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Refactored "SetProblem" function to "setProblem". Coding Standards for the win.

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This commit is contained in:
Fabian Becker 2013-01-25 13:20:25 +00:00
parent ffd4041594
commit ef36d09218
53 changed files with 109 additions and 109 deletions
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22
src/eva2/OptimizerFactory.java
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@ -127,7 +127,7 @@ public class OptimizerFactory {
setTemplateOperators(problem, new NoMutation(), 0, new NoCrossover(), 0); setTemplateOperators(problem, new NoMutation(), 0, new NoCrossover(), 0);
DifferentialEvolution de = new DifferentialEvolution(); DifferentialEvolution de = new DifferentialEvolution();
de.SetProblem(problem); de.setProblem(problem);
de.getPopulation().setTargetSize(popsize); de.getPopulation().setTargetSize(popsize);
de.setDEType(DETypeEnum.DE2_CurrentToBest); de.setDEType(DETypeEnum.DE2_CurrentToBest);
de.setF(f); de.setF(f);
@ -208,7 +208,7 @@ public class OptimizerFactory {
// theES.setParentSelection(selection); // theES.setParentSelection(selection);
// theES.setPartnerSelection(selection); // theES.setPartnerSelection(selection);
theES.setEnvironmentSelection(selection); theES.setEnvironmentSelection(selection);
theES.SetProblem(problem); theES.setProblem(problem);
theES.init(); theES.init();
if (listener != null) listener.registerPopulationStateChanged(theES.getPopulation(), ""); if (listener != null) listener.registerPopulationStateChanged(theES.getPopulation(), "");
@ -240,7 +240,7 @@ public class OptimizerFactory {
setTemplateOperators(problem, mut, pm, cross, pc); setTemplateOperators(problem, mut, pm, cross, pc);
GeneticAlgorithm ga = new GeneticAlgorithm(); GeneticAlgorithm ga = new GeneticAlgorithm();
ga.SetProblem(problem); ga.setProblem(problem);
ga.getPopulation().setTargetSize(popsize); ga.getPopulation().setTargetSize(popsize);
ga.setParentSelection(select); ga.setParentSelection(select);
ga.setPartnerSelection(select); ga.setPartnerSelection(select);
@ -308,7 +308,7 @@ public class OptimizerFactory {
InterfacePopulationChangedEventListener listener) { InterfacePopulationChangedEventListener listener) {
problem.initProblem(); problem.initProblem();
subOpt.SetProblem(problem); subOpt.setProblem(problem);
return new MultiObjectiveEA(subOpt, archiving, archiveSize, return new MultiObjectiveEA(subOpt, archiving, archiveSize,
infoRetrieval, problem); infoRetrieval, problem);
@ -376,7 +376,7 @@ public class OptimizerFactory {
hc.setIdentifier("-"+popSize+"-"+mutator.getStringRepresentation()); hc.setIdentifier("-"+popSize+"-"+mutator.getStringRepresentation());
hc.getPopulation().setTargetSize(popSize); hc.getPopulation().setTargetSize(popSize);
hc.addPopulationChangedEventListener(listener); hc.addPopulationChangedEventListener(listener);
hc.SetProblem(problem); hc.setProblem(problem);
hc.init(); hc.init();
if (listener != null) listener.registerPopulationStateChanged(hc.getPopulation(), ""); if (listener != null) listener.registerPopulationStateChanged(hc.getPopulation(), "");
@ -403,7 +403,7 @@ public class OptimizerFactory {
MonteCarloSearch mc = new MonteCarloSearch(); MonteCarloSearch mc = new MonteCarloSearch();
mc.getPopulation().setTargetSize(popsize); mc.getPopulation().setTargetSize(popsize);
mc.addPopulationChangedEventListener(listener); mc.addPopulationChangedEventListener(listener);
mc.SetProblem(problem); mc.setProblem(problem);
mc.init(); mc.init();
if (listener != null) listener.registerPopulationStateChanged(mc.getPopulation(), ""); if (listener != null) listener.registerPopulationStateChanged(mc.getPopulation(), "");
@ -437,7 +437,7 @@ public class OptimizerFactory {
setTemplateOperators(problem, new NoMutation(), 0, new NoCrossover(), 0); setTemplateOperators(problem, new NoMutation(), 0, new NoCrossover(), 0);
ParticleSwarmOptimization pso = new ParticleSwarmOptimization(); ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
pso.SetProblem(problem); pso.setProblem(problem);
pso.getPopulation().setTargetSize(popsize); pso.getPopulation().setTargetSize(popsize);
pso.setPhi1(phi1); pso.setPhi1(phi1);
pso.setPhi2(phi2); pso.setPhi2(phi2);
@ -480,7 +480,7 @@ public class OptimizerFactory {
SimulatedAnnealing sa = new SimulatedAnnealing(); SimulatedAnnealing sa = new SimulatedAnnealing();
sa.setAlpha(alpha); sa.setAlpha(alpha);
sa.setInitialTemperature(temperature); sa.setInitialTemperature(temperature);
sa.SetProblem(problem); sa.setProblem(problem);
sa.getPopulation().setTargetSize(popsize); sa.getPopulation().setTargetSize(popsize);
sa.addPopulationChangedEventListener(listener); sa.addPopulationChangedEventListener(listener);
sa.init(); sa.init();
@ -518,7 +518,7 @@ public class OptimizerFactory {
pbil.setPositiveSamples(positiveSamples); pbil.setPositiveSamples(positiveSamples);
pbil.addPopulationChangedEventListener(listener); pbil.addPopulationChangedEventListener(listener);
pbil.SetProblem(problem); pbil.setProblem(problem);
if (listener != null) listener.registerPopulationStateChanged(pbil.getPopulation(), ""); if (listener != null) listener.registerPopulationStateChanged(pbil.getPopulation(), "");
@ -763,7 +763,7 @@ public class OptimizerFactory {
InterfaceTerminator term) { InterfaceTerminator term) {
GOParameters params = new GOParameters(); GOParameters params = new GOParameters();
params.setProblem(problem); params.setProblem(problem);
opt.SetProblem(problem); opt.setProblem(problem);
opt.setPopulation(pop); opt.setPopulation(pop);
params.setOptimizer(opt); params.setOptimizer(opt);
params.setTerminator(term); params.setTerminator(term);
@ -1340,7 +1340,7 @@ public class OptimizerFactory {
AbstractOptimizationProblem problem, int evalCycle, int popSize, double minImprovement, AbstractOptimizationProblem problem, int evalCycle, int popSize, double minImprovement,
PostProcessMethod method, double hcInitialStep, double hcStepThresh, double sigmaClust) { PostProcessMethod method, double hcInitialStep, double hcStepThresh, double sigmaClust) {
ClusteringHillClimbing chc = new ClusteringHillClimbing(); ClusteringHillClimbing chc = new ClusteringHillClimbing();
chc.SetProblem(problem); chc.setProblem(problem);
chc.setEvalCycle(evalCycle); chc.setEvalCycle(evalCycle);
chc.setInitialPopSize(popSize); chc.setInitialPopSize(popSize);

