public OffspringSelectionEvolutionStrategy()
: base() {
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", "µ (mu) - the size of the population.", new IntValue(20)));
Parameters.Add(new ValueParameter<IntValue>("ParentsPerChild", "ρ (rho) - how many parents should be recombined.", new IntValue(1)));
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter<BoolValue>("PlusSelection", "True for plus selection (elitist population), false for comma selection (non-elitist population).", new BoolValue(true)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
Parameters.Add(new OptionalConstrainedValueParameter<ICrossover>("Recombinator", "The operator used to cross solutions."));
Parameters.Add(new ConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterCreator>("StrategyParameterCreator", "The operator that creates the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterCrossover>("StrategyParameterCrossover", "The operator that recombines the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterManipulator>("StrategyParameterManipulator", "The operator that manipulates the strategy parameters."));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
Parameters.Add(new ValueLookupParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(40)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
Parameters.Add(new ValueParameter<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions.", new IntValue(int.MaxValue)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactor", "The comparison factor is used to determine whether the offspring should be compared to the better parent, the worse parent or a quality value linearly interpolated between them. It is in the range [0;1].", new DoubleValue(0.5)));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
UniformSubScopesProcessor strategyVectorProcessor = new UniformSubScopesProcessor();
Placeholder strategyVectorCreator = new Placeholder();
ResultsCollector resultsCollector = new ResultsCollector();
OffspringSelectionEvolutionStrategyMainLoop mainLoop = new OffspringSelectionEvolutionStrategyMainLoop();
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 = strategyVectorProcessor;
strategyVectorProcessor.Operator = strategyVectorCreator;
strategyVectorProcessor.Successor = resultsCollector;
strategyVectorCreator.OperatorParameter.ActualName = "StrategyParameterCreator";
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.PlusSelectionParameter.ActualName = PlusSelectionParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.RecombinatorParameter.ActualName = RecombinatorParameter.Name;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
mainLoop.MaximumEvaluatedSolutionsParameter.ActualName = MaximumEvaluatedSolutionsParameter.Name;
mainLoop.SelectedParentsParameter.ActualName = SelectedParentsParameter.Name;
mainLoop.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
mainLoop.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio";
mainLoop.SelectionPressureParameter.ActualName = "SelectionPressure";
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
selectionPressureAnalyzer = new ValueAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
private OffspringSelectionEvolutionStrategy(OffspringSelectionEvolutionStrategy original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
Initialize();
}
private OffspringSelectionGeneticAlgorithm(OffspringSelectionGeneticAlgorithm original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
successfulOffspringAnalyzer = cloner.Clone(original.successfulOffspringAnalyzer);
Initialize();
}
public OffspringSelectionGeneticAlgorithm()
: base() {
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 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<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorLowerBound", "The lower bound of the comparison factor (start).", new DoubleValue(0)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorUpperBound", "The upper bound of the comparison factor (end).", new DoubleValue(1)));
Parameters.Add(new OptionalConstrainedValueParameter<IDiscreteDoubleValueModifier>("ComparisonFactorModifier", "The operator used to modify the comparison factor.", new ItemSet<IDiscreteDoubleValueModifier>(new IDiscreteDoubleValueModifier[] { new LinearDiscreteDoubleValueModifier() }), new LinearDiscreteDoubleValueModifier()));
Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
Parameters.Add(new ValueLookupParameter<BoolValue>("OffspringSelectionBeforeMutation", "True if the offspring selection step should be applied before mutation, false if it should be applied after mutation.", new BoolValue(false)));
Parameters.Add(new ValueLookupParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(200)));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions (approximately).", new IntValue(int.MaxValue)));
Parameters.Add(new FixedValueParameter<BoolValue>("FillPopulationWithParents", "True if the population should be filled with parent individual or false if worse children should be used when the maximum selection pressure is exceeded.", new BoolValue(false)) { Hidden = true });
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
OffspringSelectionGeneticAlgorithmMainLoop mainLoop = new OffspringSelectionGeneticAlgorithmMainLoop();
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 = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", "", "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ComparisonFactorModifierParameter.ActualName = ComparisonFactorModifierParameter.Name;
mainLoop.ComparisonFactorParameter.ActualName = "ComparisonFactor";
mainLoop.ComparisonFactorStartParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
mainLoop.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
SelectorParameter.ValidValues.Add(selector);
ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
ParameterizeSelectors();
foreach (IDiscreteDoubleValueModifier modifier in ApplicationManager.Manager.GetInstances<IDiscreteDoubleValueModifier>().OrderBy(x => x.Name))
ComparisonFactorModifierParameter.ValidValues.Add(modifier);
IDiscreteDoubleValueModifier linearModifier = ComparisonFactorModifierParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("LinearDiscreteDoubleValueModifier"));
if (linearModifier != null) ComparisonFactorModifierParameter.Value = linearModifier;
ParameterizeComparisonFactorModifiers();
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
selectionPressureAnalyzer = new ValueAnalyzer();
successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
private ValueAnalyzer(ValueAnalyzer original, Cloner cloner)
: base(original, cloner)
{
Initialize();
}
public AlpsOffspringSelectionGeneticAlgorithm()
: base() {
#region Add parameters
Parameters.Add(new FixedValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new FixedValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new FixedValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze all individuals from all layers combined.", new MultiAnalyzer()));
Parameters.Add(new FixedValueParameter<MultiAnalyzer>("LayerAnalyzer", "The operator used to analyze each layer.", new MultiAnalyzer()));
Parameters.Add(new FixedValueParameter<IntValue>("NumberOfLayers", "The number of layers.", new IntValue(10)));
Parameters.Add(new FixedValueParameter<IntValue>("PopulationSize", "The size of the population of solutions in each layer.", new IntValue(100)));
Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new FixedValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
Parameters.Add(new FixedValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
Parameters.Add(new FixedValueParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
Parameters.Add(new FixedValueParameter<DoubleValue>("ComparisonFactor", "The comparison factor is used to determine whether the offspring should be compared to the better parent, the worse parent or a quality value linearly interpolated between them. It is in the range [0;1].", new DoubleValue(1)));
Parameters.Add(new FixedValueParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
Parameters.Add(new FixedValueParameter<BoolValue>("OffspringSelectionBeforeMutation", "True if the offspring selection step should be applied before mutation, false if it should be applied after mutation.", new BoolValue(false)));
Parameters.Add(new FixedValueParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(200)));
Parameters.Add(new FixedValueParameter<BoolValue>("FillPopulationWithParents", "True if the population should be filled with parent individual or false if worse children should be used when the maximum selection pressure is exceeded.", new BoolValue(false)) { Hidden = true });
