private void Initialize() { #region Create parameters Parameters.Add(new ValueLookupParameter <IRandom>("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter <BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter <IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter <IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization.")); Parameters.Add(new ValueLookupParameter <IntValue>("Elites", "The numer of elite solutions which are kept in each generation.")); Parameters.Add(new ValueLookupParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); Parameters.Add(new ValueLookupParameter <IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.")); Parameters.Add(new ValueLookupParameter <VariableCollection>("Results", "The variable collection where results should be stored.")); Parameters.Add(new ValueLookupParameter <IOperator>("Analyzer", "The operator used to analyze each generation.")); Parameters.Add(new ValueLookupParameter <IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated.")); Parameters.Add(new ValueLookupParameter <IntValue>("PopulationSize", "The size of the population.")); Parameters.Add(new ValueLookupParameter <IntValue>("MinimumPopulationSize", "The minimum size of the population of solutions.")); Parameters.Add(new ValueLookupParameter <IntValue>("MaximumPopulationSize", "The maximum size of the population of solutions.")); Parameters.Add(new ValueLookupParameter <DoubleValue>("ComparisonFactor", "The comparison factor.")); Parameters.Add(new ValueLookupParameter <IntValue>("Effort", "The maximum number of offspring created in each generation.")); Parameters.Add(new ValueLookupParameter <IntValue>("BatchSize", "The number of children that should be created during one iteration of the offspring creation process.")); Parameters.Add(new ValueLookupParameter <ISolutionSimilarityCalculator>("SimilarityCalculator", "The operator used to calculate the similarity between two solutions.")); Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the genetic algorithm should be applied.")); #endregion #region Create operators VariableCreator variableCreator = new VariableCreator(); Assigner assigner1 = new Assigner(); ResultsCollector resultsCollector = new ResultsCollector(); Placeholder analyzer1 = new Placeholder(); Placeholder selector = new Placeholder(); SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor(); ChildrenCreator childrenCreator = new ChildrenCreator(); UniformSubScopesProcessor uniformSubScopesProcessor = new UniformSubScopesProcessor(); Placeholder crossover = new Placeholder(); StochasticBranch stochasticBranch = new StochasticBranch(); Placeholder mutator = new Placeholder(); Placeholder evaluator = new Placeholder(); WeightedParentsQualityComparator weightedParentsQualityComparator = new WeightedParentsQualityComparator(); SubScopesRemover subScopesRemover = new SubScopesRemover(); IntCounter intCounter1 = new IntCounter(); IntCounter intCounter2 = new IntCounter(); ConditionalSelector conditionalSelector = new ConditionalSelector(); RightReducer rightReducer1 = new RightReducer(); DuplicatesSelector duplicateSelector = new DuplicatesSelector(); LeftReducer leftReducer1 = new LeftReducer(); ProgressiveOffspringPreserver progressiveOffspringSelector = new ProgressiveOffspringPreserver(); SubScopesCounter subScopesCounter2 = new SubScopesCounter(); ExpressionCalculator calculator1 = new ExpressionCalculator(); ConditionalBranch conditionalBranch1 = new ConditionalBranch(); Comparator comparator1 = new Comparator(); ConditionalBranch conditionalBranch2 = new ConditionalBranch(); LeftReducer leftReducer2 = new LeftReducer(); SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor(); BestSelector bestSelector = new BestSelector(); RightReducer rightReducer2 = new RightReducer(); ScopeCleaner scopeCleaner = new ScopeCleaner(); ScopeRestorer scopeRestorer = new ScopeRestorer(); MergingReducer mergingReducer = new MergingReducer(); IntCounter intCounter3 = new IntCounter(); SubScopesCounter subScopesCounter3 = new SubScopesCounter(); ExpressionCalculator calculator2 = new ExpressionCalculator(); Comparator comparator2 = new Comparator(); ConditionalBranch conditionalBranch3 = new ConditionalBranch(); Placeholder analyzer2 = new Placeholder(); Comparator comparator3 = new Comparator(); ConditionalBranch conditionalBranch4 = new ConditionalBranch(); Comparator comparator4 = new Comparator(); ConditionalBranch conditionalBranch5 = new ConditionalBranch(); Assigner assigner3 = new