6
src/eva2/server/go/GOStandaloneVersion.java
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@ -312,7 +312,7 @@ public class GOStandaloneVersion implements InterfaceGOStandalone, InterfacePopu
((GAIndividualDoubleData)tmpIndy).setMutationProbability(1.0); ((GAIndividualDoubleData)tmpIndy).setMutationProbability(1.0);
((F1Problem)problem).setEAIndividual(tmpIndy); ((F1Problem)problem).setEAIndividual(tmpIndy);
//((FGRNInferringProblem)this.m_Problem).setStructreSkelInterval(1); //((FGRNInferringProblem)this.m_Problem).setStructreSkelInterval(1);
this.m_GO.getOptimizer().SetProblem(problem); this.m_GO.getOptimizer().setProblem(problem);
this.m_GO.getOptimizer().addPopulationChangedEventListener(this); this.m_GO.getOptimizer().addPopulationChangedEventListener(this);
this.doWork(); this.doWork();
break; break;
@ -329,7 +329,7 @@ public class GOStandaloneVersion implements InterfaceGOStandalone, InterfacePopu
((F1Problem)problem).setEAIndividual(tmpIndy); ((F1Problem)problem).setEAIndividual(tmpIndy);
//((FGRNInferringProblem)this.m_Problem).setUseHEigenMatrix(true); //((FGRNInferringProblem)this.m_Problem).setUseHEigenMatrix(true);
//((FGRNInferringProblem)this.m_Problem).setUseOnlyPositiveNumbers(true); //((FGRNInferringProblem)this.m_Problem).setUseOnlyPositiveNumbers(true);
this.m_GO.getOptimizer().SetProblem(problem); this.m_GO.getOptimizer().setProblem(problem);
this.m_GO.getOptimizer().addPopulationChangedEventListener(this); this.m_GO.getOptimizer().addPopulationChangedEventListener(this);
this.doWork(); this.doWork();
break; break;
@ -375,7 +375,7 @@ public class GOStandaloneVersion implements InterfaceGOStandalone, InterfacePopu
// init problem // init problem
this.m_GO.getProblem().initProblem(); this.m_GO.getProblem().initProblem();
this.m_GO.getOptimizer().SetProblem(this.m_GO.getProblem()); this.m_GO.getOptimizer().setProblem(this.m_GO.getProblem());
// int optimizer and population // int optimizer and population
//this.m_GO.getOptimizer().init(); //this.m_GO.getOptimizer().init();

2
src/eva2/server/go/MOCCOStandalone.java
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@ -363,7 +363,7 @@ public class MOCCOStandalone implements InterfaceGOStandalone, InterfacePopulati
if (this.m_JFrame != null) { if (this.m_JFrame != null) {
} }
this.m_StillWorking = true; this.m_StillWorking = true;
this.m_State.m_Optimizer.SetProblem(this.m_State.m_CurrentProblem); this.m_State.m_Optimizer.setProblem(this.m_State.m_CurrentProblem);
if (this.m_Debug) System.out.println(""+this.m_State.m_Optimizer.getStringRepresentation()); if (this.m_Debug) System.out.println(""+this.m_State.m_Optimizer.getStringRepresentation());
this.m_State.m_CurrentProblem.evaluate(this.m_State.m_Optimizer.getPopulation()); this.m_State.m_CurrentProblem.evaluate(this.m_State.m_Optimizer.getPopulation());
this.m_State.m_Optimizer.getPopulation().SetFunctionCalls(0); this.m_State.m_Optimizer.getPopulation().SetFunctionCalls(0);

4
src/eva2/server/go/mocco/MOCCOParameterizeGDF.java
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@ -125,7 +125,7 @@ public class MOCCOParameterizeGDF extends MOCCOPhase implements InterfaceProcess
gbc.gridwidth = 1; gbc.gridwidth = 1;
this.m_EOpt = new GeneralGOEProperty(); this.m_EOpt = new GeneralGOEProperty();
this.m_Opt = new GeneticAlgorithm(); this.m_Opt = new GeneticAlgorithm();
this.m_Opt.SetProblem(this.m_Mocco.m_State.m_CurrentProblem); this.m_Opt.setProblem(this.m_Mocco.m_State.m_CurrentProblem);
this.m_Mocco.m_State.m_Optimizer = this.m_Opt; this.m_Mocco.m_State.m_Optimizer = this.m_Opt;
this.m_EOpt.m_Name = "Island Model EA"; this.m_EOpt.m_Name = "Island Model EA";
try { try {
@ -217,7 +217,7 @@ public class MOCCOParameterizeGDF extends MOCCOPhase implements InterfaceProcess
PropertyDoubleArray da = new PropertyDoubleArray(w); PropertyDoubleArray da = new PropertyDoubleArray(w);
wf.setOutputDimension(da.getNumRows()); wf.setOutputDimension(da.getNumRows());
wf.setWeights(da); wf.setWeights(da);
m_Opt.SetProblem(m_Mocco.m_State.m_CurrentProblem); m_Opt.setProblem(m_Mocco.m_State.m_CurrentProblem);
m_Mocco.m_State.m_Optimizer = m_Opt; m_Mocco.m_State.m_Optimizer = m_Opt;
m_Mocco.m_JPanelControl.removeAll(); m_Mocco.m_JPanelControl.removeAll();
m_Mocco.m_JPanelControl.validate(); m_Mocco.m_JPanelControl.validate();

2
src/eva2/server/go/mocco/MOCCOParameterizeMO.java
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@ -138,7 +138,7 @@ public class MOCCOParameterizeMO extends MOCCOPhase implements InterfaceProcessE
//m_Mocco.m_State.m_Optimizer = (InterfaceOptimizer)m_Optimizer.clone(); //m_Mocco.m_State.m_Optimizer = (InterfaceOptimizer)m_Optimizer.clone();
m_Mocco.m_JPanelControl.removeAll(); m_Mocco.m_JPanelControl.removeAll();
m_Mocco.m_JPanelParameters.removeAll(); m_Mocco.m_JPanelParameters.removeAll();
m_Mocco.m_State.m_Optimizer.SetProblem(m_Mocco.m_State.m_CurrentProblem); m_Mocco.m_State.m_Optimizer.setProblem(m_Mocco.m_State.m_CurrentProblem);
Population pop = m_Mocco.m_State.m_Optimizer.getPopulation(); Population pop = m_Mocco.m_State.m_Optimizer.getPopulation();
pop.clear(); pop.clear();
if (pop.getArchive() != null) pop.getArchive().clear(); if (pop.getArchive() != null) pop.getArchive().clear();