Parameters.Add(new FixedValueParameter<EnumValue<AgingScheme>>("AgingScheme", "The aging scheme for setting the age-limits for the layers.", new EnumValue<AgingScheme>(ALPS.AgingScheme.Polynomial)));
Parameters.Add(new FixedValueParameter<IntValue>("AgeGap", "The frequency of reseeding the lowest layer and scaling factor for the age-limits for the layers.", new IntValue(20)));
Parameters.Add(new FixedValueParameter<DoubleValue>("AgeInheritance", "A weight that determines the age of a child after crossover based on the older (1.0) and younger (0.0) parent.", new DoubleValue(1.0)) { Hidden = true });
Parameters.Add(new FixedValueParameter<IntArray>("AgeLimits", "The maximum age an individual is allowed to reach in a certain layer.", new IntArray(new int[0])) { Hidden = true });
Parameters.Add(new FixedValueParameter<IntValue>("MatingPoolRange", "The range of layers used for creating a mating pool. (1 = current + previous layer)", new IntValue(1)) { Hidden = true });
Parameters.Add(new FixedValueParameter<BoolValue>("ReduceToPopulationSize", "Reduce the CurrentPopulationSize after elder migration to PopulationSize", new BoolValue(true)) { Hidden = true });
Parameters.Add(new FixedValueParameter<MultiTerminator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop.", new MultiTerminator()));
#endregion
#region Create operators
var globalRandomCreator = new RandomCreator();
var layer0Creator = new SubScopesCreator() { Name = "Create Layer Zero" };
var layer0Processor = new SubScopesProcessor();
var localRandomCreator = new LocalRandomCreator();
var layerSolutionsCreator = new SolutionsCreator();
var initializeAgeProcessor = new UniformSubScopesProcessor();
var initializeAge = new VariableCreator() { Name = "Initialize Age" };
var initializeCurrentPopulationSize = new SubScopesCounter() { Name = "Initialize CurrentPopulationCounter" };
var initializeLocalEvaluatedSolutions = new Assigner() { Name = "Initialize LayerEvaluatedSolutions" };
var initializeGlobalEvaluatedSolutions = new DataReducer() { Name = "Initialize EvaluatedSolutions" };
var resultsCollector = new ResultsCollector();
var mainLoop = new AlpsOffspringSelectionGeneticAlgorithmMainLoop();
#endregion
#region Create and parameterize operator graph
OperatorGraph.InitialOperator = globalRandomCreator;
globalRandomCreator.RandomParameter.ActualName = "GlobalRandom";
globalRandomCreator.SeedParameter.Value = null;
globalRandomCreator.SeedParameter.ActualName = SeedParameter.Name;
globalRandomCreator.SetSeedRandomlyParameter.Value = null;
globalRandomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
globalRandomCreator.Successor = layer0Creator;
layer0Creator.NumberOfSubScopesParameter.Value = new IntValue(1);
layer0Creator.Successor = layer0Processor;
layer0Processor.Operators.Add(localRandomCreator);
layer0Processor.Successor = initializeGlobalEvaluatedSolutions;
localRandomCreator.Successor = layerSolutionsCreator;
layerSolutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
layerSolutionsCreator.Successor = initializeAgeProcessor;
initializeAgeProcessor.Operator = initializeAge;
initializeAgeProcessor.Successor = initializeCurrentPopulationSize;
initializeCurrentPopulationSize.ValueParameter.ActualName = "CurrentPopulationSize";
initializeCurrentPopulationSize.Successor = initializeLocalEvaluatedSolutions;
initializeAge.CollectedValues.Add(new ValueParameter<DoubleValue>("Age", new DoubleValue(0)));
initializeAge.Successor = null;
initializeLocalEvaluatedSolutions.LeftSideParameter.ActualName = "LayerEvaluatedSolutions";
initializeLocalEvaluatedSolutions.RightSideParameter.ActualName = "CurrentPopulationSize";
initializeLocalEvaluatedSolutions.Successor = null;
initializeGlobalEvaluatedSolutions.ReductionOperation.Value.Value = ReductionOperations.Sum;
initializeGlobalEvaluatedSolutions.TargetOperation.Value.Value = ReductionOperations.Assign;
initializeGlobalEvaluatedSolutions.ParameterToReduce.ActualName = "LayerEvaluatedSolutions";
initializeGlobalEvaluatedSolutions.TargetParameter.ActualName = "EvaluatedSolutions";
initializeGlobalEvaluatedSolutions.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.Successor = mainLoop;
mainLoop.GlobalRandomParameter.ActualName = "GlobalRandom";
