 @@ public class OptimizerFactory {
*/
public static final GOParameters hillClimbing(
AbstractOptimizationProblem problem, int popSize) {
return makeParams(new HillClimbing(), popSize, problem, randSeed, makeDefaultTerminator());
return makeParams(new HillClimbing(), popSize, problem, randSeed, getTerminator());
}
public static final GOParameters monteCarlo(
AbstractOptimizationProblem problem) {
return makeParams(new MonteCarloSearch(), 50, problem, randSeed, makeDefaultTerminator());
return makeParams(new MonteCarloSearch(), 50, problem, randSeed, getTerminator());
}
/**
@ -1202,6 +1222,27 @@ public class OptimizerFactory {
new ClusteringDensityBased(clusterSigma, minClustSize), haltingWindowLength, haltingWindowEpsilon, popSize);
}
/**
* A standard CBNPSO with density based clustering working on personal best positions.
*
* @param problem
* @return
*/
public static final GOParameters standardCbnPSO(AbstractOptimizationProblem problem) {
return createCbnPSO(problem, cbnDefaultClusterSigma, cbnDefaultMinGroupSize, cbnDefaultMaxGroupSize,
cbnDefaultHaltingWindowLength , cbnDefaultHaltingWindowEpsilon, 100 );
}
public static final GOParameters createCbnPSO(AbstractOptimizationProblem problem, double clusterSigma, int minClustSize,
int maxSpecSize, int haltingWindowLength, double haltingWindowEpsilon, int popSize ) {
GOParameters psoParams = standardPSO(problem);
ParticleSwarmOptimization pso = (ParticleSwarmOptimization)psoParams.getOptimizer();
ClusteringDensityBased clust = new ClusteringDensityBased(clusterSigma, minClustSize,
new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey));
return createCbn(problem, pso, clust, maxSpecSize, new ClusteringDensityBased(clust),
haltingWindowLength, haltingWindowEpsilon, popSize);
}
/**
* Create a generic Clustering-based Niching EA with given parameters.
*
@ -1226,7 +1267,7 @@ public class OptimizerFactory {
cbn.setShowCycle(0); // don't do graphical output
cbn.setHaltingWindow(haltingWindowLength);
cbn.setEpsilonBound(haltingWindowEpsilon);
return makeParams(cbn, popSize, problem, randSeed, makeDefaultTerminator());
return makeParams(cbn, popSize, problem, randSeed, getTerminator());
}
/**
@ -1265,24 +1306,11 @@ public class OptimizerFactory {
return createCbn(problem, ga, cbnDefaultClusterSigma, cbnDefaultMinGroupSize, cbnDefaultMaxGroupSize, cbnDefaultHaltingWindowLength, cbnDefaultHaltingWindowEpsilon, 100);
}
/**
* A standard CBNPSO with density based clustering working on personal best positions.
*
* @param problem
* @return
*/
public static final GOParameters standardCbnPSO(AbstractOptimizationProblem problem) {
GOParameters psoParams = standardPSO(problem);
ParticleSwarmOptimization pso = (ParticleSwarmOptimization)psoParams.getOptimizer();
ClusteringDensityBased clust = new ClusteringDensityBased(cbnDefaultClusterSigma, cbnDefaultMinGroupSize, new IndividualDataMetric(ParticleSwarmOptimization.partBestPosKey));
return createCbn(problem, pso, clust, cbnDefaultMaxGroupSize, new ClusteringDensityBased(clust), cbnDefaultHaltingWindowLength, cbnDefaultHaltingWindowEpsilon, 100);
}
public static final GOParameters standardPBIL(AbstractOptimizationProblem problem) {
PopulationBasedIncrementalLearning pbil = createPBIL(0.04, 0.01, 0.5, 1,
new SelectBestIndividuals(), 50, problem, null);
return makeParams(pbil, pbil.getPopulation(), problem, randSeed, makeDefaultTerminator());
return makeParams(pbil, pbil.getPopulation(), problem, randSeed, getTerminator());
}
/**
@ -1347,7 +1375,7 @@ public class OptimizerFactory {
chc.setNotifyGuiEvery(0);
chc.setStepSizeThreshold(hcStepThresh);
chc.setSigmaClust(sigmaClust);
return makeParams(chc, popSize, problem, randSeed, makeDefaultTerminator());
return makeParams(chc, popSize, problem, randSeed, getTerminator());
}
@ -1410,7 +1438,7 @@ public class OptimizerFactory {
public static final GOParameters standardNMS(AbstractOptimizationProblem problem) {
NelderMeadSimplex nms = NelderMeadSimplex.createNelderMeadSimplex(problem, null);
return makeParams(nms, 50, problem, randSeed, makeDefaultTerminator());
return makeParams(nms, 50, problem, randSeed, getTerminator());
}
/**
@ -1427,7 +1455,7 @@ public class OptimizerFactory {
de.setK(0.6);
de.setLambda(0.6);
de.setMt(0.05); // this is not really employed for currentToBest
return makeParams(de, 50, problem, randSeed, makeDefaultTerminator());
return makeParams(de, 50, problem, randSeed, getTerminator());
}
public static final GOParameters standardES(
@ -1451,7 +1479,7 @@ public class OptimizerFactory {
GeneticAlgorithm ga = new GeneticAlgorithm();
ga.setElitism(true);
return makeParams(ga, 100, problem, randSeed, makeDefaultTerminator());
return makeParams(ga, 100, problem, randSeed, getTerminator());
}
/**
@ -1467,13 +1495,109 @@ public class OptimizerFactory {
// pso.getTopology().setSelectedTag("Grid");
pso.setTopology(PSOTopologyEnum.grid);
pso.setTopologyRange(1);
return makeParams(pso, 30, problem, randSeed, makeDefaultTerminator());
return makeParams(pso, 30, problem, randSeed, getTerminator());
}
public static final GOParameters tribes(AbstractOptimizationProblem problem) {
return makeParams(new Tribes(), 1, problem, randSeed, makeDefaultTerminator());
return makeParams(new Tribes(), 1, problem, randSeed, getTerminator());
}
/**
* A standard niching ES which employs predefined values and standard ES variation operators.
