From 88879683a43c27f3965152c06568cd8140c0e57a Mon Sep 17 00:00:00 2001 From: Marcel Kronfeld Date: Thu, 19 Nov 2009 16:56:28 +0000 Subject: [PATCH] Some repairs (GradientDescentAlgorithm, F1 and F2 are now derivable); renamed Population.m_Size to m_TargetSize to avoid misunderstandings; Exceptions are now caught and their messages tried to display in a JOptionPane in addition to printing to System.err. --- src/eva2/OptimizerFactory.java | 14 +- src/eva2/server/go/GOStandaloneVersion.java | 4 +- src/eva2/server/go/MOCCOStandalone.java | 2 +- .../archiving/ArchivingAllDominating.java | 2 +- .../operators/archiving/ArchivingMaxiMin.java | 4 +- .../operators/archiving/ArchivingNSGAII.java | 8 +- .../operators/archiving/ArchivingPESAII.java | 2 +- .../operators/archiving/ArchivingSPEAII.java | 6 +- .../InformationRetrievalInserting.java | 4 +- .../InformationRetrievalReplacing.java | 2 +- ...oveSurplusIndividualsDynamicHyperCube.java | 2 +- ...moveSurplusIndividualsStaticHyperCube.java | 2 +- .../operators/cluster/ClusteringXMeans.java | 2 +- .../operators/crossover/TestESCrossover.java | 8 +- .../distancemetric/IndividualDataMetric.java | 75 ++++ .../migration/MOClusteringSeparation.java | 4 +- .../operators/migration/MOConeSeparation.java | 4 +- .../migration/MOXMeansSeparation.java | 8 +- .../operators/mutation/MutateESRankMuCMA.java | 1 + .../MetricSWithReference.java | 2 +- .../go/operators/postprocess/PostProcess.java | 8 +- .../go/operators/selection/SelectAll.java | 2 +- .../operators/selection/SelectBestSingle.java | 2 +- .../SelectMONSGAIICrowedTournament.java | 2 +- .../go/operators/selection/SelectMOPESA.java | 2 +- .../operators/selection/SelectMOPESAII.java | 2 +- .../selection/SelectParticleWheel.java | 2 +- .../go/operators/selection/SelectRandom.java | 2 +- .../operators/selection/SelectTournament.java | 2 +- .../selection/SelectXProbRouletteWheel.java | 2 +- .../server/go/populations/PBILPopulation.java | 10 +- .../server/go/populations/Population.java | 80 ++-- .../go/problems/AbstractDynTransProblem.java | 2 +- .../AbstractMultiModalProblemKnown.java | 2 +- ...ractMultiObjectiveOptimizationProblem.java | 85 +--- .../problems/AbstractOptimizationProblem.java | 42 +- .../go/problems/AbstractProblemBinary.java | 31 +- .../go/problems/AbstractProblemDouble.java | 12 +- .../problems/AbstractProblemDoubleOffset.java | 99 +++++ .../go/problems/ExternalRuntimeProblem.java | 13 +- src/eva2/server/go/problems/F10Problem.java | 2 +- src/eva2/server/go/problems/F11Problem.java | 2 +- src/eva2/server/go/problems/F12Problem.java | 2 +- src/eva2/server/go/problems/F13Problem.java | 2 +- src/eva2/server/go/problems/F14Problem.java | 2 +- src/eva2/server/go/problems/F1Problem.java | 87 +---- src/eva2/server/go/problems/F2Problem.java | 19 +- src/eva2/server/go/problems/F3Problem.java | 2 +- src/eva2/server/go/problems/F4Problem.java | 2 +- src/eva2/server/go/problems/F5Problem.java | 2 +- src/eva2/server/go/problems/F6Problem.java | 2 +- src/eva2/server/go/problems/F7Problem.java | 2 +- src/eva2/server/go/problems/F8Problem.java | 2 +- src/eva2/server/go/problems/F9Problem.java | 2 +- src/eva2/server/go/problems/FLensProblem.java | 12 +- .../InterfaceFirstOrderDerivableProblem.java | 19 +- .../go/problems/InterfaceProgramProblem.java | 2 +- .../server/go/problems/MatlabProblem.java | 13 +- .../go/problems/PSymbolicRegression.java | 13 +- .../go/problems/SimpleProblemWrapper.java | 14 +- src/eva2/server/go/problems/TF1Problem.java | 13 +- .../CHCAdaptiveSearchAlgorithm.java | 6 +- .../go/strategies/ClusterBasedNichingEA.java | 36 +- .../go/strategies/ClusteringHillClimbing.java | 73 ++-- .../go/strategies/EvolutionStrategies.java | 10 +- .../server/go/strategies/FloodAlgorithm.java | 2 +- .../go/strategies/GeneticAlgorithm.java | 6 +- .../strategies/GradientDescentAlgorithm.java | 368 ++++++++++-------- .../server/go/strategies/HillClimbing.java | 2 +- .../server/go/strategies/IslandModelEA.java | 2 +- .../go/strategies/MonteCarloSearch.java | 2 +- .../go/strategies/MultiObjectiveEA.java | 6 +- .../go/strategies/NelderMeadSimplex.java | 6 +- .../ParticleFilterOptimization.java | 14 +- .../strategies/ParticleSwarmOptimization.java | 7 +- .../go/strategies/SimulatedAnnealing.java | 2 +- .../go/strategies/ThresholdAlgorithm.java | 2 +- .../go/strategies/tribes/TribesSwarm.java | 2 +- src/eva2/server/modules/MOEAParameters.java | 2 +- src/eva2/server/modules/Processor.java | 17 +- src/eva2/tools/StringTools.java | 48 +++ 81 files changed, 755 insertions(+), 632 deletions(-) create mode 100644 src/eva2/server/go/operators/distancemetric/IndividualDataMetric.java create mode 100644 src/eva2/server/go/problems/AbstractProblemDoubleOffset.java diff --git a/src/eva2/OptimizerFactory.java b/src/eva2/OptimizerFactory.java index b4abd972..afde0b02 100644 --- a/src/eva2/OptimizerFactory.java +++ b/src/eva2/OptimizerFactory.java @@ -143,7 +143,7 @@ public class OptimizerFactory { DifferentialEvolution de = new DifferentialEvolution(); de.SetProblem(problem); - de.getPopulation().setPopulationSize(popsize); + de.getPopulation().setTargetSize(popsize); de.setDEType(DETypeEnum.DE2_CurrentToBest); de.setF(f); de.setK(CR); @@ -260,7 +260,7 @@ public class OptimizerFactory { GeneticAlgorithm ga = new GeneticAlgorithm(); ga.SetProblem(problem); - ga.getPopulation().setPopulationSize(popsize); + ga.getPopulation().setTargetSize(popsize); ga.setParentSelection(select); ga.setPartnerSelection(select); ga.addPopulationChangedEventListener(listener); @@ -374,7 +374,7 @@ public class OptimizerFactory { tmpIndi.setCrossoverProbability(0); HillClimbing hc = new HillClimbing(); - hc.getPopulation().setPopulationSize(pop); + hc.getPopulation().setTargetSize(pop); hc.addPopulationChangedEventListener(listener); hc.SetProblem(problem); hc.init(); @@ -405,7 +405,7 @@ public class OptimizerFactory { tmpIndi.setCrossoverProbability(0); MonteCarloSearch mc = new MonteCarloSearch(); - mc.getPopulation().setPopulationSize(popsize); + mc.getPopulation().setTargetSize(popsize); mc.addPopulationChangedEventListener(listener); mc.SetProblem(problem); mc.init(); @@ -447,7 +447,7 @@ public class OptimizerFactory { ParticleSwarmOptimization pso = new ParticleSwarmOptimization(); pso.SetProblem(problem); - pso.getPopulation().setPopulationSize(popsize); + pso.getPopulation().setTargetSize(popsize); pso.setPhi1(phi1); pso.setPhi2(phi2); pso.setSpeedLimit(k); @@ -493,7 +493,7 @@ public class OptimizerFactory { sa.setAlpha(alpha); sa.setInitialTemperature(temperature); sa.SetProblem(problem); - sa.getPopulation().setPopulationSize(popsize); + sa.getPopulation().setTargetSize(popsize); sa.addPopulationChangedEventListener(listener); sa.init(); @@ -732,7 +732,7 @@ public class OptimizerFactory { * @return */ public static GOParameters makeParams(InterfaceOptimizer opt, AbstractOptimizationProblem problem, InterfaceTerminator term) { - return makeParams(opt, opt.getPopulation().getPopulationSize(), problem, randSeed, term); + return makeParams(opt, opt.getPopulation().getTargetSize(), problem, randSeed, term); } /** * Set the population size, initialize the population and return a parameter structure containing all diff --git a/src/eva2/server/go/GOStandaloneVersion.java b/src/eva2/server/go/GOStandaloneVersion.java index f70960cb..09e36d6a 100644 --- a/src/eva2/server/go/GOStandaloneVersion.java +++ b/src/eva2/server/go/GOStandaloneVersion.java @@ -317,7 +317,7 @@ public class GOStandaloneVersion implements InterfaceGOStandalone, InterfacePopu ga.setParentSelection(tour); ga.setPartnerSelection(tour); this.m_GO.setOptimizer(ga); - this.m_GO.getOptimizer().getPopulation().setPopulationSize(100); + this.m_GO.getOptimizer().getPopulation().setTargetSize(100); F1Problem problem = new F1Problem(); tmpIndy = new GAIndividualDoubleData(); ((GAIndividualDoubleData)tmpIndy).setCrossoverOperator(new CrossoverGANPoint()); @@ -335,7 +335,7 @@ public class GOStandaloneVersion implements InterfaceGOStandalone, InterfacePopu this.m_OutputFileName = "X360_StandardES"; EvolutionStrategies es = new EvolutionStrategies(); this.m_GO.setOptimizer(es); - this.m_GO.getOptimizer().getPopulation().setPopulationSize(50); + this.m_GO.getOptimizer().getPopulation().setTargetSize(50); F1Problem problem = new F1Problem(); tmpIndy = new ESIndividualDoubleData(); ((AbstractEAIndividual)tmpIndy).setMutationOperator(new MutateESLocal()); diff --git a/src/eva2/server/go/MOCCOStandalone.java b/src/eva2/server/go/MOCCOStandalone.java index a12931c5..ed9f058e 100644 --- a/src/eva2/server/go/MOCCOStandalone.java +++ b/src/eva2/server/go/MOCCOStandalone.java @@ -128,7 +128,7 @@ public class MOCCOStandalone implements InterfaceGOStandalone, InterfacePopulati while (!tmpP.isFinished()) { try { Thread.sleep(1000); } catch (java.lang.InterruptedException e) { }} this.m_State.m_InitialPopulationSize = Math.max(1, this.m_State.m_InitialPopulationSize); Population pop = new Population(); - pop.setPopulationSize(this.m_State.m_InitialPopulationSize); + pop.setTargetSize(this.m_State.m_InitialPopulationSize); this.m_State.m_CurrentProblem = (InterfaceOptimizationProblem) this.m_State.m_OriginalProblem.clone(); this.m_State.m_CurrentProblem.initPopulation(pop); this.m_State.m_CurrentProblem.evaluate(pop); diff --git a/src/eva2/server/go/operators/archiving/ArchivingAllDominating.java b/src/eva2/server/go/operators/archiving/ArchivingAllDominating.java index abe4496e..96df0779 100644 --- a/src/eva2/server/go/operators/archiving/ArchivingAllDominating.java +++ b/src/eva2/server/go/operators/archiving/ArchivingAllDominating.java @@ -46,7 +46,7 @@ public class ArchivingAllDominating extends AbstractArchiving implements java.io if ((pop.getArchive().size() == 0) && (pop.size() > 0)) { SelectBestIndividuals select = new SelectBestIndividuals(); select.setObeyDebsConstViolationPrinciple(true); - pop.getArchive().addPopulation(select.selectFrom(pop, pop.getArchive().getPopulationSize())); + pop.getArchive().addPopulation(select.selectFrom(pop, pop.getArchive().getTargetSize())); } } else { // test for each element in population if it diff --git a/src/eva2/server/go/operators/archiving/ArchivingMaxiMin.java b/src/eva2/server/go/operators/archiving/ArchivingMaxiMin.java index 7a5a7d49..00d88f81 100644 --- a/src/eva2/server/go/operators/archiving/ArchivingMaxiMin.java +++ b/src/eva2/server/go/operators/archiving/ArchivingMaxiMin.java @@ -59,8 +59,8 @@ public class ArchivingMaxiMin implements InterfaceArchiving, java.io.Serializabl this.m_MaxiMin.convertMultiObjective2SingleObjective(tmpPop); this.m_Selection.setObeyDebsConstViolationPrinciple(this.m_ObeyDebsConstViolationPrinciple); this.m_Selection.prepareSelection(tmpPop); - archive = this.m_Selection.selectFrom(tmpPop, pop.getArchive().getPopulationSize()); - archive.setPopulationSize(pop.getArchive().getPopulationSize()); + archive = this.m_Selection.selectFrom(tmpPop, pop.getArchive().getTargetSize()); + archive.setTargetSize(pop.getArchive().getTargetSize()); // now unconvert from SO to MO for (int i = 0; i < archive.size(); i++) { diff --git a/src/eva2/server/go/operators/archiving/ArchivingNSGAII.java b/src/eva2/server/go/operators/archiving/ArchivingNSGAII.java index b448e0fa..0edd122c 100644 --- a/src/eva2/server/go/operators/archiving/ArchivingNSGAII.java +++ b/src/eva2/server/go/operators/archiving/ArchivingNSGAII.java @@ -74,21 +74,21 @@ public class ArchivingNSGAII extends ArchivingNSGA implements java.io.Serializab // Now init the new archive Population archive = new Population(); - archive.setPopulationSize(pop.getArchive().getPopulationSize()); + archive.setTargetSize(pop.getArchive().getTargetSize()); // Now add the fronts to the archive int index = 0; - while ((index < fronts.length) && ((archive.size() + fronts[index].size()) < archive.getPopulationSize())) { + while ((index < fronts.length) && ((archive.size() + fronts[index].size()) < archive.getTargetSize())) { archive.addPopulation(fronts[index]); index++; } - if ((index < fronts.length) && (archive.size() < archive.getPopulationSize())) { + if ((index < fronts.length) && (!archive.targetSizeReached())) { // In this case there are still elements left in the front which could be added to the archive! // and there is still some place left in the archive // therefore we could add some individuals from front[index] // to the archive using the crowding distance sorting - fronts[index].setPopulationSize(archive.getPopulationSize() - archive.size()); + fronts[index].setTargetSize(archive.getTargetSize() - archive.size()); this.m_Cleaner.removeSurplusIndividuals(fronts[index]); archive.addPopulation(fronts[index]); } diff --git a/src/eva2/server/go/operators/archiving/ArchivingPESAII.java b/src/eva2/server/go/operators/archiving/ArchivingPESAII.java index 3fe5ffcb..db6e275e 100644 --- a/src/eva2/server/go/operators/archiving/ArchivingPESAII.java +++ b/src/eva2/server/go/operators/archiving/ArchivingPESAII.java @@ -136,7 +136,7 @@ public class ArchivingPESAII extends AbstractArchiving implements java.io.Serial // Now check whether there are individuals to remove int bigSqueeze, index; int[] squeezeFactor; - while(archive.size() > archive.getPopulationSize()) { + while(archive.targetSizeExceeded()) { squeezeFactor = this.calculateSqueezeFactor(archive); bigSqueeze = 0; index = -1; diff --git a/src/eva2/server/go/operators/archiving/ArchivingSPEAII.java b/src/eva2/server/go/operators/archiving/ArchivingSPEAII.java index 845f9578..e0af5cc3 100644 --- a/src/eva2/server/go/operators/archiving/ArchivingSPEAII.java +++ b/src/eva2/server/go/operators/archiving/ArchivingSPEAII.java @@ -98,7 +98,7 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial // Now init the new archive Population archive = new Population(); - archive.setPopulationSize(pop.getArchive().getPopulationSize()); + archive.setTargetSize(pop.getArchive().getTargetSize()); // archive = this.m_Selection.selectFrom(tmpPop, archive.getPopulationSize()); @@ -160,7 +160,7 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial // if there is some place left let's add some more int currentLevel = 0; - while (archive.size() < archive.getPopulationSize()) { + while (!archive.targetSizeReached()) { currentLevel++; for (int i = 0; i < RawFitness.length; i++) { if ((RawFitness[i] >= currentLevel) && (RawFitness[i] < currentLevel +1)) { @@ -194,7 +194,7 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial // RawFitness for the lowest kthDistance()!!! int ICurSma; double curSmall, highestLevel; - while (archive.size() > archive.getPopulationSize()) { + while (archive.targetSizeExceeded()) { highestLevel = 0; RawFitness = this.calculateRawFitness(archive); for (int i = 0; i < RawFitness.length; i++) { diff --git a/src/eva2/server/go/operators/archiving/InformationRetrievalInserting.java b/src/eva2/server/go/operators/archiving/InformationRetrievalInserting.java index 68f03135..e24cae6f 100644 --- a/src/eva2/server/go/operators/archiving/InformationRetrievalInserting.java +++ b/src/eva2/server/go/operators/archiving/InformationRetrievalInserting.java @@ -31,9 +31,9 @@ public class InformationRetrievalInserting implements InterfaceInformationRetrie Population archive = pop.getArchive(); if (archive == null) return; - if (archive.size() < pop.getPopulationSize()) { + if (archive.size() < pop.getTargetSize()) { // remove archive size individuals from pop - pop.removeNIndividuals(archive.size()-(pop.getPopulationSize()-pop.size())); + pop.removeNIndividuals(archive.size()-(pop.getTargetSize()-pop.size())); } else { pop.clear(); } diff --git a/src/eva2/server/go/operators/archiving/InformationRetrievalReplacing.java b/src/eva2/server/go/operators/archiving/InformationRetrievalReplacing.java index c2254d32..a44a4938 100644 --- a/src/eva2/server/go/operators/archiving/InformationRetrievalReplacing.java +++ b/src/eva2/server/go/operators/archiving/InformationRetrievalReplacing.java @@ -33,7 +33,7 @@ public class InformationRetrievalReplacing implements InterfaceInformationRetrie Population tmp = new Population(); tmp.addPopulation(archive); - while (tmp.size() < archive.getPopulationSize()) { + while (tmp.size() < archive.getTargetSize()) { tmp.add(pop.get(RNG.randomInt(0,pop.size()-1))); } diff --git a/src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsDynamicHyperCube.java b/src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsDynamicHyperCube.java index 5a4f3aac..a092436f 100644 --- a/src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsDynamicHyperCube.java +++ b/src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsDynamicHyperCube.java @@ -35,7 +35,7 @@ public class RemoveSurplusIndividualsDynamicHyperCube implements InterfaceRemove double[][] fitness; double[] space; int indexSmallHyperCube; - while(archive.size() > archive.getPopulationSize()) { + while(archive.targetSizeExceeded()) { // select the individual with the least space around him // to do this i got to find the next smaller and the next bigger one fitness = new double[archive.size()][]; diff --git a/src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsStaticHyperCube.java b/src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsStaticHyperCube.java index be55b139..7c2b0b72 100644 --- a/src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsStaticHyperCube.java +++ b/src/eva2/server/go/operators/archiving/RemoveSurplusIndividualsStaticHyperCube.java @@ -47,7 +47,7 @@ public class RemoveSurplusIndividualsStaticHyperCube extends RemoveSurplusIndivi ((AbstractEAIndividual)archive.get(i)).putData("HyperCube", new Double(space[i])); } - while(archive.size() > archive.getPopulationSize()) { + while(archive.targetSizeExceeded()) { // select the individual with the least space around him // to do this i got to find the next smaller and the next bigger one smallestHyperCube = ((Double)((AbstractEAIndividual)archive.get(0)).getData("HyperCube")).doubleValue(); diff --git a/src/eva2/server/go/operators/cluster/ClusteringXMeans.java b/src/eva2/server/go/operators/cluster/ClusteringXMeans.java index f38be480..4222e524 100644 --- a/src/eva2/server/go/operators/cluster/ClusteringXMeans.java +++ b/src/eva2/server/go/operators/cluster/ClusteringXMeans.java @@ -273,7 +273,7 @@ public class ClusteringXMeans implements InterfaceClustering, java.io.Serializab ckm.setUseSearchSpace(true); ckm.m_Debug = true; Population pop = new Population(); - pop.setPopulationSize(100); + pop.setTargetSize(100); F1Problem f1 = new F1Problem(); f1.setProblemDimension(2); f1.setEAIndividual(new ESIndividualDoubleData()); diff --git a/src/eva2/server/go/operators/crossover/TestESCrossover.java b/src/eva2/server/go/operators/crossover/TestESCrossover.java index 6970f559..76816478 100644 --- a/src/eva2/server/go/operators/crossover/TestESCrossover.java +++ b/src/eva2/server/go/operators/crossover/TestESCrossover.java @@ -96,7 +96,7 @@ public class TestESCrossover implements java.io.Serializable { ActionListener initListener = new ActionListener() { public void actionPerformed(ActionEvent event) { m_Partners = new Population(); - m_Partners.setPopulationSize(m_NumberOfPartners); + m_Partners.setTargetSize(m_NumberOfPartners); m_Partners.clear(); InterfaceDataTypeDouble tmpIndyD = new ESIndividualDoubleData(); @@ -108,7 +108,7 @@ public class TestESCrossover implements java.io.Serializable { } tmpIndyD.setDoubleDataLength(m_Dimension); tmpIndyD.SetDoubleRange(newRange); - for (int i = 0; i < m_Partners.getPopulationSize(); i++) { + for (int i = 0; i < m_Partners.getTargetSize(); i++) { tmpIndyEA = (AbstractEAIndividual)((AbstractEAIndividual)tmpIndyD).clone(); tmpIndyEA.init(m_Problem); m_Partners.add(tmpIndyEA); @@ -134,7 +134,7 @@ public class TestESCrossover implements java.io.Serializable { ActionListener init2Listener = new ActionListener() { public void actionPerformed(ActionEvent event) { m_Partners = new Population(); - m_Partners.setPopulationSize(2); + m_Partners.setTargetSize(2); m_Partners.clear(); InterfaceDataTypeDouble tmpIndyD = new ESIndividualDoubleData(); @@ -177,7 +177,7 @@ public class TestESCrossover implements java.io.Serializable { ActionListener init3Listener = new ActionListener() { public void actionPerformed(ActionEvent event) { m_Partners = new Population(); - m_Partners.setPopulationSize(3); + m_Partners.setTargetSize(3); m_Partners.clear(); InterfaceDataTypeDouble tmpIndyD = new ESIndividualDoubleData(); diff --git a/src/eva2/server/go/operators/distancemetric/IndividualDataMetric.java b/src/eva2/server/go/operators/distancemetric/IndividualDataMetric.java new file mode 100644 index 00000000..46833cf8 --- /dev/null +++ b/src/eva2/server/go/operators/distancemetric/IndividualDataMetric.java @@ -0,0 +1,75 @@ +package eva2.server.go.operators.distancemetric; + +import java.io.Serializable; + +import eva2.server.go.individuals.AbstractEAIndividual; +import eva2.server.go.individuals.InterfaceDataTypeDouble; +import eva2.server.go.strategies.ParticleSwarmOptimization; + +/** + * Define a metric on data stored within individuals, such as the personal best position + * in PSO. + * @author mkron + * + */ +public class IndividualDataMetric implements InterfaceDistanceMetric, Serializable { + private String dataKey = ParticleSwarmOptimization.partBestPosKey; + private boolean normedDistance = true; // flag whether to use normed distances (for InterfaceDataTypeDouble) + + public IndividualDataMetric() {} + + public IndividualDataMetric(String key) { + dataKey = key; + } + + public IndividualDataMetric(IndividualDataMetric pBestMetric) { + // TODO Auto-generated constructor stub + } + + /** This method allows you to make a deep clone of + * the object + * @return the deep clone + */ + public Object clone() { + return new IndividualDataMetric(this); + } + + public double distance(AbstractEAIndividual indy1, AbstractEAIndividual indy2) { + if (dataKey==null) throw new RuntimeException("Error, no data key defined in " + this.getClass().getName() + "::distance()"); + else { + Object data1 = indy1.getData(dataKey); + Object data2 = indy2.getData(dataKey); + if (data1 instanceof double[] && (data2 instanceof double[])) { + if (normedDistance) { + double[][] range1 = ((InterfaceDataTypeDouble)indy1).getDoubleRange(); + double[][] range2 = ((InterfaceDataTypeDouble)indy2).getDoubleRange(); + return EuclideanMetric.normedEuclideanDistance((double[])data1, range1, (double[])data2, range2); + } else return EuclideanMetric.euclideanDistance((double[])data1, (double[])data2); + } else throw new RuntimeException("Error, invalid key data, double array required by " + this.getClass().getName()); + } + } + + public String dataKeyTipText() { + return "Name of the data key to use to retrieve individual data (double[] for now)."; + } + public String getDataKey() { + return dataKey; + } + public void setDataKey(String dataKey) { + this.dataKey = dataKey; + } + + public String normedDistanceTipText() { + return "Flag whether to use euclidean distance directly or normed by the double range."; + } + public boolean isNormedDistance() { + return normedDistance; + } + public void setNormedDistance(boolean normedDistance) { + this.normedDistance = normedDistance; + } + + public String globalInfo() { + return "Uses individual object data (so far only double[]) to calculate the distance."; + } +} diff --git a/src/eva2/server/go/operators/migration/MOClusteringSeparation.java b/src/eva2/server/go/operators/migration/MOClusteringSeparation.java index f30145bd..8036b843 100644 --- a/src/eva2/server/go/operators/migration/MOClusteringSeparation.java +++ b/src/eva2/server/go/operators/migration/MOClusteringSeparation.java @@ -217,8 +217,8 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria oldIPOP[i].clear(); oldIPOP[i].addPopulation(newIPOP[i]); // todo remove this for nice pictures - if (oldIPOP[i].size() < oldIPOP[i].getPopulationSize()) { - oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getPopulationSize()-oldIPOP[i].size())); + if (!oldIPOP[i].targetSizeReached()) { + oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getTargetSize()-oldIPOP[i].size())); } if (this.m_Debug) System.out.println("Setting "+i+" to population size " + oldIPOP[i].size()); islands[i].setPopulation(oldIPOP[i]); diff --git a/src/eva2/server/go/operators/migration/MOConeSeparation.java b/src/eva2/server/go/operators/migration/MOConeSeparation.java index bd4a13a6..659a2346 100644 --- a/src/eva2/server/go/operators/migration/MOConeSeparation.java +++ b/src/eva2/server/go/operators/migration/MOConeSeparation.java @@ -111,8 +111,8 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl oldIPOP[i].clear(); oldIPOP[i].addPopulation(newIPOP[i]); // todo remove this for nice pictures - if (oldIPOP[i].size() < oldIPOP[i].getPopulationSize()) { - oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getPopulationSize()-oldIPOP[i].size())); + if (!oldIPOP[i].targetSizeReached()) { + oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getTargetSize()-oldIPOP[i].size())); } if (this.m_Debug) System.out.println("Setting island "+i+" to population size " + oldIPOP[i].size()); allDom.addElementsToArchive(oldIPOP[i]); diff --git a/src/eva2/server/go/operators/migration/MOXMeansSeparation.java b/src/eva2/server/go/operators/migration/MOXMeansSeparation.java index f73929b1..ca3794cf 100644 --- a/src/eva2/server/go/operators/migration/MOXMeansSeparation.java +++ b/src/eva2/server/go/operators/migration/MOXMeansSeparation.java @@ -137,7 +137,7 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa newIPOP = this.m_XMeans.cluster(collector, c); for (int i = 0; i < islands.length; i++) { islands[i].getPopulation().clear(); - islands[i].getPopulation().setPopulationSize(0); + islands[i].getPopulation().setTargetSize(0); } if (this.m_Debug) { Plot plot; @@ -218,12 +218,12 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa oldIPOP[i].clear(); oldIPOP[i].addPopulation(newIPOP[i]); // todo remove this for nice pictures - if (oldIPOP[i].size() < oldIPOP[i].getPopulationSize()) { - oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getPopulationSize()-oldIPOP[i].size())); + if (!oldIPOP[i].targetSizeReached()) { + oldIPOP[i].addPopulation(this.m_Selection.selectFrom(memory, oldIPOP[i].getFreeSlots())); } if (this.m_Debug) System.out.println("Setting "+i+" to population size " + oldIPOP[i].size()); islands[i].setPopulation(oldIPOP[i]); - islands[i].getPopulation().setPopulationSize(oldIPOP[i].size()); + islands[i].getPopulation().setTargetSize(oldIPOP[i].size()); } } diff --git a/src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java b/src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java index 1763c671..d9b4baba 100644 --- a/src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java +++ b/src/eva2/server/go/operators/mutation/MutateESRankMuCMA.java @@ -413,6 +413,7 @@ public class MutateESRankMuCMA implements InterfaceMutationGenerational, Seriali } if (TRACE_1) { + System.out.println("sigma=" + params.sigma); System.out.print("psLen=" + (psNorm) + " "); outputParams(params, mu); } diff --git a/src/eva2/server/go/operators/paretofrontmetrics/MetricSWithReference.java b/src/eva2/server/go/operators/paretofrontmetrics/MetricSWithReference.java index 7c345050..61d1deef 100644 --- a/src/eva2/server/go/operators/paretofrontmetrics/MetricSWithReference.java +++ b/src/eva2/server/go/operators/paretofrontmetrics/MetricSWithReference.java @@ -105,7 +105,7 @@ public class MetricSWithReference implements InterfaceParetoFrontMetric, java.io if (this.m_ReferenceSMetric < 0) { Population tmpPop = new Population(); AbstractEAIndividual tmpIndy; - tmpPop.setPopulationSize(this.m_Reference.length); + tmpPop.setTargetSize(this.m_Reference.length); tmpPop.clear(); for (int i = 0; i < this.m_Reference.length; i++) { tmpIndy = new ESIndividualDoubleData(); diff --git a/src/eva2/server/go/operators/postprocess/PostProcess.java b/src/eva2/server/go/operators/postprocess/PostProcess.java index 967f66e8..98c438f1 100644 --- a/src/eva2/server/go/operators/postprocess/PostProcess.java +++ b/src/eva2/server/go/operators/postprocess/PostProcess.java @@ -134,7 +134,7 @@ public class PostProcess { for (int i=0; i listeners = null; @@ -86,7 +86,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea */ public Population(int initialCapacity) { super(initialCapacity); - setPopulationSize(initialCapacity); + setTargetSize(initialCapacity); } public Population(Population population) { @@ -110,7 +110,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea public void setSameParams(Population population) { this.m_Generation = population.m_Generation; this.m_FunctionCalls = population.m_FunctionCalls; - this.m_Size = population.m_Size; + this.m_TargetSize = population.m_TargetSize; this.useHistory = population.useHistory; this.notifyEvalInterval = population.notifyEvalInterval; // this.m_Listener = population.m_Listener; @@ -196,15 +196,13 @@ public class Population extends ArrayList implements PopulationInterface, Clonea * are reset and m_Size default Individuals are created and initialized by * the GAIndividual default init() method. */ - public void defaultInitPopulation() { - GAIndividualBinaryData tmpIndy; - + public void defaultInit(AbstractEAIndividual template) { this.m_Generation = 0; this.m_FunctionCalls = 0; this.m_Archive = null; this.clear(); - for (int i = 0; i < this.m_Size; i++) { - tmpIndy = new GAIndividualBinaryData(); + for (int i = 0; i < this.m_TargetSize; i++) { + AbstractEAIndividual tmpIndy = (AbstractEAIndividual)template.clone(); tmpIndy.defaultInit(); super.add(tmpIndy); } @@ -239,8 +237,8 @@ public class Population extends ArrayList implements PopulationInterface, Clonea return; } AbstractEAIndividual template = pop.getEAIndividual(0); - if (fillPop && (pop.size() maxDist) maxDist = d; } } res[1] = minDist; res[2] = maxDist; - if (this.size() > 1) res[0] = meanDist / (this.size() * (this.size()-1) / 2); + if (this.size() > 1) res[0] = distSum / (this.size() * (this.size()-1) / 2); else { // only one indy? res[1]=0; res[2]=0; @@ -1496,14 +1494,14 @@ public class Population extends ArrayList implements PopulationInterface, Clonea * this method cannot grow, of course. */ public void fitToSize() { - if (size() != getPopulationSize()) { - while (size() > getPopulationSize()) remove(size()-1); - if (size() < getPopulationSize()) { + if (size() != getTargetSize()) { + while (size() > getTargetSize()) remove(size()-1); + if (size() < getTargetSize()) { if (size() == 0) System.err.println("Cannot grow empty population!"); else { int origSize=size(); int k=0; - while (size()< getPopulationSize()) { + while (size()< getTargetSize()) { addIndividual((AbstractEAIndividual)getEAIndividual(k%origSize).clone()); } } @@ -1530,7 +1528,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea * */ public void synchSize() { - setPopulationSize(size()); + setTargetSize(size()); } /** @@ -1577,6 +1575,30 @@ public class Population extends ArrayList implements PopulationInterface, Clonea // else return ((hashes.head().equals(evaluationTimeHashes.head())) && (hashes.tail().equals(evaluationTimeHashes.tail())) && (evaluationTimeModCount == modCount)); // } + /** + * Return true if the targeted size of the population has been reached. + */ + public boolean targetSizeReached() { + return this.size()>=this.getTargetSize(); + } + + /** + * Return true if the targeted size of the population has been exceeded. + */ + public boolean targetSizeExceeded() { + return this.size()>this.getTargetSize(); + } + + /** + * Return the number of free individual slots until the target size is reached. + * Returns zero if the target size is already reached or exceeded. + * + * @return + */ + public int getFreeSlots() { + return Math.max(0, this.getTargetSize() - this.size()); + } + // /** // * Mark the population at the current state as evaluated. Changes to the modCount or hashes of individuals // * will invalidate the mark. diff --git a/src/eva2/server/go/problems/AbstractDynTransProblem.java b/src/eva2/server/go/problems/AbstractDynTransProblem.java index 556005b8..ff652cdf 100644 --- a/src/eva2/server/go/problems/AbstractDynTransProblem.java +++ b/src/eva2/server/go/problems/AbstractDynTransProblem.java @@ -158,7 +158,7 @@ public abstract class AbstractDynTransProblem extends AbstractSynchronousOptimiz this.prob = prob; /* to get the right values for problemDimension and Range */ Population pop = new Population(); - pop.setPopulationSize(1); + pop.setTargetSize(1); prob.initPopulation(pop); AbstractEAIndividual indy = (AbstractEAIndividual)pop.get(0); if (indy instanceof InterfaceDataTypeDouble) { diff --git a/src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java b/src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java index fd7dfdb7..5c01570b 100644 --- a/src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java +++ b/src/eva2/server/go/problems/AbstractMultiModalProblemKnown.java @@ -79,7 +79,7 @@ public abstract class AbstractMultiModalProblemKnown extends AbstractProblemDoub // this.m_ProblemDimension = 2; ((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.m_ProblemDimension); ((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(makeRange()); - for (int i = 0; i < population.getPopulationSize(); i++) { + for (int i = 0; i < population.getTargetSize(); i++) { tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone(); tmpIndy.init(this); population.add(tmpIndy); diff --git a/src/eva2/server/go/problems/AbstractMultiObjectiveOptimizationProblem.java b/src/eva2/server/go/problems/AbstractMultiObjectiveOptimizationProblem.java index 385c2e2b..caf4d7ff 100644 --- a/src/eva2/server/go/problems/AbstractMultiObjectiveOptimizationProblem.java +++ b/src/eva2/server/go/problems/AbstractMultiObjectiveOptimizationProblem.java @@ -181,68 +181,22 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract public void resetParetoFront() { this.m_ParetoFront = new Population(); } - - /** This method evaluates a given population and set the fitness values - * accordingly - * @param population The population that is to be evaluated. - */ - public void evaluate(Population population) { - AbstractEAIndividual tmpIndy; - double[] fitness; - - evaluatePopulationStart(population); - - - if (this.parallelthreads > 1) { - Vector queue = new Vector(); - Vector finished = new Vector(); - queue.addAll(population); - /* Semaphore available=new Semaphore(parallelthreads); - while (finished.size() < population.size()) { - try { - available.acquire(); - } catch (InterruptedException e) { - // TODO Auto-generated catch block - e.printStackTrace(); - } - if ((population.size()-(queue.size() + finished.size())) < parallelthreads) { - if (queue.size() > 0) { - AbstractEAIndividual tmpindy = queue.get(0); - queue.remove(0); - tmpindy.resetConstraintViolation(); - MultiObjectiveEvalThread evalthread = new MultiObjectiveEvalThread(this,tmpindy,finished,population,available); - evalthread.start(); - - } - } - } - */ - Semaphore sema=new Semaphore(0); - ExecutorService pool = Executors.newFixedThreadPool(parallelthreads); - for (int i = 0; i < population.size(); i++){ - AbstractEAIndividual tmpindy = (AbstractEAIndividual)population.get(i); - tmpindy.resetConstraintViolation(); - EvalThread evalthread = new EvalThread(this,tmpindy,finished,population,sema); - pool.execute(evalthread); - - } - try { - sema.acquire(population.size()); - } catch (InterruptedException e) { - // TODO Auto-generated catch block - e.printStackTrace(); - } - pool.shutdownNow(); - }else { - // first evaluate the population - for (int i = 0; i < population.size(); i++) { - tmpIndy = (AbstractEAIndividual) population.get(i); - tmpIndy.resetConstraintViolation(); - this.evaluate(tmpIndy); + + public void evaluatePopulationStart(Population population) { + super.evaluatePopulationStart(population); + if (this.m_Show && (this.m_Plot==null)) this.initProblemFrame(); + } + + public void evaluatePopulationEnd(Population population) { + super.evaluatePopulationEnd(population); + double[] fitness; + + for (int i = 0; i < population.size(); i++) { + // check and update border if necessary + AbstractEAIndividual tmpIndy = (AbstractEAIndividual) population.get(i); fitness = tmpIndy.getFitness(); // check and update border if necessary - if (m_Border == null) - this.m_Border = new double[fitness.length][2]; + if (m_Border == null) this.m_Border = new double[fitness.length][2]; else if (fitness.length != this.m_Border.length) { //System.out.println("AbstractMOOptimizationProblem: Warning fitness.length("+fitness.length+") doesn't fit border.length("+this.m_Border.length+")"); //System.out.println("Resetting the border!"); @@ -257,17 +211,8 @@ public abstract class AbstractMultiObjectiveOptimizationProblem extends Abstract this.m_Border[j][0] = Math.min(this.m_Border[j][0], fitness[j]); this.m_Border[j][1] = Math.max(this.m_Border[j][1], fitness[j]); } - population.incrFunctionCalls(); } - } - evaluatePopulationEnd(population); // refactored by MK - } - - public void evaluatePopulationStart(Population population) { - if (this.m_Show && (this.m_Plot==null)) this.initProblemFrame(); - } - - public void evaluatePopulationEnd(Population population) { + // So what is the problem: // on the one hand i want to log the pareto-front in the // multiobjective case diff --git a/src/eva2/server/go/problems/AbstractOptimizationProblem.java b/src/eva2/server/go/problems/AbstractOptimizationProblem.java index 372536ef..87e95ea9 100644 --- a/src/eva2/server/go/problems/AbstractOptimizationProblem.java +++ b/src/eva2/server/go/problems/AbstractOptimizationProblem.java @@ -1,12 +1,11 @@ package eva2.server.go.problems; import java.awt.BorderLayout; -import java.util.Vector; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Semaphore; import java.io.Serializable; - +import java.util.Vector; import javax.swing.JComponent; import javax.swing.JPanel; @@ -42,14 +41,14 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati class EvalThread extends Thread { AbstractOptimizationProblem prob; AbstractEAIndividual ind; - Vector resultrep; +// Vector resultrep; Population pop; Semaphore m_Semaphore; - public EvalThread(AbstractOptimizationProblem prob, AbstractEAIndividual ind, Vector resultrep, Population pop,Semaphore sema) { + public EvalThread(AbstractOptimizationProblem prob, AbstractEAIndividual ind, Population pop,Semaphore sema) { this.ind = ind; this.prob = prob; - this.resultrep = resultrep; +// this.resultrep = resultrep; this.pop = pop; this.m_Semaphore=sema; } @@ -58,7 +57,7 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati // System.out.println("Running ET " + this); // long time=System.nanoTime(); prob.evaluate(ind); - resultrep.add(ind); +// resultrep.add(ind); pop.incrFunctionCalls(); m_Semaphore.release(); // long duration=System.nanoTime()-time; @@ -110,7 +109,7 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati if (this.parallelthreads > 1) { Vector queue = new Vector(population.size()); - Vector finished = new Vector(population.size()); +// Vector finished = new Vector(population.size()); /* queue.addAll(population); Semaphore sema=new Semaphore(parallelthreads); while (finished.size() < population.size()) { @@ -132,19 +131,19 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati } }*/ Semaphore sema=new Semaphore(0); - ExecutorService pool = Executors.newFixedThreadPool(parallelthreads); - for (int i = 0; i < population.size(); i++){ - AbstractEAIndividual tmpindy = (AbstractEAIndividual)population.get(i); + ExecutorService pool = Executors.newFixedThreadPool(parallelthreads); + int cntIndies=0; + for (; cntIndies < population.size(); cntIndies++){ + AbstractEAIndividual tmpindy = (AbstractEAIndividual)population.get(cntIndies); tmpindy.resetConstraintViolation(); - EvalThread evalthread = new EvalThread(this,tmpindy,finished,population,sema); + EvalThread evalthread = new EvalThread(this,tmpindy,population,sema); pool.execute(evalthread); - } try { - sema.acquire(population.size()); + sema.acquire(cntIndies); } catch (InterruptedException e) { - // TODO Auto-generated catch block e.printStackTrace(); + throw new RuntimeException("Threading error in AbstractOptimizationProblem: " + e.getMessage()); } pool.shutdownNow(); } else { @@ -185,6 +184,21 @@ public abstract class AbstractOptimizationProblem implements InterfaceOptimizati */ public abstract void evaluate(AbstractEAIndividual individual); + + public static void defaultInitPopulation(Population population, AbstractEAIndividual