mainLoop.LocalRandomParameter.ActualName = localRandomCreator.LocalRandomParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.LayerAnalyzerParameter.ActualName = LayerAnalyzerParameter.Name;
mainLoop.NumberOfLayersParameter.ActualName = NumberOfLayersParameter.Name;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.CurrentPopulationSizeParameter.ActualName = "CurrentPopulationSize";
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
mainLoop.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
mainLoop.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
mainLoop.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
mainLoop.AgeParameter.ActualName = "Age";
mainLoop.AgeGapParameter.ActualName = AgeGapParameter.Name;
mainLoop.AgeInheritanceParameter.ActualName = AgeInheritanceParameter.Name;
mainLoop.AgeLimitsParameter.ActualName = AgeLimitsParameter.Name;
mainLoop.MatingPoolRangeParameter.ActualName = MatingPoolRangeParameter.Name;
mainLoop.ReduceToPopulationSizeParameter.ActualName = ReduceToPopulationSizeParameter.Name;
mainLoop.TerminatorParameter.ActualName = TerminatorParameter.Name;
#endregion
#region Set operators
foreach (var selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(s => !(s is IMultiObjectiveSelector)).OrderBy(s => Name))
SelectorParameter.ValidValues.Add(selector);
var defaultSelector = SelectorParameter.ValidValues.OfType<GeneralizedRankSelector>().FirstOrDefault();
if (defaultSelector != null) {
defaultSelector.PressureParameter.Value = new DoubleValue(4.0);
SelectorParameter.Value = defaultSelector;
}
#endregion
#region Create analyzers
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
layerQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ageAnalyzer = new OldestAverageYoungestAgeAnalyzer();
layerAgeAnalyzer = new OldestAverageYoungestAgeAnalyzer();
ageDistributionAnalyzer = new AgeDistributionAnalyzer();
layerAgeDistributionAnalyzer = new AgeDistributionAnalyzer();
selectionPressureAnalyzer = new ValueAnalyzer();
layerSelectionPressureAnalyzer = new ValueAnalyzer();
currentSuccessRatioAnalyzer = new ValueAnalyzer();
#endregion
#region Create terminators
generationsTerminator = new ComparisonTerminator<IntValue>("Generations", ComparisonType.Less, new IntValue(1000)) { Name = "Generations" };
evaluationsTerminator = new ComparisonTerminator<IntValue>("EvaluatedSolutions", ComparisonType.Less, new IntValue(int.MaxValue)) { Name = "Evaluations" };
qualityTerminator = new SingleObjectiveQualityTerminator() { Name = "Quality" };
executionTimeTerminator = new ExecutionTimeTerminator(this, new TimeSpanValue(TimeSpan.FromMinutes(5)));
#endregion
#region Parameterize
UpdateAnalyzers();
ParameterizeAnalyzers();
ParameterizeSelectors();
UpdateTerminators();
ParameterizeAgeLimits();
#endregion
Initialize();
}
private AlpsOffspringSelectionGeneticAlgorithm(AlpsOffspringSelectionGeneticAlgorithm original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
layerQualityAnalyzer = cloner.Clone(original.layerQualityAnalyzer);
ageAnalyzer = cloner.Clone(original.ageAnalyzer);
layerAgeAnalyzer = cloner.Clone(original.layerAgeAnalyzer);
ageDistributionAnalyzer = cloner.Clone(original.ageDistributionAnalyzer);
layerAgeDistributionAnalyzer = cloner.Clone(original.layerAgeDistributionAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
layerSelectionPressureAnalyzer = cloner.Clone(original.layerSelectionPressureAnalyzer);
currentSuccessRatioAnalyzer = cloner.Clone(original.currentSuccessRatioAnalyzer);
generationsTerminator = cloner.Clone(original.generationsTerminator);
evaluationsTerminator = cloner.Clone(original.evaluationsTerminator);
qualityTerminator = cloner.Clone(original.qualityTerminator);
executionTimeTerminator = cloner.Clone(original.executionTimeTerminator);
Initialize();
}
private SASEGASA(SASEGASA original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
villageQualityAnalyzer = cloner.Clone(original.villageQualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
villageSelectionPressureAnalyzer = cloner.Clone(original.villageSelectionPressureAnalyzer);
successfulOffspringAnalyzer = cloner.Clone(original.successfulOffspringAnalyzer);
Initialize();
}
private ValueAnalyzer(ValueAnalyzer original, Cloner cloner)
: base(original, cloner) {
Initialize();
}