4
src/eva2/server/go/mocco/MOCCOParameterizeRefPoint.java
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@ -137,7 +137,7 @@ public class MOCCOParameterizeRefPoint extends MOCCOPhase implements InterfacePr
this.m_Island.setMigrationRate(2); this.m_Island.setMigrationRate(2);
this.m_Island.setMigrationStrategy(new SOBestMigration()); this.m_Island.setMigrationStrategy(new SOBestMigration());
this.m_Island.setNumberLocalCPUs(this.m_Perturbations); this.m_Island.setNumberLocalCPUs(this.m_Perturbations);
this.m_Island.SetProblem(this.m_Mocco.m_State.m_CurrentProblem); this.m_Island.setProblem(this.m_Mocco.m_State.m_CurrentProblem);
this.m_Mocco.m_State.m_Optimizer = this.m_Island; this.m_Mocco.m_State.m_Optimizer = this.m_Island;
this.m_EIMEA.m_Name = "Island Model EA"; this.m_EIMEA.m_Name = "Island Model EA";
try { try {
@ -234,7 +234,7 @@ public class MOCCOParameterizeRefPoint extends MOCCOPhase implements InterfacePr
// m_Island.setNumberLocalCPUs(m_Perturbations); // m_Island.setNumberLocalCPUs(m_Perturbations);
// } // }
m_Mocco.m_State.m_Optimizer = m_Island; m_Mocco.m_State.m_Optimizer = m_Island;
m_Mocco.m_State.m_Optimizer.SetProblem(m_Mocco.m_State.m_CurrentProblem); m_Mocco.m_State.m_Optimizer.setProblem(m_Mocco.m_State.m_CurrentProblem);
m_Island.init(); m_Island.init();
double[] tmpD; double[] tmpD;
MOSOLpMetric[] tmpLPs = new MOSOLpMetric[m_Perturbations]; MOSOLpMetric[] tmpLPs = new MOSOLpMetric[m_Perturbations];

2
src/eva2/server/go/mocco/MOCCOParameterizeSO.java
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@ -66,7 +66,7 @@ public class MOCCOParameterizeSO extends MOCCOPhase implements InterfaceProcessE
"a Genetic Algorithms, please parameterize accordingly.", "a Genetic Algorithms, please parameterize accordingly.",
"Warning", JOptionPane.WARNING_MESSAGE); "Warning", JOptionPane.WARNING_MESSAGE);
this.m_Mocco.m_State.m_Optimizer = new GeneticAlgorithm(); this.m_Mocco.m_State.m_Optimizer = new GeneticAlgorithm();
this.m_Mocco.m_State.m_Optimizer.SetProblem(this.m_Mocco.m_State.m_CurrentProblem); this.m_Mocco.m_State.m_Optimizer.setProblem(this.m_Mocco.m_State.m_CurrentProblem);
} }
this.m_Mocco.m_JPanelParameters.removeAll(); this.m_Mocco.m_JPanelParameters.removeAll();
this.m_Mocco.m_JPanelParameters.setLayout(new BorderLayout()); this.m_Mocco.m_JPanelParameters.setLayout(new BorderLayout());

4
src/eva2/server/go/mocco/MOCCOParameterizeSTEP.java
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@ -167,7 +167,7 @@ public class MOCCOParameterizeSTEP extends MOCCOPhase implements InterfaceProces
gbc.gridwidth = 1; gbc.gridwidth = 1;
this.m_EOpt = new GeneralGOEProperty(); this.m_EOpt = new GeneralGOEProperty();
this.m_Opt = new GeneticAlgorithm(); this.m_Opt = new GeneticAlgorithm();
this.m_Opt.SetProblem(this.m_Mocco.m_State.m_CurrentProblem); this.m_Opt.setProblem(this.m_Mocco.m_State.m_CurrentProblem);
this.m_Mocco.m_State.m_Optimizer = this.m_Opt; this.m_Mocco.m_State.m_Optimizer = this.m_Opt;
this.m_EOpt.m_Name = "Island Model EA"; this.m_EOpt.m_Name = "Island Model EA";
try { try {
@ -294,7 +294,7 @@ public class MOCCOParameterizeSTEP extends MOCCOPhase implements InterfaceProces
double[] setWeights = mapObjectives2Fitness(weights); double[] setWeights = mapObjectives2Fitness(weights);
PropertyDoubleArray da = new PropertyDoubleArray(setWeights); PropertyDoubleArray da = new PropertyDoubleArray(setWeights);
wf.setWeights(da); wf.setWeights(da);
m_Opt.SetProblem(m_Mocco.m_State.m_CurrentProblem); m_Opt.setProblem(m_Mocco.m_State.m_CurrentProblem);
m_Mocco.m_State.m_Optimizer = m_Opt; m_Mocco.m_State.m_Optimizer = m_Opt;
m_Mocco.m_JPanelControl.removeAll(); m_Mocco.m_JPanelControl.removeAll();
m_Mocco.m_JPanelControl.validate(); m_Mocco.m_JPanelControl.validate();

4
src/eva2/server/go/mocco/MOCCOParameterizeTchebycheff.java
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@ -114,7 +114,7 @@ public class MOCCOParameterizeTchebycheff extends MOCCOPhase implements Interfac
this.m_Island.setMigrationRate(2); this.m_Island.setMigrationRate(2);
this.m_Island.setMigrationStrategy(new SOBestMigration()); this.m_Island.setMigrationStrategy(new SOBestMigration());
this.m_Island.setNumberLocalCPUs(this.m_Perturbations); this.m_Island.setNumberLocalCPUs(this.m_Perturbations);
this.m_Island.SetProblem(this.m_Mocco.m_State.m_CurrentProblem); this.m_Island.setProblem(this.m_Mocco.m_State.m_CurrentProblem);
this.m_Mocco.m_State.m_Optimizer = this.m_Island; this.m_Mocco.m_State.m_Optimizer = this.m_Island;
this.m_EIMEA.m_Name = "Island Model EA"; this.m_EIMEA.m_Name = "Island Model EA";
try { try {
@ -241,7 +241,7 @@ public class MOCCOParameterizeTchebycheff extends MOCCOPhase implements Interfac
// m_Island.setNumberLocalCPUs(m_Perturbations); // m_Island.setNumberLocalCPUs(m_Perturbations);
// } // }
m_Mocco.m_State.m_Optimizer = m_Island; m_Mocco.m_State.m_Optimizer = m_Island;
m_Mocco.m_State.m_Optimizer.SetProblem(m_Mocco.m_State.m_CurrentProblem); m_Mocco.m_State.m_Optimizer.setProblem(m_Mocco.m_State.m_CurrentProblem);
m_Island.init(); m_Island.init();
double[] tmpD; double[] tmpD;
double sum = 0, l = 0, u = 1; double sum = 0, l = 0, u = 1;