template, InterfaceOptimizationProblem prob) { + AbstractEAIndividual tmpIndy; + population.clear(); + + for (int i = 0; i < population.getTargetSize(); i++) { + tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)template).clone(); + tmpIndy.init(prob); + population.add(tmpIndy); + } + // population init must be last + // it set's fitcalls and generation to zero + population.init(); + } + /******************** Some output methods *******************************************/ /** This method allows you to output a string that describes a found solution diff --git a/src/eva2/server/go/problems/AbstractProblemBinary.java b/src/eva2/server/go/problems/AbstractProblemBinary.java index fb9cf087..d0344738 100644 --- a/src/eva2/server/go/problems/AbstractProblemBinary.java +++ b/src/eva2/server/go/problems/AbstractProblemBinary.java @@ -55,31 +55,20 @@ public abstract class AbstractProblemBinary extends AbstractOptimizationProblem */ public abstract int getProblemDimension(); - /** - * Initialize a single individual with index k in the - * initPopulation cycle. - * @param k - * @param indy - */ - protected void initIndy(int k, AbstractEAIndividual indy) { - indy.init(this); - } +// /** +// * Initialize a single individual with index k in the +// * initPopulation cycle. +// * @param k +// * @param indy +// */ +// protected void initIndy(int k, AbstractEAIndividual indy) { +// indy.init(this); +// } @Override public void initPopulation(Population population) { - AbstractEAIndividual tmpIndy; - population.clear(); - ((InterfaceDataTypeBinary)this.m_Template).setBinaryDataLength(this.getProblemDimension()); - - for (int i = 0; i < population.getPopulationSize(); i++) { - tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone(); - initIndy(i, tmpIndy); - population.add(tmpIndy); - } - // population init must be last - // it set's fitcalls and generation to zero - population.init(); + AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this); } @Override diff --git a/src/eva2/server/go/problems/AbstractProblemDouble.java b/src/eva2/server/go/problems/AbstractProblemDouble.java index e6fa37fa..ccd06f7a 100644 --- a/src/eva2/server/go/problems/AbstractProblemDouble.java +++ b/src/eva2/server/go/problems/AbstractProblemDouble.java @@ -155,18 +155,8 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem @Override public void initPopulation(Population population) { - AbstractEAIndividual tmpIndy; - population.clear(); initTemplate(); - - for (int i = 0; i < population.getPopulationSize(); i++) { - tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone(); - tmpIndy.init(this); - population.add(tmpIndy); - } - // population init must be last - // it set's fitcalls and generation to zero - population.init(); + AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this); } /** diff --git a/src/eva2/server/go/problems/AbstractProblemDoubleOffset.java b/src/eva2/server/go/problems/AbstractProblemDoubleOffset.java new file mode 100644 index 00000000..774581e2 --- /dev/null +++ b/src/eva2/server/go/problems/AbstractProblemDoubleOffset.java @@ -0,0 +1,99 @@ +package eva2.server.go.problems; + +/** + * Created by IntelliJ IDEA. + * User: streiche + * Date: 24.03.2003 + * Time: 17:58:55 + * To change this template use Options | File Templates. + */ +public abstract class AbstractProblemDoubleOffset extends AbstractProblemDouble implements Interface2DBorderProblem { + +// protected AbstractEAIndividual m_OverallBest = null; + protected int m_ProblemDimension = 10; + protected double m_XOffSet = 0.0; + protected double m_YOffSet = 0.0; +// protected boolean m_UseTestConstraint = false; + + public AbstractProblemDoubleOffset() { + super(); + setDefaultRange(10); + } + + public AbstractProblemDoubleOffset(AbstractProblemDoubleOffset b) { + super(); + super.cloneObjects(b); + this.m_ProblemDimension = b.m_ProblemDimension; + this.m_XOffSet = b.m_XOffSet; + this.m_YOffSet = b.m_YOffSet; +// this.m_UseTestConstraint = b.m_UseTestConstraint; + } + + public AbstractProblemDoubleOffset(int dim) { + this(); + setProblemDimension(dim); + } + + public AbstractProblemDoubleOffset(int dim, double defRange) { + this(dim); + setDefaultRange(defRange); + } + + /** This method inits the Problem to log multiruns + */ + public void initProblem() { +// this.m_OverallBest = null; + initTemplate(); + } + +/********************************************************************************************************************** + * These are for GUI + */ + /** This method allows the CommonJavaObjectEditorPanel to read the + * name to the current object. + * @return The name. + */ + public String getName() { + return "Double-valued-Problem+Offsets"; + } + + /** This method allows you to set/get an offset for decision variables. + * @param XOffSet The offset for the decision variables. + */ + public void setXOffSet(double XOffSet) { + this.m_XOffSet = XOffSet; + } + public double getXOffSet() { + return this.m_XOffSet; + } + public String xOffSetTipText() { + return "Choose an offset for the decision variable."; + } + + /** This method allows you to set/get the offset for the + * objective value. + * @param YOffSet The offset for the objective value. + */ + public void setYOffSet(double YOffSet) { + this.m_YOffSet = YOffSet; + } + public double getYOffSet() { + return this.m_YOffSet; + } + public String yOffSetTipText() { + return "Choose an offset for the objective value."; + } + /** Length of the x vector at is to be optimized + * @param t Length of the x vector at is to be optimized + */ + public void setProblemDimension(int t) { + this.m_ProblemDimension = t; + } + public int getProblemDimension() { + return this.m_ProblemDimension; + } + public String problemDimensionTipText() { + return "Length of the x vector to be optimized."; + } +} + diff --git a/src/eva2/server/go/problems/ExternalRuntimeProblem.java b/src/eva2/server/go/problems/ExternalRuntimeProblem.java index 55ae54c0..b896fcce 100644 --- a/src/eva2/server/go/problems/ExternalRuntimeProblem.java +++ b/src/eva2/server/go/problems/ExternalRuntimeProblem.java @@ -105,23 +105,12 @@ public class ExternalRuntimeProblem extends AbstractOptimizationProblem implemen * @param population The populations that is to be inited */ public void initPopulation(Population population) { - AbstractEAIndividual tmpIndy; - this.m_OverallBest = null; - population.clear(); - ((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.m_ProblemDimension); ((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(makeRange()); - for (int i = 0; i < population.getPopulationSize(); i++) { - tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone(); - tmpIndy.init(this); - population.add(tmpIndy); - } - // population init must be last - // it set's fitcalls and generation to zero - population.init(); + AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this); } public double[][] makeRange() { diff --git a/src/eva2/server/go/problems/F10Problem.java b/src/eva2/server/go/problems/F10Problem.java index 01b85f1e..4f8f7cbb 100644 --- a/src/eva2/server/go/problems/F10Problem.java +++ b/src/eva2/server/go/problems/F10Problem.java @@ -9,7 +9,7 @@ import eva2.server.go.individuals.ESIndividualDoubleData; * Time: 14:33:07 * To change this template use File | Settings | File Templates. */ -public class F10Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable { +public class F10Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable { private double m_D = 1.5; private double m_b = 2.3; diff --git a/src/eva2/server/go/problems/F11Problem.java b/src/eva2/server/go/problems/F11Problem.java index 9c010a21..2b3dd67c 100644 --- a/src/eva2/server/go/problems/F11Problem.java +++ b/src/eva2/server/go/problems/F11Problem.java @@ -12,7 +12,7 @@ import eva2.server.go.populations.Population; * Time: 14:59:23 * To change this template use File | Settings | File Templates. */ -public class F11Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable { +public class F11Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable { private double m_D = 4000; diff --git a/src/eva2/server/go/problems/F12Problem.java b/src/eva2/server/go/problems/F12Problem.java index aea92c0e..eb313c3e 100644 --- a/src/eva2/server/go/problems/F12Problem.java +++ b/src/eva2/server/go/problems/F12Problem.java @@ -12,7 +12,7 @@ import eva2.server.go.populations.Population; * Time: 14:59:33 * To change this template use File | Settings | File Templates. */ -public class F12Problem extends F1Problem implements java.io.Serializable { +public class F12Problem extends AbstractProblemDoubleOffset implements java.io.Serializable { private final static double f12range = 5.; public F12Problem() { diff --git a/src/eva2/server/go/problems/F13Problem.java b/src/eva2/server/go/problems/F13Problem.java index 55228d7c..edbd3076 100644 --- a/src/eva2/server/go/problems/F13Problem.java +++ b/src/eva2/server/go/problems/F13Problem.java @@ -6,7 +6,7 @@ import eva2.server.go.individuals.ESIndividualDoubleData; * Schwefels sine root function (1981) with a minimum at 420.9687^n of value 0. * Function f(x) = (418.9829 * n) - sum_n(x_i * sin(sqrt(abs(x_i)))) + (418.9829 * n); */ -public class F13Problem extends F1Problem implements InterfaceMultimodalProblem { +public class F13Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem { public F13Problem() { this.m_Template = new ESIndividualDoubleData(); diff --git a/src/eva2/server/go/problems/F14Problem.java b/src/eva2/server/go/problems/F14Problem.java index a9ac0065..27cd7718 100644 --- a/src/eva2/server/go/problems/F14Problem.java +++ b/src/eva2/server/go/problems/F14Problem.java @@ -9,7 +9,7 @@ import eva2.server.go.individuals.ESIndividualDoubleData; * Time: 19:15:03 * To change this template use File | Settings | File Templates. */ -public class F14Problem extends F1Problem implements InterfaceMultimodalProblem, java.io.Serializable { +public class F14Problem extends AbstractProblemDoubleOffset implements InterfaceMultimodalProblem, java.io.Serializable { double rotation = 0.; double rotationDX = 2; diff --git a/src/eva2/server/go/problems/F1Problem.java b/src/eva2/server/go/problems/F1Problem.java index fe34622b..ba754814 100644 --- a/src/eva2/server/go/problems/F1Problem.java +++ b/src/eva2/server/go/problems/F1Problem.java @@ -1,7 +1,5 @@ package eva2.server.go.problems; -import eva2.server.go.individuals.AbstractEAIndividual; -import eva2.server.go.operators.constraint.GenericConstraint; import eva2.server.go.strategies.InterfaceOptimizer; /** @@ -11,18 +9,7 @@ import eva2.server.go.strategies.InterfaceOptimizer; * Time: 17:58:55 * To change this template use Options | File Templates. */ -public class F1Problem extends AbstractProblemDouble implements Interface2DBorderProblem, java.io.Serializable { - - /** - * - */ - private static final long serialVersionUID = 4870484001737601464L; -// protected AbstractEAIndividual m_OverallBest = null; - protected int m_ProblemDimension = 10; - protected double m_XOffSet = 0.0; - protected double m_YOffSet = 0.0; -// protected boolean m_UseTestConstraint = false; - +public class F1Problem extends AbstractProblemDoubleOffset implements Interface2DBorderProblem, java.io.Serializable, InterfaceFirstOrderDerivableProblem { public F1Problem() { super(); setDefaultRange(10); @@ -31,15 +18,10 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde public F1Problem(F1Problem b) { super(); super.cloneObjects(b); - this.m_ProblemDimension = b.m_ProblemDimension; - this.m_XOffSet = b.m_XOffSet; - this.m_YOffSet = b.m_YOffSet; -// this.m_UseTestConstraint = b.m_UseTestConstraint; } public F1Problem(int dim) { - this(); - setProblemDimension(dim); + super(dim); } public F1Problem(int dim, double defRange) { @@ -54,13 +36,6 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde return (Object) new F1Problem(this); } - /** This method inits the Problem to log multiruns - */ - public void initProblem() { -// this.m_OverallBest = null; - initTemplate(); - } - /** Ths method allows you to evaluate a simple bit string to determine the fitness * @param x The n-dimensional input vector * @return The m-dimensional output vector. @@ -110,54 +85,12 @@ public class F1Problem extends AbstractProblemDouble implements Interface2DBorde return "F1: multidimensional parabola problem"; } - /** This method allows you to set/get an offset for decision variables. - * @param XOffSet The offset for the decision variables. - */ - public void setXOffSet(double XOffSet) { - this.m_XOffSet = XOffSet; - } - public double getXOffSet() { - return this.m_XOffSet; - } - public String xOffSetTipText() { - return "Choose an offset for the decision variable."; - } - - /** This method allows you to set/get the offset for the - * objective value. - * @param YOffSet The offset for the objective value. - */ - public void setYOffSet(double YOffSet) { - this.m_YOffSet = YOffSet; - } - public double getYOffSet() { - return this.m_YOffSet; - } - public String yOffSetTipText() { - return "Choose an offset for the objective value."; - } - /** Length of the x vector at is to be optimized - * @param t Length of the x vector at is to be optimized - */ - public void setProblemDimension(int t) { - this.m_ProblemDimension = t; - } - public int getProblemDimension() { - return this.m_ProblemDimension; - } - public String problemDimensionTipText() { - return "Length of the x vector to be optimized."; - } -// /** This method allows you to toggle the application of a simple test constraint. -// * @param b The mode for the test constraint -// */ -// public void setUseTestConstraint(boolean b) { -// this.m_UseTestConstraint = b; -// } -// public boolean getUseTestConstraint() { -// return this.m_UseTestConstraint; -// } -// public String useTestConstraintTipText() { -// return "Just a simple test constraint of x[0] >= 1."; -// } + public double[] getFirstOrderGradients(double[] x) { + // first order partial derivation in direction x_i is 2*x_i + double[] grads=new double[x.length]; + for (int i=0; iTitle: EvA2