*
* @param problem
* @return
*/
public static final GOParameters standardNichingEs(AbstractOptimizationProblem prob) {
// nichingEs(AbstractOptimizationProblem prob, double nicheRadius, int muPerPeak, int lambdaPerPeak,
return createNichingEs(prob, -1, 100, 100,
// int expectedPeaks, int rndImmigrants, int explorerPeaks, int resetExplInterval, int etaPresel) {
10, 200, 0, 100, 50);
}
/**
* A niching ES.
*
* @param problem
* @return
*/
public static final GOParameters createNichingEs(AbstractOptimizationProblem prob, double nicheRadius, int muPerPeak, int lambdaPerPeak, int expectedPeaks, int rndImmigrants, int explorerPeaks, int resetExplInterval, int etaPresel) {
EsDpiNiching nes = new EsDpiNiching(nicheRadius, muPerPeak, lambdaPerPeak, expectedPeaks, rndImmigrants, explorerPeaks, resetExplInterval, etaPresel);
if (assertIndyType(prob, InterfaceESIndividual.class)) {
setTemplateOperators(prob, new MutateESGlobal(0.2, MutateESCrossoverTypeEnum.intermediate), 0.9, new CrossoverESDefault(), 0.2);
} else {
System.err.println("Error, standard ES is implemented for ES individuals only (requires double data types)");
return null;
}
// pop. size will be ignored by nes
return makeParams(nes, 100, prob, randSeed, getTerminator());
}
/**
* This actually employs the rank-mu CMA operator.
*
* @param prob
* @return
*/
public static GOParameters standardNichingCmaEs(AbstractOptimizationProblem prob) {
return createNichingCmaEs(prob, -1, 10, 10, 0, 0, -1);
}
/**
* This actually employs the rank-mu CMA operator.
*
* @param prob
* @return
*/
public static GOParameters createNichingCmaEs(AbstractOptimizationProblem prob, double nicheRad) {
return createNichingCmaEs(prob, nicheRad, 10, 10, 0, 0, -1);
}
/**
* This actually employs the rank-mu CMA operator.
*
* @param prob
* @return
*/
public static GOParameters createNichingCmaEs(AbstractOptimizationProblem prob, double nicheRad, double stagnationEpsilon) {
return createNichingCmaEs(prob, nicheRad, 10, 10, 0, 0, stagnationEpsilon);
}
/**
* This actually employs the rank-mu CMA operator.
*
* @param prob
* @return
*/
public static GOParameters createNichingCmaEs(AbstractOptimizationProblem prob, double nicheRad, int nicheCount, double stagnationEpsilon) {
return createNichingCmaEs(prob, nicheRad, 10, nicheCount, 0, 0, stagnationEpsilon);
}
/**
* A generic niching CMA-ES using the rank-mu CMA operator
* @param prob
* @param nicheRadius
* @param lambda
* @param expectedPeaks
* @param explorerPeaks
* @param resetExplInterval
* @return
*/
public static GOParameters createNichingCmaEs(AbstractOptimizationProblem prob, double nicheRadius, int lambda, int expectedPeaks, int explorerPeaks, int resetExplInterval, double stagnationEpsilon) {
EsDpiNiching nes = new EsDpiNichingCma(nicheRadius, lambda, expectedPeaks, explorerPeaks, resetExplInterval);
if (stagnationEpsilon>0) nes.setEpsilonBound(stagnationEpsilon);
if (assertIndyType(prob, InterfaceESIndividual.class)) {
setTemplateOperators(prob, new MutateESRankMuCMA(), 1., new CrossoverESDefault(), 0.);
} else {
System.err.println("Error, standard ES is implemented for ES individuals only (requires double data types)");
return null;
}
// pop. size will be ignored by nes
return makeParams(nes, 100, prob, randSeed, getTerminator());
}
/**
* Check if a given problem instance has a template individual matching the given class.
*