4
src/eva2/server/go/mocco/MOCCOState.java
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@ -56,14 +56,14 @@ public class MOCCOState {
this.m_BackupOptimizer = null; this.m_BackupOptimizer = null;
} }
this.m_Optimizer.setPopulation(pop); this.m_Optimizer.setPopulation(pop);
this.m_Optimizer.SetProblem(this.m_CurrentProblem); this.m_Optimizer.setProblem(this.m_CurrentProblem);
this.m_CurrentProblem.evaluate(this.m_Optimizer.getPopulation()); this.m_CurrentProblem.evaluate(this.m_Optimizer.getPopulation());
} }
public void makeBackup() { public void makeBackup() {
this.m_BackupProblem = (InterfaceOptimizationProblem)this.m_CurrentProblem.clone(); this.m_BackupProblem = (InterfaceOptimizationProblem)this.m_CurrentProblem.clone();
this.m_BackupOptimizer = (InterfaceOptimizer)this.m_Optimizer.clone(); this.m_BackupOptimizer = (InterfaceOptimizer)this.m_Optimizer.clone();
this.m_BackupOptimizer.SetProblem(null); this.m_BackupOptimizer.setProblem(null);
} }
public void addPopulation2History(Population pop) { public void addPopulation2History(Population pop) {

2
src/eva2/server/go/operators/migration/MOClusteringSeparation.java
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@ -201,7 +201,7 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria
// else out += "\n Using objective space."; // else out += "\n Using objective space.";
// System.out.println(""+out); // System.out.println(""+out);
// } // }
islands[i].SetProblem(prob); islands[i].setProblem(prob);
} }
} }
} }

4
src/eva2/server/go/operators/migration/MOConeSeparation.java
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@ -266,7 +266,7 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl
((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.add(b); ((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.add(b);
} }
islands[i].SetProblem(prob); islands[i].setProblem(prob);
} }
} }
} }
@ -360,7 +360,7 @@ public class MOConeSeparation implements InterfaceMigration, java.io.Serializabl
((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.add(sts); ((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.add(sts);
((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.add(bts); ((AbstractMultiObjectiveOptimizationProblem)prob).m_AreaConst4Parallelization.add(bts);
} }
islands[i].SetProblem(prob); islands[i].setProblem(prob);
// if (true) { // if (true) {
// prob.evaluate(newIPOP[i]); // prob.evaluate(newIPOP[i]);
// System.out.println("Invalid Individual in Island "+i+" ("+newIPOP[i].size()+"): "); // System.out.println("Invalid Individual in Island "+i+" ("+newIPOP[i].size()+"): ");

2
src/eva2/server/go/operators/migration/MOXMeansSeparation.java
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@ -207,7 +207,7 @@ public class MOXMeansSeparation implements InterfaceMigration, java.io.Serializa
// else out += "\n Using objective space."; // else out += "\n Using objective space.";
// System.out.println(""+out); // System.out.println(""+out);
// } // }
islands[i].SetProblem(prob); islands[i].setProblem(prob);
} }
} }
} }

4
src/eva2/server/go/operators/postprocess/PostProcess.java
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@ -386,7 +386,7 @@ public class PostProcess {
public static void processWithHC(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term, InterfaceMutation mute) { public static void processWithHC(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term, InterfaceMutation mute) {
HillClimbing hc = new HillClimbing(); HillClimbing hc = new HillClimbing();
// HC depends heavily on the selected mutation operator! // HC depends heavily on the selected mutation operator!
hc.SetProblem(problem); hc.setProblem(problem);
mute.init(problem.getIndividualTemplate(), problem); mute.init(problem.getIndividualTemplate(), problem);
hc.SetMutationOperator(mute); hc.SetMutationOperator(mute);
if (pop.size() != pop.getTargetSize()) { if (pop.size() != pop.getTargetSize()) {
@ -405,7 +405,7 @@ public class PostProcess {
public static int processWithGDA(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term, int baseEvals, double minStepSize, double maxStepSize) { public static int processWithGDA(Population pop, AbstractOptimizationProblem problem, InterfaceTerminator term, int baseEvals, double minStepSize, double maxStepSize) {
GradientDescentAlgorithm gda = new GradientDescentAlgorithm(); GradientDescentAlgorithm gda = new GradientDescentAlgorithm();
gda.setAdaptStepSizeLocally(true); gda.setAdaptStepSizeLocally(true);
gda.SetProblem(problem); gda.setProblem(problem);
gda.setLocalMinStepSize(minStepSize); gda.setLocalMinStepSize(minStepSize);
gda.setLocalMaxStepSize(maxStepSize); gda.setLocalMaxStepSize(maxStepSize);
gda.setRecovery(false); gda.setRecovery(false);

2
src/eva2/server/go/problems/F2Problem.java
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@ -111,7 +111,7 @@ public class F2Problem extends AbstractProblemDoubleOffset implements InterfaceL
private void initLS() { private void initLS() {
localSearchOptimizer = new GradientDescentAlgorithm(); localSearchOptimizer = new GradientDescentAlgorithm();
localSearchOptimizer.SetProblem(this); localSearchOptimizer.setProblem(this);
localSearchOptimizer.init(); localSearchOptimizer.init();
} }

2
src/eva2/server/go/problems/F6Problem.java
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@ -142,7 +142,7 @@ implements InterfaceMultimodalProblem, InterfaceFirstOrderDerivableProblem, Inte
private void initLS() { private void initLS() {
localSearchOptimizer = new GradientDescentAlgorithm(); localSearchOptimizer = new GradientDescentAlgorithm();
localSearchOptimizer.SetProblem(this); localSearchOptimizer.setProblem(this);
localSearchOptimizer.init(); localSearchOptimizer.init();
} }

2
src/eva2/server/go/strategies/BOA.java
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@ -599,7 +599,7 @@ public class BOA implements InterfaceOptimizer, java.io.Serializable {
return this.m_Identifier; return this.m_Identifier;
} }
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.problem = (AbstractOptimizationProblem) problem; this.problem = (AbstractOptimizationProblem) problem;
} }

2
src/eva2/server/go/strategies/BinaryScatterSearch.java
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@ -731,7 +731,7 @@ public class BinaryScatterSearch implements InterfaceOptimizer, java.io.Serializ
return this.m_Identifier; return this.m_Identifier;
} }
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.problem = (AbstractOptimizationProblem) problem; this.problem = (AbstractOptimizationProblem) problem;
} }

4
src/eva2/server/go/strategies/CBNPSO.java
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@ -67,8 +67,8 @@ public class CBNPSO extends ClusterBasedNichingEA implements Serializable {
} }
@Override @Override
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
super.SetProblem(problem); super.setProblem(problem);
if (problem instanceof AbstractProblemDouble) { if (problem instanceof AbstractProblemDouble) {
AbstractProblemDouble dblProb = ((AbstractProblemDouble)problem); AbstractProblemDouble dblProb = ((AbstractProblemDouble)problem);
adaptMinMaxSwarmSizeByDim(dblProb); adaptMinMaxSwarmSizeByDim(dblProb);

2
src/eva2/server/go/strategies/CHCAdaptiveSearchAlgorithm.java
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@ -238,7 +238,7 @@ public class CHCAdaptiveSearchAlgorithm implements InterfaceOptimizer, java.io.S
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

4
src/eva2/server/go/strategies/ClusterBasedNichingEA.java
View File

@ -972,9 +972,9 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem); this.m_Optimizer.setProblem(this.m_Problem);
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {
return this.m_Problem; return this.m_Problem;