- *

Description:

- *

Copyright: Copyright (c) 2003

- *

Company:

- * @author not attributable - * @version 1.0 + * An interface for first-order derivable problems which can be used for gradient descent. + * + * @author hplanatscher, mkron + * */ - public interface InterfaceFirstOrderDerivableProblem { + /** + * Calculate the first order gradients of this problem. + * @param x + * @return the first order gradients of this problem + */ public double[] getFirstOrderGradients(double[] x); - public double getFirstOrderGradient(int paramindex,double[] x); - +// public double getFirstOrderGradient(int paramindex,double[] x); } diff --git a/src/eva2/server/go/problems/InterfaceProgramProblem.java b/src/eva2/server/go/problems/InterfaceProgramProblem.java index 4071439b..07856cc9 100644 --- a/src/eva2/server/go/problems/InterfaceProgramProblem.java +++ b/src/eva2/server/go/problems/InterfaceProgramProblem.java @@ -9,7 +9,7 @@ import eva2.server.go.individuals.codings.gp.GPArea; * Time: 14:43:03 * To change this template use Options | File Templates. */ -public interface InterfaceProgramProblem { +public interface InterfaceProgramProblem extends InterfaceOptimizationProblem { /** This method allows a GP program to sense the environment, e.g. * input values, current time etc diff --git a/src/eva2/server/go/problems/MatlabProblem.java b/src/eva2/server/go/problems/MatlabProblem.java index 00d860e1..1ec83039 100644 --- a/src/eva2/server/go/problems/MatlabProblem.java +++ b/src/eva2/server/go/problems/MatlabProblem.java @@ -437,19 +437,8 @@ public class MatlabProblem extends AbstractOptimizationProblem implements Interf @Override public void initPopulation(Population population) { - AbstractEAIndividual tmpIndy; - population.clear(); initTemplate(); - - for (int i = 0; i < population.getPopulationSize(); i++) { - tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone(); - tmpIndy.init(this); -// System.err.println("initPopulation: " + AbstractEAIndividual.getDefaultDataString(tmpIndy) + " , " + tmpIndy.getStringRepresentation()); - population.add(tmpIndy); - } - // population init must be last - // it set's fitcalls and generation to zero - population.init(); + AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this); } public String getStringRepresentationForProblem(InterfaceOptimizer opt) { diff --git a/src/eva2/server/go/problems/PSymbolicRegression.java b/src/eva2/server/go/problems/PSymbolicRegression.java index 8d346390..5b1be57d 100644 --- a/src/eva2/server/go/problems/PSymbolicRegression.java +++ b/src/eva2/server/go/problems/PSymbolicRegression.java @@ -143,9 +143,8 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements * @param population The populations that is to be inited */ public static void initPopulation(Population pop, InterfaceProgramProblem prob, boolean useInnerConsts, int numConsts) { - AbstractEAIndividual tmpIndy, template; + AbstractEAIndividual template; - pop.clear(); template = ((AbstractOptimizationProblem)prob).getIndividualTemplate(); GPArea tmpArea[] = new GPArea[1]; tmpArea[0] = prob.getArea(); @@ -154,14 +153,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements if ((template instanceof GAPIndividualProgramData) && useInnerConsts) { ((GAPIndividualProgramData)template).setDoubleDataLength(numConsts); } - for (int i = 0; i < pop.getPopulationSize(); i++) { - tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)template).clone(); - tmpIndy.init((AbstractOptimizationProblem)prob); - pop.add(tmpIndy); - } - // population init must be last - // it set's fitcalls and generation to zero - pop.init(); + AbstractOptimizationProblem.defaultInitPopulation(pop, template, prob); } /** This method init the enviroment panel if necessary. @@ -238,6 +230,7 @@ public class PSymbolicRegression extends AbstractOptimizationProblem implements if ((this.m_OverallBest == null) || (this.m_OverallBest.getFitness(0) > individual.getFitness(0))) { this.m_OverallBest = (AbstractEAIndividual)individual.clone(); if (this.m_Show) { + if (m_Plot==null) this.initEnvironmentPanel(); this.m_Plot.clearAll(); program = ((InterfaceDataTypeProgram)this.m_OverallBest).getProgramData()[0]; for (double i = 0; i < this.m_NumberOfCheckPoints; i++) { diff --git a/src/eva2/server/go/problems/SimpleProblemWrapper.java b/src/eva2/server/go/problems/SimpleProblemWrapper.java index 59a488b1..8c46afc1 100644 --- a/src/eva2/server/go/problems/SimpleProblemWrapper.java +++ b/src/eva2/server/go/problems/SimpleProblemWrapper.java @@ -101,20 +101,8 @@ public class SimpleProblemWrapper extends AbstractOptimizationProblem { @Override public void initPopulation(Population population) { - AbstractEAIndividual tmpIndy; - population.clear(); - initTemplate(); - - for (int i = 0; i < population.getPopulationSize(); i++) { - tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone(); - tmpIndy.init(this); - population.add(tmpIndy); - } - // population init must be last - // it set's fitcalls and generation to zero - population.init(); - + AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this); } @Override diff --git a/src/eva2/server/go/problems/TF1Problem.java b/src/eva2/server/go/problems/TF1Problem.java index b074824b..6e80c3b5 100644 --- a/src/eva2/server/go/problems/TF1Problem.java +++ b/src/eva2/server/go/problems/TF1Problem.java @@ -90,23 +90,14 @@ public class TF1Problem extends AbstractMultiObjectiveOptimizationProblem implem * @param population The populations that is to be inited */ public void initPopulation(Population population) { - AbstractEAIndividual tmpIndy; this.m_ParetoFront = new Population(); double[][] newRange = makeRange(); - population.clear(); - ((InterfaceDataTypeDouble)this.m_Template).setDoubleDataLength(this.m_ProblemDimension); ((InterfaceDataTypeDouble)this.m_Template).SetDoubleRange(newRange); - for (int i = 0; i < population.getPopulationSize(); i++) { - tmpIndy = (AbstractEAIndividual)((AbstractEAIndividual)this.m_Template).clone(); - tmpIndy.init(this); - population.add(tmpIndy); - } - // population init must be last - // it set's fitcalls and generation to zero - population.init(); + + AbstractOptimizationProblem.defaultInitPopulation(population, m_Template, this); } protected double[][] makeRange() { diff --git a/src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java b/src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java index 3b458e37..1db94196 100644 --- a/src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java +++ b/src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java @@ -118,7 +118,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S //System.out.println("Population:"+this.m_Population.getSolutionRepresentationFor()); this.m_PopulSelectionOperator.prepareSelection(this.m_Population); this.m_RecombSelectionOperator.prepareSelection(this.m_Population); - parents = this.m_PopulSelectionOperator.selectFrom(this.m_Population, this.m_Population.getPopulationSize()); + parents = this.m_PopulSelectionOperator.selectFrom(this.m_Population, this.m_Population.getTargetSize()); //System.out.println("Parents:"+parents.getSolutionRepresentationFor()); for (int i = 0; i < parents.size(); i++) { @@ -169,7 +169,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S this.m_Population.clear(); this.m_Population.add(best); - for (int i = 1; i < this.m_Population.getPopulationSize(); i++) { + for (int i = 1; i < this.m_Population.getTargetSize(); i++) { mutant = (InterfaceGAIndividual)best.clone(); tmpBitSet = mutant.getBGenotype(); for (int j = 0; j < mutant.getGenotypeLength(); j++) { @@ -207,7 +207,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S nextGeneration.addPopulation(this.m_Population); // this.m_NormationOperator.computeSelectionProbability(nextGeneration, "Fitness"); this.m_PopulSelectionOperator.prepareSelection(this.m_Population); - tmp = this.m_PopulSelectionOperator.selectFrom(nextGeneration, this.m_Population.getPopulationSize()); + tmp = this.m_PopulSelectionOperator.selectFrom(nextGeneration, this.m_Population.getTargetSize()); nextGeneration.clear(); nextGeneration.addPopulation(tmp); this.m_Population = nextGeneration; diff --git a/src/eva2/server/go/strategies/ClusterBasedNichingEA.java b/src/eva2/server/go/strategies/ClusterBasedNichingEA.java index 62eb0ddd..f7108757 100644 --- a/src/eva2/server/go/strategies/ClusterBasedNichingEA.java +++ b/src/eva2/server/go/strategies/ClusterBasedNichingEA.java @@ -111,7 +111,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis this.m_Optimizer.addPopulationChangedEventListener(this); this.m_Undifferentiated = new Population(); this.m_Undifferentiated.setUseHistory(true); - this.m_Undifferentiated.setPopulationSize(this.m_PopulationSize); + this.m_Undifferentiated.setTargetSize(this.m_PopulationSize); this.m_Species = new ArrayList(); this.m_Problem.initPopulation(this.m_Undifferentiated); this.evaluatePopulation(this.m_Undifferentiated); @@ -132,7 +132,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis pop.setUseHistory(true); this.m_Undifferentiated = (Population)pop.clone(); if (reset) this.m_Undifferentiated.init(); - this.m_Undifferentiated.setPopulationSize(this.m_PopulationSize); + this.m_Undifferentiated.setTargetSize(this.m_PopulationSize); this.m_Species = new ArrayList(); m_Archive = new Population(); convergedCnt = 0; @@ -273,7 +273,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis private Population initializeIndividuals(int n) { Population result = new Population(); result.setUseHistory(true); - result.setPopulationSize(n); + result.setTargetSize(n); //@todo: crossover between species is to be impelemented this.m_Problem.initPopulation(result); this.m_Problem.evaluate(result); @@ -334,18 +334,18 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis if (TRACE) System.out.println("mu: "+es.getMu() + " / lambda: " + es.getLambda()); } if (TRACE) { - System.out.println("Bef: spec size: " + species.size() + ", says its " + species.getPopulationSize()); + System.out.println("Bef: spec size: " + species.size() + ", target size " + species.getTargetSize()); System.out.println("Best bef: " + BeanInspector.toString(m_Optimizer.getPopulation().getBestFitness())); } this.m_Optimizer.optimize(); Population retPop = m_Optimizer.getPopulation(); if (TRACE) { - System.out.println("Aft: spec size: " + retPop.size() + ", says its " + retPop.getPopulationSize()); + System.out.println("Aft: spec size: " + retPop.size() + ", target size " + retPop.getTargetSize()); System.out.println("Best aft: " + BeanInspector.toString(retPop.getBestFitness())); } - if (retPop.size() != retPop.getPopulationSize()) { - if (TRACE) System.out.println("correcting popsize after opt: " + retPop.getPopulationSize() + " to " + retPop.size()); - retPop.setPopulationSize(retPop.size()); + if (retPop.size() != retPop.getTargetSize()) { + if (TRACE) System.out.println("correcting popsize after opt: " + retPop.getTargetSize() + " to " + retPop.size()); + retPop.synchSize(); } // if (useDistraction) { @@ -376,7 +376,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis } // optimize D_0 - this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.size()); + this.m_Undifferentiated.synchSize(); if (isActive(m_Undifferentiated)) { this.capMutationRate(this.m_Undifferentiated, 0); // MK TODO this sets mutation rate to 0! why? m_Undifferentiated = optimizeSpecies(m_Undifferentiated); @@ -398,7 +398,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis if (TRACE) System.out.println("-Deme " + i + " size: " + ((Population)this.m_Species.get(i)).size()); curSpecies = ((Population)this.m_Species.get(i)); curSpecies.SetFunctionCalls(0); - curSpecies.setPopulationSize(curSpecies.size()); + curSpecies.synchSize(); if (isActive(curSpecies)) { if ((haltingWindow > 0) && (this.testSpeciesForConvergence(curSpecies))) { ///////////////////////////////////////////// Halting Window ///////////////////////////////////////////////// @@ -431,7 +431,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis // reinit the surplus individuals and add these new individuals to undifferentiated m_Undifferentiated.addPopulation(this.initializeIndividuals(reinitCount)); m_Undifferentiated.incrFunctionCallsBy(reinitCount); - m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()+reinitCount); + m_Undifferentiated.setTargetSize(this.m_Undifferentiated.getTargetSize()+reinitCount); // if (this.m_Debug) { // System.out.println("Undiff.Size: " + this.m_Undifferentiated.size() +"/"+this.m_Undifferentiated.getPopulationSize()); // System.out.println("Diff.Size : " + ((Population)this.m_Species.get(i)).size() +"/"+((Population)this.m_Species.get(i)).getPopulationSize()); @@ -492,9 +492,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis ClusterResult = this.m_CAForSpeciesDifferentation.cluster(curSpecies); this.m_Undifferentiated.addPopulation(ClusterResult[0]); // ClusterResult[0].setUseHistory(true); - this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize() + ClusterResult[0].size()); + this.m_Undifferentiated.setTargetSize(this.m_Undifferentiated.getTargetSize() + ClusterResult[0].size()); for (int j = 1; j < ClusterResult.length; j++) { // set up new species - ClusterResult[j].setPopulationSize(ClusterResult[j].size()); + ClusterResult[j].synchSize(); ClusterResult[j].setUseHistory(true); // if (ClusterResult.length > 2) ClusterResult[j].m_History = new ArrayList(); // mk: why min 3? Ill copy the history from the original pop for any j... ClusterResult[j].m_History = (ArrayList) curSpecies.m_History.clone(); @@ -537,7 +537,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis this.m_Undifferentiated.remove(i); if (isActive(curSpecies)) { curSpecies.add(tmpIndy); - this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()-1); + this.m_Undifferentiated.setTargetSize(this.m_Undifferentiated.getTargetSize()-1); i--; // needs to be reduced because D0 size has decreased } else { // the species is inactive and seen as converged, so reinitialize the individual @@ -578,7 +578,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis // reinitialized individuals and add them to undifferentiated this.m_Undifferentiated.addPopulation(this.initializeIndividuals(reinitCount)); // m_Undifferentiated.incrFunctionCallsby(reinitCount); - this.m_Undifferentiated.setPopulationSize(this.m_Undifferentiated.getPopulationSize()+reinitCount); + this.m_Undifferentiated.setTargetSize(this.m_Undifferentiated.getTargetSize()+reinitCount); } } } @@ -639,7 +639,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis * @param spec */ protected void deactivateSpecies(Population spec, AbstractEAIndividual survivor) { - spec.setPopulationSize(1); + spec.setTargetSize(1); spec.clear(); spec.add(survivor); } @@ -736,7 +736,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis this.m_Population = (Population)m_Undifferentiated.clone(); // m_Population.addPopulation(this.m_Undifferentiated); for (int i = 0; i < this.m_Species.size(); i++) this.m_Population.addPopulation((Population)this.m_Species.get(i)); - m_Population.setPopulationSize(m_Population.size()); // set it to true value + m_Population.synchSize(); // set target size to current size return this.m_Population; } @@ -753,7 +753,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis // return inactive species Population sols = (Population)m_Archive.clone(); sols.addPopulation(getPopulation()); - sols.setPopulationSize(sols.size()); + sols.synchSize(); return new SolutionSet(getPopulation(), sols); } diff --git a/src/eva2/server/go/strategies/ClusteringHillClimbing.java b/src/eva2/server/go/strategies/ClusteringHillClimbing.java index b1cbe5e2..8538fd25 100644 --- a/src/eva2/server/go/strategies/ClusteringHillClimbing.java +++ b/src/eva2/server/go/strategies/ClusteringHillClimbing.java @@ -4,6 +4,7 @@ import java.io.Serializable; import eva2.gui.GenericObjectEditor; import eva2.server.go.InterfacePopulationChangedEventListener; +import eva2.server.go.PopulationInterface; import eva2.server.go.enums.PostProcessMethod; import eva2.server.go.operators.mutation.MutateESFixedStepSize; import eva2.server.go.operators.postprocess.PostProcess; @@ -12,6 +13,7 @@ import eva2.server.go.populations.Population; import eva2.server.go.populations.SolutionSet; import eva2.server.go.problems.AbstractOptimizationProblem; import eva2.server.go.problems.F1Problem; +import eva2.server.go.problems.InterfaceAdditionalPopulationInformer; import eva2.server.go.problems.InterfaceOptimizationProblem; import eva2.tools.Pair; @@ -31,7 +33,7 @@ import eva2.tools.Pair; * @author mkron * */ -public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener, InterfaceOptimizer, Serializable { +public class ClusteringHillClimbing implements InterfacePopulationChangedEventListener, InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer { transient private InterfacePopulationChangedEventListener m_Listener; public static final boolean TRACE = false; @@ -52,6 +54,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi private double reduceFactor = 0.2; private MutateESFixedStepSize mutator = new MutateESFixedStepSize(0.1); private PostProcessMethod localSearchMethod = PostProcessMethod.nelderMead; + private boolean doReinitialization = true; public ClusteringHillClimbing() { hideHideable(); @@ -83,6 +86,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi */ public void hideHideable() { GenericObjectEditor.setHideProperty(getClass(), "population", true); + setDoReinitialization(isDoReinitialization()); + setLocalSearchMethod(getLocalSearchMethod()); } public void SetIdentifier(String name) { @@ -117,7 +122,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi mutator = new MutateESFixedStepSize(initialStepSize); archive = new Population(); hideHideable(); - m_Population.setPopulationSize(initialPopSize); + m_Population.setTargetSize(initialPopSize); this.m_Problem.initPopulation(this.m_Population); m_Population.addPopulationChangedEventListener(null); // noone will be notified directly on pop changes this.m_Problem.evaluate(this.m_Population); @@ -152,16 +157,26 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi m_Population.addPopulationChangedEventListener(this); m_Population.setNotifyEvalInterval(notifyGuiEvery); Pair popD; - if (TRACE) System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + (2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle))); - popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, 2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5, mutator); -// (m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5); + int funCallsBefore=m_Population.getFunctionCalls(); + int evalsNow, lastOverhead = (m_Population.getFunctionCalls() % hcEvalCycle); + if (lastOverhead>0) evalsNow = (2*hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle)); + else evalsNow = hcEvalCycle; + do { + if (TRACE) System.out.println("evalCycle: " + hcEvalCycle + ", evals now: " + evalsNow); + popD = PostProcess.clusterLocalSearch(localSearchMethod, m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, evalsNow, 0.5, mutator); + // (m_Population, (AbstractOptimizationProblem)m_Problem, sigmaClust, hcEvalCycle - (m_Population.getFunctionCalls() % hcEvalCycle), 0.5); + if (popD.head().getFunctionCalls()==funCallsBefore) { + System.err.println("Bad case, increasing allowed evaluations!"); + evalsNow=Math.max(evalsNow++, (int)(evalsNow*1.2)); + } + } while (popD.head().getFunctionCalls()==funCallsBefore); improvement = popD.tail(); m_Population = popD.head(); if (TRACE) System.out.println("num inds after clusterLS: " + m_Population.size()); popD.head().setGenerationTo(m_Population.getGeneration()+1); - if (improvement < minImprovement) { + if (doReinitialization && (improvement < minImprovement)) { if (TRACE) System.out.println("improvement below " + minImprovement); if ((localSearchMethod != PostProcessMethod.hillClimber) || (mutator.getSigma() < stepSizeThreshold)) { // reinit! // is performed for nm and cma, and if hc has too low sigma @@ -175,10 +190,10 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi Population tmpPop = new Population(); tmpPop.addPopulationChangedEventListener(null); - tmpPop.setPopulationSize(initialPopSize); + tmpPop.setTargetSize(initialPopSize); this.m_Problem.initPopulation(tmpPop); tmpPop.setSameParams(m_Population); - tmpPop.setPopulationSize(initialPopSize); + tmpPop.setTargetSize(initialPopSize); this.m_Problem.evaluate(tmpPop); // reset population while keeping function calls etc. @@ -245,7 +260,7 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi public String getStringRepresentation() { StringBuffer sbuf = new StringBuffer("Clustering Hill Climbing"); sbuf.append(", initial pop size: "); - sbuf.append(getPopulation().getPopulationSize()); + sbuf.append(getPopulation().getTargetSize()); sbuf.append("Optimization Problem: "); sbuf.append(this.m_Problem.getStringRepresentationForProblem(this)); sbuf.append(this.m_Population.getStringRepresentation()); @@ -259,7 +274,8 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi } public String globalInfo() { - return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency."; + return "Similar to multi-start HC, but clusters the population during optimization to remove redundant individuals for efficiency." + + "If the local search step does not achieve a minimum improvement, the population may be reinitialized."; } /** @@ -394,23 +410,32 @@ public class ClusteringHillClimbing implements InterfacePopulationChangedEventLi public void setLocalSearchMethod(PostProcessMethod localSearchMethod) { this.localSearchMethod = localSearchMethod; + GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeInitial", localSearchMethod==PostProcessMethod.hillClimber); + GenericObjectEditor.setShowProperty(this.getClass(), "stepSizeThreshold", localSearchMethod==PostProcessMethod.hillClimber); } public String localSearchMethodTipText() { - return "Set the method to be used for local search"; + return "Set the method to be used for the hill climbing as local search"; } -// /** -// * @return the mutator -// */ -// public InterfaceMutation getMutator() { -// return mutator; -// } -// -// /** -// * @param mutator the mutator to set -// */ -// public void setMutator(InterfaceMutation mutator) { -// this.mutator = mutator; -// } + public String getAdditionalFileStringHeader(PopulationInterface pop) { + return "#Indies"; + } + + public String getAdditionalFileStringValue(PopulationInterface pop) { + return ""+m_Population.size(); + } + + public boolean isDoReinitialization() { + return doReinitialization; + } + + public void setDoReinitialization(boolean doReinitialization) { + this.doReinitialization = doReinitialization; + GenericObjectEditor.setShowProperty(this.getClass(), "minImprovement", doReinitialization); + } + + public String doReinitializationTipText() { + return "Activate reinitialization if no improvement was achieved."; + } } diff --git a/src/eva2/server/go/strategies/EvolutionStrategies.java b/src/eva2/server/go/strategies/EvolutionStrategies.java index 9ad99654..4882ee38 100644 --- a/src/eva2/server/go/strategies/EvolutionStrategies.java +++ b/src/eva2/server/go/strategies/EvolutionStrategies.java @@ -52,7 +52,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ public static final String esMuParam = "EvolutionStrategyMuParameter"; public EvolutionStrategies() { - this.m_Population.setPopulationSize(this.m_Lambda); + this.m_Population.setTargetSize(this.m_Lambda); } public EvolutionStrategies(int mu, int lambda, boolean usePlus) { @@ -111,7 +111,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ * @param reset If true the population is reset. */ public void initByPopulation(Population pop, boolean reset) { - origPopSize = pop.getPopulationSize(); + origPopSize = pop.getTargetSize(); // System.out.println("ES: orig popsize is " + origPopSize); this.m_Population = (Population)pop.clone(); if (reset) { @@ -368,9 +368,9 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ System.err.println("Invalid mu/lambda ratio! Setting mu=lambda="+m_Mu); this.m_Lambda = this.m_Mu; } - if (this.m_UsePlusStrategy) this.m_Population.setPopulationSize(this.m_Mu + this.m_Lambda); - else this.m_Population.setPopulationSize(this.m_Lambda); - origPopSize=m_Population.getPopulationSize(); + if (this.m_UsePlusStrategy) this.m_Population.setTargetSize(this.m_Mu + this.m_Lambda); + else this.m_Population.setTargetSize(this.m_Lambda); + origPopSize=m_Population.getTargetSize(); } /** This method allows you to set an identifier for the algorithm diff --git a/src/eva2/server/go/strategies/FloodAlgorithm.java b/src/eva2/server/go/strategies/FloodAlgorithm.java index 9b5fca45..795215b0 100644 --- a/src/eva2/server/go/strategies/FloodAlgorithm.java +++ b/src/eva2/server/go/strategies/FloodAlgorithm.java @@ -39,7 +39,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable public FloodAlgorithm() { this.m_Population = new Population(); - this.m_Population.setPopulationSize(10); + this.m_Population.setTargetSize(10); } public FloodAlgorithm(FloodAlgorithm a) { diff --git a/src/eva2/server/go/strategies/GeneticAlgorithm.java b/src/eva2/server/go/strategies/GeneticAlgorithm.java index fa931494..3d381477 100644 --- a/src/eva2/server/go/strategies/GeneticAlgorithm.java +++ b/src/eva2/server/go/strategies/GeneticAlgorithm.java @@ -110,7 +110,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl //System.out.println("Population:"+this.m_Population.getSolutionRepresentationFor()); this.m_ParentSelection.prepareSelection(this.m_Population); this.m_PartnerSelection.prepareSelection(this.m_Population); - parents = this.m_ParentSelection.selectFrom(this.m_Population, this.m_Population.getPopulationSize()); + parents = this.m_ParentSelection.selectFrom(this.m_Population, this.m_Population.getTargetSize()); //System.out.println("Parents:"+parents.getSolutionRepresentationFor()); if (parents.getEAIndividual(0).getMutationOperator() instanceof InterfaceMutationGenerational) { @@ -153,7 +153,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl } if (this.m_Plague > 0) { for (int i = 0; i < this.m_Plague; i++) if (this.m_Population.size() > 2) this.m_Population.remove(this.m_Population.getWorstEAIndividual()); - this.m_Population.setPopulationSize(this.m_Population.size()); + this.m_Population.setTargetSize(this.m_Population.size()); } //System.out.println("Population size: " + this.m_Population.size()); // if (this.m_Population.getArchive() != null) { @@ -203,7 +203,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl String result = ""; result += "Genetic Algorithm:\n"; result += "Using:\n"; - result += " Population Size = " + this.m_Population.getPopulationSize() + "/" + this.m_Population.size() + "\n"; + result += " Population Size = " + this.m_Population.getTargetSize() + "/" + this.m_Population.size() + "\n"; result += " Parent Selection = " + this.m_ParentSelection.getClass().toString() + "\n"; result += " Partner Selection = " + this.m_PartnerSelection.getClass().toString() + "\n"; result += " Number of Partners = " + this.m_NumberOfPartners + "\n"; diff --git a/src/eva2/server/go/strategies/GradientDescentAlgorithm.java b/src/eva2/server/go/strategies/GradientDescentAlgorithm.java index f91256c5..ac9e3f63 100644 --- a/src/eva2/server/go/strategies/GradientDescentAlgorithm.java +++ b/src/eva2/server/go/strategies/GradientDescentAlgorithm.java @@ -1,7 +1,5 @@ package eva2.server.go.strategies; -import java.util.