protected NSGA2MainLoop(NSGA2MainLoop original, Cloner cloner) : base(original, cloner) { }
public NSGA2() {
Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ValueParameter<PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on two parents.", new PercentValue(0.9)));
Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter<IntValue>("SelectedParents", "Each two parents form a new child, typically this value should be twice the population size, but because the NSGA-II is maximally elitist it can be any multiple of 2 greater than 0.", new IntValue(200)));
Parameters.Add(new FixedValueParameter<BoolValue>("DominateOnEqualQualities", "Flag which determines wether solutions with equal quality values should be treated as dominated.", new BoolValue(false)));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
RankAndCrowdingSorter rankAndCrowdingSorter = new RankAndCrowdingSorter();
ResultsCollector resultsCollector = new ResultsCollector();
NSGA2MainLoop mainLoop = new NSGA2MainLoop();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.RandomParameter.ActualName = "Random";
randomCreator.SeedParameter.ActualName = SeedParameter.Name;
randomCreator.SeedParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.Successor = solutionsCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
solutionsCreator.Successor = subScopesCounter;
subScopesCounter.Name = "Initialize EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = rankAndCrowdingSorter;
rankAndCrowdingSorter.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name;
rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
rankAndCrowdingSorter.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.CrossoverProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is ISingleObjectiveSelector)).OrderBy(x => x.Name))
SelectorParameter.ValidValues.Add(selector);
ISelector tournamentSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("CrowdedTournamentSelector"));
if (tournamentSelector != null) SelectorParameter.Value = tournamentSelector;
ParameterizeSelectors();
paretoFrontAnalyzer = new RankBasedParetoFrontAnalyzer();
paretoFrontAnalyzer.RankParameter.ActualName = "Rank";
paretoFrontAnalyzer.RankParameter.Depth = 1;
paretoFrontAnalyzer.ResultsParameter.ActualName = "Results";
ParameterizeAnalyzers();
UpdateAnalyzers();
AfterDeserialization();
}
public NSGA2()
{
Parameters.Add(new ValueParameter <IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter <BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter <IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
Parameters.Add(new ConstrainedValueParameter <ISelector>("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ValueParameter <PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on two parents.", new PercentValue(0.9)));
Parameters.Add(new ConstrainedValueParameter <ICrossover>("Crossover", "The operator used to cross solutions."));
Parameters.Add(new ValueParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
Parameters.Add(new ConstrainedValueParameter <IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueParameter <MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter <IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter <IntValue>("SelectedParents", "Each two parents form a new child, typically this value should be twice the population size, but because the NSGA-II is maximally elitist it can be any multiple of 2 greater than 0.", new IntValue(200)));
Parameters.Add(new FixedValueParameter <BoolValue>("DominateOnEqualQualities", "Flag which determines wether solutions with equal quality values should be treated as dominated.", new BoolValue(false)));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
RankAndCrowdingSorter rankAndCrowdingSorter = new RankAndCrowdingSorter();
ResultsCollector resultsCollector = new ResultsCollector();
NSGA2MainLoop mainLoop = new NSGA2MainLoop();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.RandomParameter.ActualName = "Random";
randomCreator.SeedParameter.ActualName = SeedParameter.Name;
randomCreator.SeedParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.Successor = solutionsCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
solutionsCreator.Successor = subScopesCounter;
subScopesCounter.Name = "Initialize EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = rankAndCrowdingSorter;
rankAndCrowdingSorter.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name;
rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
rankAndCrowdingSorter.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.CrossoverProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
foreach (ISelector selector in ApplicationManager.Manager.GetInstances <ISelector>().Where(x => !(x is ISingleObjectiveSelector)).OrderBy(x => x.Name))
{
SelectorParameter.ValidValues.Add(selector);
}
ISelector tournamentSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("CrowdedTournamentSelector"));
if (tournamentSelector != null)
{
SelectorParameter.Value = tournamentSelector;
}
ParameterizeSelectors();
paretoFrontAnalyzer = new RankBasedParetoFrontAnalyzer();
paretoFrontAnalyzer.RankParameter.ActualName = "Rank";
paretoFrontAnalyzer.RankParameter.Depth = 1;
paretoFrontAnalyzer.ResultsParameter.ActualName = "Results";
ParameterizeAnalyzers();
UpdateAnalyzers();
RegisterEventhandlers();
}
protected NSGA2MainLoop(NSGA2MainLoop original, Cloner cloner) : base(original, cloner)
{
}