2
src/eva2/server/go/strategies/ClusteringHillClimbing.java
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@ -107,7 +107,7 @@ InterfaceOptimizer, Serializable, InterfaceAdditionalPopulationInformer {
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

2
src/eva2/server/go/strategies/DifferentialEvolution.java
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@ -665,7 +665,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = (AbstractOptimizationProblem)problem; this.m_Problem = (AbstractOptimizationProblem)problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

4
src/eva2/server/go/strategies/DynamicParticleSwarmOptimization.java
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@ -487,8 +487,8 @@ public class DynamicParticleSwarmOptimization extends ParticleSwarmOptimization
} }
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
super.SetProblem(problem); super.setProblem(problem);
if (problem instanceof AbstractOptimizationProblem) { if (problem instanceof AbstractOptimizationProblem) {
((AbstractOptimizationProblem)problem).informAboutOptimizer(this); ((AbstractOptimizationProblem)problem).informAboutOptimizer(this);
} }

4
src/eva2/server/go/strategies/EsDpiNiching.java
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@ -243,7 +243,7 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
// Trying to come close to the selection scheme of Shir&Bäck'05: // Trying to come close to the selection scheme of Shir&Bäck'05:
peakOpts[i].setParentSelection(parentSel); peakOpts[i].setParentSelection(parentSel);
peakOpts[i].setPartnerSelection(new SelectBestSingle(true)); peakOpts[i].setPartnerSelection(new SelectBestSingle(true));
peakOpts[i].SetProblem(problem); peakOpts[i].setProblem(problem);
peakOpts[i].init(); peakOpts[i].init();
peakOpts[i].setLambda(lambdaPerPeak); // set lambda after initialization peakOpts[i].setLambda(lambdaPerPeak); // set lambda after initialization
peakOpts[i].setForceOrigPopSize(false); peakOpts[i].setForceOrigPopSize(false);
@ -1079,7 +1079,7 @@ public class EsDpiNiching implements InterfaceOptimizer, Serializable, Interface
return problem; return problem;
} }
public void SetProblem(InterfaceOptimizationProblem prob) { public void setProblem(InterfaceOptimizationProblem prob) {
this.problem = prob; this.problem = prob;
} }

2
src/eva2/server/go/strategies/EvolutionStrategies.java
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@ -268,7 +268,7 @@ public class EvolutionStrategies implements InterfaceOptimizer, java.io.Serializ
* *
* @param problem * @param problem
*/ */
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.optimizationProblem = problem; this.optimizationProblem = problem;
} }

2
src/eva2/server/go/strategies/EvolutionaryProgramming.java
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@ -131,7 +131,7 @@ public class EvolutionaryProgramming implements InterfaceOptimizer, java.io.Seri
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

2
src/eva2/server/go/strategies/FloodAlgorithm.java
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@ -120,7 +120,7 @@ public class FloodAlgorithm implements InterfaceOptimizer, java.io.Serializable
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

2
src/eva2/server/go/strategies/GeneticAlgorithm.java
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@ -197,7 +197,7 @@ public class GeneticAlgorithm implements InterfaceOptimizer, java.io.Serializabl
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

4
src/eva2/server/go/strategies/GradientDescentAlgorithm.java
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@ -324,7 +324,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
return this.m_Identifier; return this.m_Identifier;
} }
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
m_Problem = problem; m_Problem = problem;
} }
@ -350,7 +350,7 @@ public class GradientDescentAlgorithm implements InterfaceOptimizer, java.io.Ser
public static void main(String[] args) { public static void main(String[] args) {
GradientDescentAlgorithm program = new GradientDescentAlgorithm(); GradientDescentAlgorithm program = new GradientDescentAlgorithm();
InterfaceOptimizationProblem problem = new F1Problem(); InterfaceOptimizationProblem problem = new F1Problem();
program.SetProblem(problem); program.setProblem(problem);
program.init(); program.init();
for (int i = 0; i < 100; i++) { for (int i = 0; i < 100; i++) {
program.optimize(); program.optimize();

2
src/eva2/server/go/strategies/HillClimbing.java
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@ -128,7 +128,7 @@ public class HillClimbing implements InterfaceOptimizer, java.io.Serializable {
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

2
src/eva2/server/go/strategies/InterfaceOptimizer.java
View File

@ -94,7 +94,7 @@ public interface InterfaceOptimizer {
* *
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem); public void setProblem (InterfaceOptimizationProblem problem);
public InterfaceOptimizationProblem getProblem (); public InterfaceOptimizationProblem getProblem ();
/** This method will return a string describing all properties of the optimizer /** This method will return a string describing all properties of the optimizer

8
src/eva2/server/go/strategies/IslandModelEA.java
View File

@ -101,7 +101,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Population.clear(); this.m_Population.clear();
this.m_Population.init(); this.m_Population.init();
this.m_Optimizer.init(); this.m_Optimizer.init();
this.m_Optimizer.SetProblem(this.m_Problem); this.m_Optimizer.setProblem(this.m_Problem);
this.m_Optimizer.setPopulation((Population)m_Population.clone()); this.m_Optimizer.setPopulation((Population)m_Population.clone());
InterfacePopulationChangedEventListener myLocal = null; InterfacePopulationChangedEventListener myLocal = null;
if (this.m_localOnly) { if (this.m_localOnly) {
@ -171,7 +171,7 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
this.m_Population.incrGeneration(); this.m_Population.incrGeneration();
} }
this.m_Optimizer.init(); this.m_Optimizer.init();
this.m_Optimizer.SetProblem(this.m_Problem); this.m_Optimizer.setProblem(this.m_Problem);
InterfacePopulationChangedEventListener myLocal = null; InterfacePopulationChangedEventListener myLocal = null;
if (this.m_localOnly) { if (this.m_localOnly) {
// this is running on the local machine // this is running on the local machine
@ -297,9 +297,9 @@ public class IslandModelEA implements InterfacePopulationChangedEventListener, I
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
this.m_Optimizer.SetProblem(problem); this.m_Optimizer.setProblem(problem);
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {
return this.m_Problem; return this.m_Problem;