*; - import eva2.server.go.InterfacePopulationChangedEventListener; import eva2.server.go.individuals.AbstractEAIndividual; import eva2.server.go.individuals.InterfaceDataTypeDouble; @@ -11,45 +9,53 @@ import eva2.server.go.populations.SolutionSet; import eva2.server.go.problems.F1Problem; import eva2.server.go.problems.InterfaceFirstOrderDerivableProblem; import eva2.server.go.problems.InterfaceOptimizationProblem; - - +import eva2.tools.EVAERROR; /** * A gradient descent algorithm by hannes planatscher don't expect any * descriptions here... *big sigh* * + * mkron added some! + * * @author not attributable * @version 1.0 */ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Serializable { - private InterfaceOptimizationProblem m_Problem; + private InterfaceOptimizationProblem m_Problem; - InterfaceDataTypeDouble m_Best, m_Test; - private int iterations = 1; - private double localnminus = 0.5; - private double localnplus = 1.1; - boolean recovery = false; - private int recoverylocksteps = 5; - private double recoverythreshold = 100000; - boolean localstepsizeadaption = true; - boolean globalstepsizeadaption = true; - private double globalinitstepsize = 1; - double globalmaxstepsize = 3.0; - double globalminstepsize = 0.1; - boolean manhattan = false; - double localmaxstepsize = 10; - double localminstepsize = 0.1; - private boolean momentumterm = false; - transient private InterfacePopulationChangedEventListener m_Listener; - public double maximumabsolutechange = 0.2; - Hashtable indyhash; + InterfaceDataTypeDouble m_Best, m_Test; + private int iterations = 1; + private double wDecreaseStepSize = 0.5; + private double wIncreaseStepSize = 1.1; + boolean recovery = false; + private int recoverylocksteps = 5; + private double recoverythreshold = 100000; + boolean localStepSizeAdaption = true; + boolean globalStepSizeAdaption = false; + private double globalinitstepsize = 1; + double globalmaxstepsize = 3.0; + double globalminstepsize = 0.0001; + boolean manhattan = false; + double localmaxstepsize = 10; + double localminstepsize = 0.0001; + private boolean momentumterm = false; + transient private InterfacePopulationChangedEventListener m_Listener; + public double maximumabsolutechange = 0.2; + // Hashtable indyhash; - // These variables are necessary for the more complex LectureGUI enviroment - transient private String m_Identifier = ""; - private Population m_Population; - private InterfaceDataTypeDouble InterfaceDataTypeDouble; + // These variables are necessary for the more complex LectureGUI enviroment + transient private String m_Identifier = ""; + private Population m_Population; + + private static final String lockKey = "gdaLockDataKey"; + private static final String lastFitnessKey = "gdaLastFitDataKey"; + private static final String stepSizeKey = "gdaStepSizeDataKey"; + private static final String wStepSizeKey = "gdaWStepSizeDataKey"; + private static final String gradientKey = "gdaGradientDataKey"; + private static final String changesKey = "gdaChangesDataKey"; + private static final String oldParamsKey = "gdaOldParamsDataKey"; public void initByPopulation(Population pop, boolean reset) { this.setPopulation((Population) pop.clone()); @@ -59,16 +65,15 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser this.firePropertyChangedEvent(Population.nextGenerationPerformed); } //System.out.println("initByPopulation() called"); - indyhash = new Hashtable(); +// indyhash = new Hashtable(); } public GradientDescentAlgorithm() { - indyhash = new Hashtable(); +// indyhash = new Hashtable(); this.m_Population = new Population(); - this.m_Population.setPopulationSize(1); + this.m_Population.setTargetSize(1); } - public Object clone() { /**@todo Implement InterfaceOptimizer method*/ throw new java.lang.UnsupportedOperationException("Method clone() not yet implemented."); @@ -78,15 +83,13 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser return "GradientDescentAlgorithm"; } - public void init() { //System.out.println("init() called "); - indyhash = new Hashtable(); +// indyhash = new Hashtable(); this.m_Problem.initPopulation(this.m_Population); this.m_Problem.evaluate(this.m_Population); } - public double signum(double val) { return (val < 0) ? -1 : 1; } @@ -94,23 +97,23 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser public void optimize() { // System.out.println("opt. called"); AbstractEAIndividual indy; - if ((this.indyhash == null) || (this.indyhash.size() <1)) init(); +// if ((this.indyhash == null) || (this.indyhash.size() <1)) init(); for (int i = 0; i < this.m_Population.size(); i++) { indy = ((AbstractEAIndividual)this.m_Population.get(i)); - if (!indyhash.containsKey(indy)) { + if (!indy.hasData(gradientKey)) { //System.out.println("new indy to hash"); - Hashtable history = new Hashtable(); +// Hashtable history = new Hashtable(); int[] lock = new int[((InterfaceDataTypeDouble) indy).getDoubleData().length]; double[] wstepsize = new double[((InterfaceDataTypeDouble) indy).getDoubleData().length]; for (int li = 0; li < lock.length; li++) lock[li] = 0; for (int li = 0; li < lock.length; li++) wstepsize[li] = 1.0; double fitness = 0; - history.put("lock", lock); - history.put("lastfitness", new Double(fitness)); - history.put("stepsize", new Double(globalinitstepsize)); - history.put("wstepsize", wstepsize); - indyhash.put(indy, history); + indy.putData(lockKey, lock); + indy.putData(lastFitnessKey, new Double(fitness)); + indy.putData(stepSizeKey, new Double(globalinitstepsize)); + indy.putData(wStepSizeKey, wstepsize); +// indyhash.put(indy, history); } else { //System.out.println("indy already in hash"); } @@ -121,48 +124,49 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser indy = ((AbstractEAIndividual)this.m_Population.get(i)); double[][] range = ((InterfaceDataTypeDouble) indy).getDoubleRange(); double[] params = ((InterfaceDataTypeDouble) indy).getDoubleData(); + indy.putData(oldParamsKey , params); - int[] lock = (int[]) ((Hashtable) indyhash.get(indy)).get("lock"); - double indystepsize = ((Double) ((Hashtable) indyhash.get(indy)).get("stepsize")).doubleValue(); + int[] lock = (int[]) indy.getData(lockKey); + double indystepsize = ((Double) indy.getData(stepSizeKey)).doubleValue(); // System.out.println("indystepsize" + indystepsize); if ((this.m_Problem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) { - Hashtable history = (Hashtable) indyhash.get(indy); +// Hashtable history = (Hashtable) indyhash.get(indy); for (int iterations = 0; iterations < this.iterations; iterations++) { - double[] oldgradient = (double[]) history.get("gradient"); - double[] wstepsize = (double[]) history.get("wstepsize"); + double[] oldgradient = indy.hasData(gradientKey) ? (double[]) indy.getData(gradientKey) : null; + double[] wstepsize = (double[]) indy.getData(wStepSizeKey); double[] oldchange = null; double[] gradient = ((InterfaceFirstOrderDerivableProblem) m_Problem).getFirstOrderGradients(params); - if ((oldgradient != null) && (wstepsize != null)) { + if ((oldgradient != null) && (wstepsize != null)) { // LOCAL adaption for (int li = 0; li < wstepsize.length; li++) { double prod = gradient[li] * oldgradient[li]; if (prod < 0) { - wstepsize[li] = localnminus * wstepsize[li]; + wstepsize[li] = wDecreaseStepSize * wstepsize[li]; } else if (prod > 0) { - wstepsize[li] = localnplus * wstepsize[li]; + wstepsize[li] = wIncreaseStepSize * wstepsize[li]; } wstepsize[li] = (wstepsize[li] < localminstepsize) ? localminstepsize : wstepsize[li]; wstepsize[li] = (wstepsize[li] > localmaxstepsize) ? localmaxstepsize : wstepsize[li]; - //System.out.println("wstepsize "+ li + " " + wstepsize[li]); +// System.out.println("wstepsize "+ li + " " + wstepsize[li]); } } double[] newparams = new double[params.length]; - history.put("gradient", gradient); + indy.putData(gradientKey, gradient); double[] change = new double[params.length]; - if (history.containsKey("changes")) { - oldchange =(double[]) history.get("changes"); + if (indy.hasData(changesKey)) { + oldchange =(double[]) indy.getData(changesKey); } boolean dograddesc = (this.momentumterm) && (oldchange != null); for (int j = 0; j < newparams.length; j++) { if (lock[j] == 0) { double tempstepsize = 1; - if (this.localstepsizeadaption) tempstepsize = tempstepsize *wstepsize[j]; - if (this.globalstepsizeadaption) tempstepsize = tempstepsize *indystepsize; + if (this.localStepSizeAdaption) tempstepsize = tempstepsize * wstepsize[j]; + if (this.globalStepSizeAdaption) tempstepsize = tempstepsize * indystepsize; double wchange = signum(tempstepsize * gradient[j]) * Math.min(maximumabsolutechange,Math.abs(tempstepsize * gradient[j])); //indystepsize * gradient[j]; if (this.manhattan) wchange = this.signum(wchange) * tempstepsize; if (dograddesc) { @@ -181,26 +185,31 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser } params = newparams; - history.put("changes", change); + indy.putData(changesKey, change); - } + } // end loop iterations ((InterfaceDataTypeDouble) indy).SetDoubleGenotype(params); + } // end if ((this.m_Problem instanceof InterfaceFirstOrderDerivableProblem) && (indy instanceof InterfaceDataTypeDouble)) { + else { + String msg="Warning, problem of type InterfaceFirstOrderDerivableProblem and template of type InterfaceDataTypeDouble is required for " + this.getClass(); + EVAERROR.errorMsgOnce(msg); + throw new RuntimeException(msg); } - } + } // for loop population size this.m_Problem.evaluate(this.m_Population); if (this.recovery) { for (int i = 0; i < this.m_Population.size(); i++) { indy = ((AbstractEAIndividual)this.m_Population.get(i)); - Hashtable history = (Hashtable) indyhash.get(indy); +// Hashtable history = (Hashtable) indyhash.get(indy); if (indy.getFitness()[0] > recoverythreshold) { System.out.println("Gradient Descent: Fitness critical:" + indy.getFitness()[0]); - ((InterfaceDataTypeDouble) indy).SetDoublePhenotype((double[]) history.get("params")); - double[] changes = (double[]) history.get("changes"); - int[] lock = (int[]) history.get("lock"); + ((InterfaceDataTypeDouble) indy).SetDoublePhenotype((double[]) indy.getData(oldParamsKey)); + double[] changes = (double[]) indy.getData(changesKey); + int[] lock = (int[]) indy.getData(lockKey); int indexmaxchange = 0; double maxchangeval = Double.NEGATIVE_INFINITY; @@ -211,37 +220,37 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser } } lock[indexmaxchange] = recoverylocksteps; - history.put("lock", lock); + indy.putData(lockKey, lock); } else { } } this.m_Problem.evaluate(this.m_Population); } - if (this.globalstepsizeadaption) { + if (this.globalStepSizeAdaption) { //System.out.println("gsa main"); for (int i = 0; i < this.m_Population.size(); i++) { indy = ((AbstractEAIndividual)this.m_Population.get(i)); - Hashtable history = (Hashtable) indyhash.get(indy); - if (history == null) break; - if (history.get("lastfitness") != null) { - double lastfit = ((Double) history.get("lastfitness")).doubleValue(); - double indystepsize = ((Double) history.get("stepsize")).doubleValue(); +// Hashtable history = (Hashtable) indyhash.get(indy); +// if (history == null) break; + if (indy.getData(lastFitnessKey) != null) { + double lastfit = ((Double) indy.getData(lastFitnessKey)).doubleValue(); + double indystepsize = ((Double) indy.getData(stepSizeKey)).doubleValue(); - if (lastfit < indy.getFitness()[0]) { - indystepsize *= 0.5; + if (lastfit < indy.getFitness()[0]) { // GLOBAL adaption + indystepsize *= wDecreaseStepSize; } else { - indystepsize *= 1.1; + indystepsize *= wIncreaseStepSize; } //System.out.println("newstepsize" + indystepsize); indystepsize = (indystepsize > globalmaxstepsize) ? globalmaxstepsize : indystepsize; indystepsize = (indystepsize < globalminstepsize) ? globalminstepsize : indystepsize; - history.put("stepsize", new Double(indystepsize)); + indy.putData(stepSizeKey, new Double(indystepsize)); } //System.out.println("newstepsize in bounds" + indystepsize); - history.put("lastfitness", new Double(indy.getFitness()[0])); + indy.putData(lastFitnessKey, new Double(indy.getFitness()[0])); } } @@ -265,11 +274,11 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser } public void setPopulation(Population pop) { - Hashtable newindyhash = new Hashtable(); - for (int i = 0; i < pop.size(); i++) { - if (indyhash.contains(pop.get(i))) newindyhash.put(pop.get(i), indyhash.get(pop.get(i))); - } - indyhash = newindyhash; +// Hashtable newindyhash = new Hashtable(); +// for (int i = 0; i < pop.size(); i++) { +// if (indyhash.contains(pop.get(i))) newindyhash.put(pop.get(i), indyhash.get(pop.get(i))); +// } +// indyhash = newindyhash; this.m_Population = pop; } @@ -298,7 +307,6 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser return "GradientDescentAlgorithm"; } - public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { this.m_Listener = ea; } @@ -324,143 +332,163 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser } } + public void freeWilly() { } - - public void freeWilly() { - + public String globalInfo() { + return "Gradient Descent can be applied to derivable functions ("+InterfaceFirstOrderDerivableProblem.class.getSimpleName()+")."; } - - - public double getGlobalMaxstepsize() { +//////////////// for global adaption + public boolean isAdaptStepSizeGlobally() { + return globalStepSizeAdaption; + } + public void setAdaptStepSizeGlobally(boolean globalstepsizeadaption) { + this.globalStepSizeAdaption = globalstepsizeadaption; + if (globalstepsizeadaption && localStepSizeAdaption) setAdaptStepSizeLocally(false); + } + public String adaptStepSizeGloballyTipText() { + return "Use a single step size per individual - (priority over local step size)."; + } + + public double getGlobalMaxStepSize() { return globalmaxstepsize; } - - public void setGlobalMaxstepsize(double p) { + public void setGlobalMaxStepSize(double p) { globalmaxstepsize = p; } + public String globalMaxStepSizeTipText() { + return "Maximum step size for global adaption."; + } - - public double getGlobalMinstepsize() { + public double getGlobalMinStepSize() { return globalminstepsize; } - - public void setGlobalMinstepsize(double p) { + public void setGlobalMinStepSize(double p) { globalminstepsize = p; } - - public int getRecoveryLocksteps() { - return recoverylocksteps; + public String globalMindStepSizeTipText() { + return "Minimum step size for global adaption."; } - - public void setRecoveryLocksteps(int locksteps) { - this.recoverylocksteps = locksteps; - } - - public double getGlobalInitstepsize() { + + public double getGlobalInitStepSize() { return globalinitstepsize; } - - public void setGlobalInitstepsize(double initstepsize) { + public void setGlobalInitStepSize(double initstepsize) { this.globalinitstepsize = initstepsize; } - public boolean isLocalStepsizeadaption() { - return localstepsizeadaption; + public String globalInitStepSizeTipText() { + return "Initial step size for global adaption."; } - public void setLocalStepsizeadaption(boolean stepsizeadaption) { - this.localstepsizeadaption = stepsizeadaption; + + //////////////// for local adaption + public boolean isAdaptStepSizeLocally() { + return localStepSizeAdaption; } + public void setAdaptStepSizeLocally(boolean stepsizeadaption) { + this.localStepSizeAdaption = stepsizeadaption; + if (globalStepSizeAdaption && localStepSizeAdaption) setAdaptStepSizeGlobally(false); + } + public String adaptStepSizeLocallyTipText() { + return "Use a step size parameter in any dimension."; + } + + public double getLocalMinStepSize() { + return localminstepsize; + } + public void setLocalMinStepSize(double localminstepsize) { + this.localminstepsize = localminstepsize; + } + + public double getLocalMaxStepSize() { + return localmaxstepsize; + } + public void setLocalMaxStepSize(double localmaxstepsize) { + this.localmaxstepsize = localmaxstepsize; + } + + public void setStepSizeIncreaseFact(double nplus) { + this.wIncreaseStepSize = nplus; + } + public double getStepSizeIncreaseFact() { + return wIncreaseStepSize; + } + public String stepSizeIncreaseFactTipText() { + return "Factor for increasing the step size in adaption."; + } + + public void setStepSizeDecreaseFact(double nminus) { + this.wDecreaseStepSize = nminus; + } + public double getStepSizeDecreaseFact() { + return wDecreaseStepSize; + } + public String stepSizeDecreaseFactTipText() { + return "Factor for decreasing the step size in adaption."; + } + +//////////////// concerning recovery public boolean isRecovery() { - return recovery; + return recovery; } public void setRecovery(boolean recovery) { - this.recovery = recovery; + this.recovery = recovery; } - public double getLocalNplus() { - return localnplus; + public int getRecoveryLocksteps() { + return recoverylocksteps; } - public double getLocalNminus() { - return localnminus; + public void setRecoveryLocksteps(int locksteps) { + this.recoverylocksteps = locksteps; } - public void setLocalNplus(double nplus) { - this.localnplus = nplus; + public double getRecoveryThreshold() { + return recoverythreshold; } - public void setLocalNminus(double nminus) { - this.localnminus = nminus; + public void setRecoveryThreshold(double recoverythreshold) { + this.recoverythreshold = recoverythreshold; } + public String recoveryThresholdTipText() { + return "If the fitness exceeds this threshold, an unstable area is assumed and one step recovered."; + } + + public int getIterations() { return iterations; } public void setIterations(int iterations) { this.iterations = iterations; } - public boolean isGlobalstepsizeadaption() { - return globalstepsizeadaption; - } - public void setGlobalstepsizeadaption(boolean globalstepsizeadaption) { - this.globalstepsizeadaption = globalstepsizeadaption; - } - public double getLocalminstepsize() { - return localminstepsize; - } - public double getLocalmaxstepsize() { - return localmaxstepsize; - } - public void setLocalminstepsize(double localminstepsize) { - this.localminstepsize = localminstepsize; - } - public void setLocalmaxstepsize(double localmaxstepsize) { - this.localmaxstepsize = localmaxstepsize; + public String iterationsTipText() { + return "The number of GD-iterations per generation."; } + + public boolean isManhattan() { return manhattan; } - - - - public boolean isMomentumTerm() { - return momentumterm; - } - - public double getMomentumweigth() { - return momentumweigth; - } - public void setManhattan(boolean manhattan) { this.manhattan = manhattan; } - - + public boolean isMomentumTerm() { + return momentumterm; + } public void setMomentumTerm(boolean momentum) { this.momentumterm = momentum; } - - public void setMomentumweigth(double momentumweigth) { + + public double getMomentumWeigth() { + return momentumweigth; + } + public void setMomentumWeigth(double momentumweigth) { this.momentumweigth = momentumweigth; } - public void setPopulationSize(int p) { - this.getPopulation().setPopulationSize(p); - } - - public int GetPopulationSize() { - return this.getPopulation().getPopulationSize(); - } - - public double getRecoverythreshold() { - return recoverythreshold; - } - public void setRecoverythreshold(double recoverythreshold) { - this.recoverythreshold = recoverythreshold; - } - - public double getMaximumabsolutechange() { + public double getMaximumAbsoluteChange() { return maximumabsolutechange; } - - - public void setMaximumabsolutechange(double maximumabsolutechange) { + public void setMaximumAbsoluteChange(double maximumabsolutechange) { this.maximumabsolutechange = maximumabsolutechange; } + public String maximumAbsoluteChangeTipText() { + return "The maximum change along a coordinate in one step."; + } } diff --git a/src/eva2/server/go/strategies/HillClimbing.java b/src/eva2/server/go/strategies/HillClimbing.java index 49391432..9bdd51bc 100644 --- a/src/eva2/server/go/strategies/HillClimbing.java +++ b/src/eva2/server/go/strategies/HillClimbing.java @@ -38,7 +38,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable { public HillClimbing() { this.m_Population = new Population(); - this.m_Population.setPopulationSize(10); + this.m_Population.setTargetSize(10); } public HillClimbing(HillClimbing a) { diff --git a/src/eva2/server/go/strategies/IslandModelEA.java b/src/eva2/server/go/strategies/IslandModelEA.java index 7bfa04ac..4618bd4b 100644 --- a/src/eva2/server/go/strategies/IslandModelEA.java +++ b/src/eva2/server/go/strategies/IslandModelEA.java @@ -336,7 +336,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I if (false) { imea.m_Optimizer = new MultiObjectiveEA(); ((MultiObjectiveEA)imea.m_Optimizer).setArchiveSize(25); - ((MultiObjectiveEA)imea.m_Optimizer).getPopulation().setPopulationSize(50); + ((MultiObjectiveEA)imea.m_Optimizer).getPopulation().setTargetSize(50); imea.m_Problem = new TF1Problem(); ((TF1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData()); // ((TF1Problem)imea.m_Problem).setEAIndividual(new ESIndividualDoubleData()); diff --git a/src/eva2/server/go/strategies/MonteCarloSearch.java b/src/eva2/server/go/strategies/MonteCarloSearch.java index 79e7c838..388b41f2 100644 --- a/src/eva2/server/go/strategies/MonteCarloSearch.java +++ b/src/eva2/server/go/strategies/MonteCarloSearch.java @@ -42,7 +42,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl public MonteCarloSearch() { this.m_Population = new Population(); - this.m_Population.setPopulationSize(50); + this.m_Population.setTargetSize(50); } public MonteCarloSearch(MonteCarloSearch a) { diff --git a/src/eva2/server/go/strategies/MultiObjectiveEA.java b/src/eva2/server/go/strategies/MultiObjectiveEA.java index ddf103d3..4e783793 100644 --- a/src/eva2/server/go/strategies/MultiObjectiveEA.java +++ b/src/eva2/server/go/strategies/MultiObjectiveEA.java @@ -48,7 +48,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl transient private InterfacePopulationChangedEventListener m_Listener; public MultiObjectiveEA() { - this.m_Optimizer.getPopulation().setPopulationSize(100); + this.m_Optimizer.getPopulation().setTargetSize(100); ((GeneticAlgorithm)this.m_Optimizer).setParentSelection(new SelectMONonDominated()); ((GeneticAlgorithm)this.m_Optimizer).setPartnerSelection(new SelectMONonDominated()); } @@ -297,7 +297,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl archive = new Population(); this.m_Optimizer.getPopulation().SetArchive(archive); } - return archive.getPopulationSize(); + return archive.getTargetSize(); } public void setArchiveSize(int b){ Population archive = this.m_Optimizer.getPopulation().getArchive(); @@ -305,7 +305,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl archive = new Population(); this.m_Optimizer.getPopulation().SetArchive(archive); } - archive.setPopulationSize(b); + archive.setTargetSize(b); } public String archiveSizeTipText() { return "Choose the size of the archive."; diff --git a/src/eva2/server/go/strategies/NelderMeadSimplex.java b/src/eva2/server/go/strategies/NelderMeadSimplex.java index 9d595540..216aacb4 100644 --- a/src/eva2/server/go/strategies/NelderMeadSimplex.java +++ b/src/eva2/server/go/strategies/NelderMeadSimplex.java @@ -312,8 +312,8 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte public void setPopulationSize(int populationSize) { this.populationSize = populationSize; if (m_Population!=null) { - m_Population.setPopulationSize(populationSize); - m_Population.setNotifyEvalInterval(m_Population.getPopulationSize()); + m_Population.setTargetSize(populationSize); + m_Population.setNotifyEvalInterval(m_Population.getTargetSize()); } } @@ -430,7 +430,7 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte indy.resetConstraintViolation(); initialPop.add((AbstractEAIndividual)indy.clone()); } - initialPop.setPopulationSize(initialPop.size()); + initialPop.synchSize(); } /** diff --git a/src/eva2/server/go/strategies/ParticleFilterOptimization.java b/src/eva2/server/go/strategies/ParticleFilterOptimization.java index f91a457d..b6fa5ea4 100644 --- a/src/eva2/server/go/strategies/ParticleFilterOptimization.java +++ b/src/eva2/server/go/strategies/ParticleFilterOptimization.java @@ -100,7 +100,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S } else { (((AbstractOptimizationProblem)m_Problem).getIndividualTemplate()).setMutationOperator(new MutateESCorrVector(mutationSigma, initialVelocity, rotationDeg)); } - m_Population.setPopSize(popSize); + m_Population.setTargetSize(popSize); this.m_Problem.initPopulation(this.m_Population); setWithShow(withShow); @@ -144,13 +144,13 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S // Generate a Population of Parents with Parantselectionmethod. // DONT forget cloning -> selection does only shallow copies! - int targetSize = this.m_Population.getPopulationSize(); + int targetSize = this.m_Population.getTargetSize(); if (randomImmigrationQuota>0) { if (randomImmigrationQuota>1.) System.err.println("Error, invalid immigration quota!"); else { - targetSize = (int)(this.m_Population.getPopulationSize() * (1.-randomImmigrationQuota)); + targetSize = (int)(this.m_Population.getTargetSize() * (1.-randomImmigrationQuota)); targetSize = Math.max(1, targetSize); // guarantee at least one to be selected - if (targetSize < this.m_Population.getPopulationSize()) doImmigr=true; + if (targetSize < this.m_Population.getTargetSize()) doImmigr=true; } } @@ -160,7 +160,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S // add immigrants AbstractEAIndividual immi; int i; - for (i=0; (i+parents.getPopulationSize())0) { + if (rest.length()<=maxLen) { + nextBreak = rest.length()-1; + } else { + nextBreak = getNextBreak(minLen, maxLen, breakChars, rest); // search for a break character in a certain interval + if (nextBreak<0) nextBreak = len; // if none found force the break at the intended length + } + if (res.length()>0) res.append("\n"); // insert newline + res.append(rest.substring(0, nextBreak+1)); + rest = rest.substring(nextBreak+1); + } + return res.toString(); + } + + public static int getNextBreak(int startIndex, int endIndex, char[] brkChars, String str) { + int index; + for (int i=0; i=0 && (index <= endIndex)) return index; + } + return -1; + } }