10
src/eva2/server/go/strategies/LTGA.java
View File

@ -118,7 +118,7 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
@Override @Override
public void init() { public void init() {
defaultInit(); this.defaultInit();
this.problem.initPopulation(this.population); this.problem.initPopulation(this.population);
this.evaluatePopulation(this.population); this.evaluatePopulation(this.population);
this.firePropertyChangedEvent(Population.nextGenerationPerformed); this.firePropertyChangedEvent(Population.nextGenerationPerformed);
@ -150,7 +150,7 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
@Override @Override
public void initByPopulation(Population pop, boolean reset) { public void initByPopulation(Population pop, boolean reset) {
if (reset) { if (reset) {
init(); this.init();
} else { } else {
defaultInit(); defaultInit();
this.population = pop; this.population = pop;
@ -247,7 +247,7 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
@Override @Override
public void optimize() { public void optimize() {
this.problem.evaluatePopulationStart(this.population); this.problem.evaluatePopulationStart(this.population);
Stack<Set<Integer>> linkageTree = buildLinkageTree(); Stack<Set<Integer>> linkageTree = this.buildLinkageTree();
Population newPop = new Population(this.popSize); Population newPop = new Population(this.popSize);
if(elitism){ if(elitism){
Population firstIndies = this.population.getBestNIndividuals(2, fitCrit); Population firstIndies = this.population.getBestNIndividuals(2, fitCrit);
@ -259,7 +259,7 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
continue; continue;
} }
Population indies = this.population.getRandNIndividuals(2); Population indies = this.population.getRandNIndividuals(2);
Population newIndies = buildNewIndies(indies, linkageTree); Population newIndies = this.buildNewIndies(indies, linkageTree);
newPop.addAll(newIndies); newPop.addAll(newIndies);
} }
this.population.clear(); this.population.clear();
@ -341,7 +341,7 @@ public class LTGA implements InterfaceOptimizer, java.io.Serializable, Interface
} }
@Override @Override
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.problem = (AbstractOptimizationProblem) problem; this.problem = (AbstractOptimizationProblem) problem;
} }

2
src/eva2/server/go/strategies/MLTGA.java
View File

@ -320,7 +320,7 @@ public class MLTGA implements InterfaceOptimizer, java.io.Serializable, Interfac
} }
@Override @Override
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.problem = (AbstractOptimizationProblem) problem; this.problem = (AbstractOptimizationProblem) problem;
} }

8
src/eva2/server/go/strategies/MemeticAlgorithm.java
View File

@ -98,7 +98,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
public void init() { public void init() {
// counter = 0; // counter = 0;
this.m_GlobalOptimizer.SetProblem(this.m_Problem); this.m_GlobalOptimizer.setProblem(this.m_Problem);
this.m_GlobalOptimizer.init(); this.m_GlobalOptimizer.init();
this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation()); this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation());
this.firePropertyChangedEvent(Population.nextGenerationPerformed); this.firePropertyChangedEvent(Population.nextGenerationPerformed);
@ -225,9 +225,9 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
* *
* @param problem * @param problem
*/ */
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
this.m_GlobalOptimizer.SetProblem(this.m_Problem); this.m_GlobalOptimizer.setProblem(this.m_Problem);
} }
public InterfaceOptimizationProblem getProblem() { public InterfaceOptimizationProblem getProblem() {
@ -320,7 +320,7 @@ public class MemeticAlgorithm implements InterfaceOptimizer,
*/ */
public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) { public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) {
this.m_GlobalOptimizer = m_GlobalOptimizer; this.m_GlobalOptimizer = m_GlobalOptimizer;
this.m_GlobalOptimizer.SetProblem(this.getProblem()); this.m_GlobalOptimizer.setProblem(this.getProblem());
this.init(); this.init();
} }

2
src/eva2/server/go/strategies/MonteCarloSearch.java
View File

@ -103,7 +103,7 @@ public class MonteCarloSearch implements InterfaceOptimizer, java.io.Serializabl
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

2
src/eva2/server/go/strategies/MultiObjectiveCMAES.java
View File

@ -88,7 +88,7 @@ public class MultiObjectiveCMAES implements InterfaceOptimizer, Serializable {
* eva2.server.go.strategies.InterfaceOptimizer#SetProblem(eva2.server.go * eva2.server.go.strategies.InterfaceOptimizer#SetProblem(eva2.server.go
* .problems.InterfaceOptimizationProblem) * .problems.InterfaceOptimizationProblem)
*/ */
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
m_Problem = (AbstractOptimizationProblem) problem; m_Problem = (AbstractOptimizationProblem) problem;
} }

6
src/eva2/server/go/strategies/MultiObjectiveEA.java
View File

@ -67,7 +67,7 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
setArchivingStrategy(archiving); setArchivingStrategy(archiving);
setArchiveSize(archiveSize); setArchiveSize(archiveSize);
setInformationRetrieval(infoRetrieval); setInformationRetrieval(infoRetrieval);
SetProblem(problem); setProblem(problem);
} }
public Object clone() { public Object clone() {
@ -171,9 +171,9 @@ public class MultiObjectiveEA implements InterfaceOptimizer, java.io.Serializabl
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
this.m_Optimizer.SetProblem(problem); this.m_Optimizer.setProblem(problem);
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {
return this.m_Problem; return this.m_Problem;

4
src/eva2/server/go/strategies/NelderMeadSimplex.java
View File

@ -62,12 +62,12 @@ public class NelderMeadSimplex implements InterfaceOptimizer, Serializable, Inte
m_Identifier = name; m_Identifier = name;
} }
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
m_Problem = (AbstractOptimizationProblem)problem; m_Problem = (AbstractOptimizationProblem)problem;
} }
public boolean setProblemAndPopSize(InterfaceOptimizationProblem problem) { public boolean setProblemAndPopSize(InterfaceOptimizationProblem problem) {
SetProblem(problem); setProblem(problem);
if (m_Problem instanceof AbstractProblemDouble) { if (m_Problem instanceof AbstractProblemDouble) {
setPopulationSize(((AbstractProblemDouble)problem).getProblemDimension()+1); setPopulationSize(((AbstractProblemDouble)problem).getProblemDimension()+1);
return true; return true;

14
src/eva2/server/go/strategies/NichePSO.java
View File

@ -256,7 +256,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac
protected void initMainSwarm(){ protected void initMainSwarm(){
// pass NichePSO parameter on to the mainswarmoptimzer // pass NichePSO parameter on to the mainswarmoptimzer
setMainSwarmSize(mainSwarmSize); // (particles are initialized later via init) setMainSwarmSize(mainSwarmSize); // (particles are initialized later via init)
getMainSwarm().SetProblem(m_Problem); getMainSwarm().setProblem(m_Problem);
getMainSwarm().SetMaxAllowedSwarmRadius(maxAllowedSwarmRadius); getMainSwarm().SetMaxAllowedSwarmRadius(maxAllowedSwarmRadius);
getMainSwarm().getPopulation().setGenerationTo(0); getMainSwarm().getPopulation().setGenerationTo(0);
@ -285,7 +285,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac
*/ */
protected void initSubswarmOptimizerTemplate(){ protected void initSubswarmOptimizerTemplate(){
// pass on the parameters set via NichePSO (done in the analogous nichePSO-Setters as well -> no init() necessary) // pass on the parameters set via NichePSO (done in the analogous nichePSO-Setters as well -> no init() necessary)
getSubswarmOptimizerTemplate().SetProblem(m_Problem); getSubswarmOptimizerTemplate().setProblem(m_Problem);
getSubswarmOptimizerTemplate().SetMaxAllowedSwarmRadius(maxAllowedSwarmRadius); getSubswarmOptimizerTemplate().SetMaxAllowedSwarmRadius(maxAllowedSwarmRadius);
// choose PSO-type for the subswarmoptimizer // choose PSO-type for the subswarmoptimizer
@ -311,7 +311,7 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac
public ParticleSubSwarmOptimization getNewSubSwarmOptimizer(){ public ParticleSubSwarmOptimization getNewSubSwarmOptimizer(){
//initSubswarmOptimizerTemplate(); //initSubswarmOptimizerTemplate();
ParticleSubSwarmOptimization template = (ParticleSubSwarmOptimization)getSubswarmOptimizerTemplate().clone(); // this implicitely clones the problem but does not initialize it again... ParticleSubSwarmOptimization template = (ParticleSubSwarmOptimization)getSubswarmOptimizerTemplate().clone(); // this implicitely clones the problem but does not initialize it again...
template.SetProblem(this.m_Problem); //... let all subswarms use the same correct initialised problem instance template.setProblem(this.m_Problem); //... let all subswarms use the same correct initialised problem instance
return template; return template;
} }
@ -1219,15 +1219,15 @@ public class NichePSO implements InterfaceAdditionalPopulationInformer, Interfac
* This method will set the problem that is to be optimized * This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
// set member // set member
this.m_Problem = problem; this.m_Problem = problem;
// pass on to the main- and subswarm optimizers // pass on to the main- and subswarm optimizers
getMainSwarm().SetProblem(problem); getMainSwarm().setProblem(problem);
for (int i = 0; i < getSubSwarms().size(); ++i){ for (int i = 0; i < getSubSwarms().size(); ++i){
getSubSwarms().get(i).SetProblem(problem); getSubSwarms().get(i).setProblem(problem);
} }
getSubswarmOptimizerTemplate().SetProblem(problem); getSubswarmOptimizerTemplate().setProblem(problem);
} }
/** @tested nn /** @tested nn

2
src/eva2/server/go/strategies/ParticleFilterOptimization.java
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@ -257,7 +257,7 @@ public class ParticleFilterOptimization implements InterfaceOptimizer, java.io.S
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
if (problem instanceof AbstractOptimizationProblem) { if (problem instanceof AbstractOptimizationProblem) {
((AbstractOptimizationProblem)problem).informAboutOptimizer(this); ((AbstractOptimizationProblem)problem).informAboutOptimizer(this);

4
src/eva2/server/go/strategies/ParticleSubSwarmOptimization.java
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@ -456,7 +456,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
/////////// ///////////
ParticleSubSwarmOptimization tmpopt = new ParticleSubSwarmOptimization(); ParticleSubSwarmOptimization tmpopt = new ParticleSubSwarmOptimization();
tmpopt.SetProblem(this.m_Problem); tmpopt.setProblem(this.m_Problem);
tmpopt.evaluatePopulation(tmp); tmpopt.evaluatePopulation(tmp);
tmpopt.initByPopulation(tmp, false); // + size FCs tmpopt.initByPopulation(tmp, false); // + size FCs
@ -558,7 +558,7 @@ public class ParticleSubSwarmOptimization extends ParticleSwarmOptimizationGCPSO
* This method will set the problem that is to be optimized * This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
updateMaxPosDist(); updateMaxPosDist();
} }

2
src/eva2/server/go/strategies/ParticleSwarmOptimization.java
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@ -1684,7 +1684,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
* *
* @param problem * @param problem
*/ */
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }

2
src/eva2/server/go/strategies/PopulationBasedIncrementalLearning.java
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@ -138,7 +138,7 @@ public class PopulationBasedIncrementalLearning implements InterfaceOptimizer, j
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
if (m_Problem instanceof AbstractOptimizationProblem) { if (m_Problem instanceof AbstractOptimizationProblem) {
if (!(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate() instanceof InterfaceGAIndividual)) { if (!(((AbstractOptimizationProblem)m_Problem).getIndividualTemplate() instanceof InterfaceGAIndividual)) {

4
src/eva2/server/go/strategies/ScatterSearch.java
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@ -104,7 +104,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
GenericObjectEditor.setHideProperty(this.getClass(), "population", true); GenericObjectEditor.setHideProperty(this.getClass(), "population", true);
} }
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
this.problem = (AbstractOptimizationProblem)problem; this.problem = (AbstractOptimizationProblem)problem;
} }
@ -818,7 +818,7 @@ public class ScatterSearch implements InterfaceOptimizer, java.io.Serializable,
AbstractOptimizationProblem problem, InterfaceTerminator term) { AbstractOptimizationProblem problem, InterfaceTerminator term) {
ScatterSearch ss = new ScatterSearch(); ScatterSearch ss = new ScatterSearch();
problem.initProblem(); problem.initProblem();
ss.SetProblem(problem); ss.setProblem(problem);
ss.setRefSetSize(refSetSize); ss.setRefSetSize(refSetSize);
ss.setNelderMeadInitPerturbation(nmInitPerturb); ss.setNelderMeadInitPerturbation(nmInitPerturb);
ss.setLocalSearchRelativeFilter(relativeFitCrit); ss.setLocalSearchRelativeFilter(relativeFitCrit);

2
src/eva2/server/go/strategies/SimulatedAnnealing.java
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@ -126,7 +126,7 @@ public class SimulatedAnnealing implements InterfaceOptimizer, java.io.Serializa
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

2
src/eva2/server/go/strategies/SteadyStateGA.java
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@ -130,7 +130,7 @@ public class SteadyStateGA implements InterfaceOptimizer, java.io.Serializable {
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

2
src/eva2/server/go/strategies/ThresholdAlgorithm.java
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@ -118,7 +118,7 @@ public class ThresholdAlgorithm implements InterfaceOptimizer, java.io.Serializa
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

4
src/eva2/server/go/strategies/Tribes.java
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@ -184,7 +184,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
} }
public Tribes(Tribes other) { public Tribes(Tribes other) {
this.SetProblem(other.getProblem()); this.setProblem(other.getProblem());
iter = other.iter; iter = other.iter;
setObjectiveFirstDim(other.getObjectiveFirstDim()); setObjectiveFirstDim(other.getObjectiveFirstDim());
setDimension(other.range.length); setDimension(other.range.length);
@ -202,7 +202,7 @@ public class Tribes implements InterfaceOptimizer, java.io.Serializable {
hideHideable(); hideHideable();
} }
public void SetProblem(InterfaceOptimizationProblem problem) { public void setProblem(InterfaceOptimizationProblem problem) {
// System.out.println("TRIBES.SetProblem()"); // System.out.println("TRIBES.SetProblem()");
m_problem = (AbstractOptimizationProblem)problem; m_problem = (AbstractOptimizationProblem)problem;
range = null; range = null;

14
src/eva2/server/go/strategies/WingedMultiObjectiveEA.java
View File

@ -66,7 +66,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
int dim = this.m_OutputDimension; int dim = this.m_OutputDimension;
double[] weights; double[] weights;
// dim = tmpProb.getOutputDimension(); // dim = tmpProb.getOutputDimension();
this.m_MOOptimizer.SetProblem((InterfaceOptimizationProblem)this.m_Problem.clone()); this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem)this.m_Problem.clone());
this.m_MOOptimizer.init(); this.m_MOOptimizer.init();
this.m_SOOptimizers = new InterfaceOptimizer[dim]; this.m_SOOptimizers = new InterfaceOptimizer[dim];
for (int i = 0; i < dim; i++) { for (int i = 0; i < dim; i++) {
@ -79,11 +79,11 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
tmpWF.setWeights(tmpDA); tmpWF.setWeights(tmpDA);
tmpP.setMOSOConverter(tmpWF); tmpP.setMOSOConverter(tmpWF);
this.m_SOOptimizers[i] = (InterfaceOptimizer)this.m_SOOptimizer.clone(); this.m_SOOptimizers[i] = (InterfaceOptimizer)this.m_SOOptimizer.clone();
this.m_SOOptimizers[i].SetProblem(tmpP); this.m_SOOptimizers[i].setProblem(tmpP);
this.m_SOOptimizers[i].init(); this.m_SOOptimizers[i].init();
} }
} else { } else {
this.m_SOOptimizer.SetProblem(this.m_Problem); this.m_SOOptimizer.setProblem(this.m_Problem);
this.m_SOOptimizer.init(); this.m_SOOptimizer.init();
} }
this.communicate(); this.communicate();
@ -104,7 +104,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
int dim = 2; int dim = 2;
double[] weights; double[] weights;
// dim = tmpProb.getOutputDimension(); // dim = tmpProb.getOutputDimension();
this.m_MOOptimizer.SetProblem((InterfaceOptimizationProblem)this.m_Problem.clone()); this.m_MOOptimizer.setProblem((InterfaceOptimizationProblem)this.m_Problem.clone());
this.m_MOOptimizer.initByPopulation(pop, reset); this.m_MOOptimizer.initByPopulation(pop, reset);
this.m_SOOptimizers = new InterfaceOptimizer[dim]; this.m_SOOptimizers = new InterfaceOptimizer[dim];
for (int i = 0; i < dim; i++) { for (int i = 0; i < dim; i++) {
@ -117,11 +117,11 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
tmpWF.setWeights(tmpDA); tmpWF.setWeights(tmpDA);
tmpP.setMOSOConverter(tmpWF); tmpP.setMOSOConverter(tmpWF);
this.m_SOOptimizers[i] = (InterfaceOptimizer)this.m_SOOptimizer.clone(); this.m_SOOptimizers[i] = (InterfaceOptimizer)this.m_SOOptimizer.clone();
this.m_SOOptimizers[i].SetProblem(tmpP); this.m_SOOptimizers[i].setProblem(tmpP);
this.m_SOOptimizers[i].initByPopulation(pop, reset); this.m_SOOptimizers[i].initByPopulation(pop, reset);
} }
} else { } else {
this.m_SOOptimizer.SetProblem(this.m_Problem); this.m_SOOptimizer.setProblem(this.m_Problem);
this.m_SOOptimizer.initByPopulation(pop, reset); this.m_SOOptimizer.initByPopulation(pop, reset);
} }
this.communicate(); this.communicate();
@ -224,7 +224,7 @@ public class WingedMultiObjectiveEA implements InterfaceOptimizer, java.io.Seria
/** This method will set the problem that is to be optimized /** This method will set the problem that is to be optimized
* @param problem * @param problem
*/ */
public void SetProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
} }
public InterfaceOptimizationProblem getProblem () { public InterfaceOptimizationProblem getProblem () {

10
src/eva2/server/modules/AbstractGOParameters.java
View File

@ -42,7 +42,7 @@ public abstract class AbstractGOParameters implements InterfaceGOParameters, Ser
this.m_Optimizer = goParameters.m_Optimizer; this.m_Optimizer = goParameters.m_Optimizer;
this.m_Problem = goParameters.m_Problem; this.m_Problem = goParameters.m_Problem;
this.m_Terminator = goParameters.m_Terminator; this.m_Terminator = goParameters.m_Terminator;
this.m_Optimizer.SetProblem(this.m_Problem); this.m_Optimizer.setProblem(this.m_Problem);
this.randomSeed = goParameters.randomSeed; this.randomSeed = goParameters.randomSeed;
this.m_PostProc = goParameters.m_PostProc; this.m_PostProc = goParameters.m_PostProc;
} }
@ -53,7 +53,7 @@ public abstract class AbstractGOParameters implements InterfaceGOParameters, Ser
m_Problem = prob; m_Problem = prob;
m_Terminator = term; m_Terminator = term;
m_PostProc = new PostProcessParams(false); m_PostProc = new PostProcessParams(false);
opt.SetProblem(prob); opt.setProblem(prob);
} }
/** /**
@ -65,7 +65,7 @@ public abstract class AbstractGOParameters implements InterfaceGOParameters, Ser
setOptimizer(src.m_Optimizer); setOptimizer(src.m_Optimizer);
setProblem(src.m_Problem); setProblem(src.m_Problem);
setTerminator(src.m_Terminator); setTerminator(src.m_Terminator);
this.m_Optimizer.SetProblem(this.m_Problem); this.m_Optimizer.setProblem(this.m_Problem);
setSeed(src.randomSeed); setSeed(src.randomSeed);
setPostProcessParams(src.m_PostProc); setPostProcessParams(src.m_PostProc);
} }
@ -139,7 +139,7 @@ public abstract class AbstractGOParameters implements InterfaceGOParameters, Ser
public void setOptimizer(InterfaceOptimizer optimizer) { public void setOptimizer(InterfaceOptimizer optimizer) {
this.m_Optimizer = optimizer; this.m_Optimizer = optimizer;
this.m_Optimizer.SetProblem(this.m_Problem); this.m_Optimizer.setProblem(this.m_Problem);
if (this.m_Listener != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener); if (this.m_Listener != null) this.m_Optimizer.addPopulationChangedEventListener(this.m_Listener);
fireNotifyOnInformers(); fireNotifyOnInformers();
} }
@ -168,7 +168,7 @@ public abstract class AbstractGOParameters implements InterfaceGOParameters, Ser
*/ */
public void setProblem (InterfaceOptimizationProblem problem) { public void setProblem (InterfaceOptimizationProblem problem) {
this.m_Problem = problem; this.m_Problem = problem;
this.m_Optimizer.SetProblem(this.m_Problem); this.m_Optimizer.setProblem(this.m_Problem);
fireNotifyOnInformers(); fireNotifyOnInformers();
} }

2
src/eva2/server/modules/Processor.java
View File

@ -230,7 +230,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
m_Statistics.startOptPerformed(getInfoString(), runCounter, goParams, getInformerList()); m_Statistics.startOptPerformed(getInfoString(), runCounter, goParams, getInformerList());
this.goParams.getProblem().initProblem(); this.goParams.getProblem().initProblem();
this.goParams.getOptimizer().SetProblem(this.goParams.getProblem()); this.goParams.getOptimizer().setProblem(this.goParams.getProblem());
this.goParams.getTerminator().init(this.goParams.getProblem()); this.goParams.getTerminator().init(this.goParams.getProblem());
maybeInitParamCtrl(goParams); maybeInitParamCtrl(goParams);
if (this.m_createInitialPopulations) { if (this.m_createInitialPopulations) {