Assigner(); Assigner assigner4 = new Assigner(); Assigner assigner5 = new Assigner(); ConditionalBranch reevaluateElitesBranch = new ConditionalBranch(); UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor(); Placeholder evaluator2 = new Placeholder(); SubScopesCounter subScopesCounter4 = new SubScopesCounter(); variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Generations", new IntValue(0))); // Class RAPGA expects this to be called Generations variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("CurrentPopulationSize", new IntValue(0))); variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("NumberOfCreatedOffspring", new IntValue(0))); variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("NumberOfSuccessfulOffspring", new IntValue(0))); variableCreator.CollectedValues.Add(new ValueParameter <ScopeList>("OffspringList", new ScopeList())); assigner1.Name = "Initialize CurrentPopulationSize"; assigner1.LeftSideParameter.ActualName = "CurrentPopulationSize"; assigner1.RightSideParameter.ActualName = PopulationSizeParameter.Name; resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("Generations")); resultsCollector.CollectedValues.Add(new LookupParameter <IntValue>("CurrentPopulationSize")); resultsCollector.ResultsParameter.ActualName = "Results"; analyzer1.Name = "Analyzer"; analyzer1.OperatorParameter.ActualName = "Analyzer"; selector.Name = "Selector"; selector.OperatorParameter.ActualName = "Selector"; childrenCreator.ParentsPerChild = new IntValue(2); uniformSubScopesProcessor.Parallel.Value = true; crossover.Name = "Crossover"; crossover.OperatorParameter.ActualName = "Crossover"; stochasticBranch.ProbabilityParameter.ActualName = "MutationProbability"; stochasticBranch.RandomParameter.ActualName = "Random"; mutator.Name = "Mutator"; mutator.OperatorParameter.ActualName = "Mutator"; evaluator.Name = "Evaluator"; evaluator.OperatorParameter.ActualName = "Evaluator"; weightedParentsQualityComparator.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name; weightedParentsQualityComparator.LeftSideParameter.ActualName = QualityParameter.Name; weightedParentsQualityComparator.MaximizationParameter.ActualName = MaximizationParameter.Name; weightedParentsQualityComparator.RightSideParameter.ActualName = QualityParameter.Name; weightedParentsQualityComparator.ResultParameter.ActualName = "SuccessfulOffspring"; subScopesRemover.RemoveAllSubScopes = true; intCounter1.Name = "Increment NumberOfCreatedOffspring"; intCounter1.ValueParameter.ActualName = "NumberOfCreatedOffspring"; intCounter1.Increment = null; intCounter1.IncrementParameter.ActualName = BatchSizeParameter.Name; intCounter2.Name = "Increment EvaluatedSolutions"; intCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; intCounter2.Increment = null; intCounter2.IncrementParameter.ActualName = BatchSizeParameter.Name; conditionalSelector.ConditionParameter.ActualName = "SuccessfulOffspring"; conditionalSelector.ConditionParameter.Depth = 1; conditionalSelector.CopySelected.Value = false; duplicateSelector.CopySelected.Value = false; progressiveOffspringSelector.OffspringListParameter.ActualName = "OffspringList"; progressiveOffspringSelector.ElitesParameter.ActualName = ElitesParameter.Name; progressiveOffspringSelector.MaximumPopulationSizeParameter.ActualName = MaximumPopulationSizeParameter.Name; subScopesCounter2.Name = "Count Successful Offspring"; subScopesCounter2.ValueParameter.ActualName = "NumberOfSuccessfulOffspring"; calculator1.Name = "NumberOfSuccessfulOffspring == MaximumPopulationSize - Elites"; calculator1.CollectedValues.Add(new ValueLookupParameter <IntValue>("NumberOfSuccessfulOffspring")); calculator1.CollectedValues.Add(new ValueLookupParameter <IntValue>("MaximumPopulationSize")); calculator1.CollectedValues.Add(new ValueLookupParameter <IntValue>("Elites")); calculator1.ExpressionParameter.Value = new StringValue("NumberOfSuccessfulOffspring MaximumPopulationSize Elites - =="); calculator1.ExpressionResultParameter.ActualName = "Break"; conditionalBranch1.Name = "Break?"; conditionalBranch1.ConditionParameter.ActualName = "Break"; comparator1.Name = "NumberOfCreatedOffspring >= Effort"; comparator1.Comparison = new Comparison(ComparisonType.GreaterOrEqual); comparator1.LeftSideParameter.ActualName = "NumberOfCreatedOffspring"; comparator1.RightSideParameter.ActualName = EffortParameter.Name; comparator1.ResultParameter.ActualName = "Break"; conditionalBranch2.Name = "Break?"; conditionalBranch2.ConditionParameter.ActualName = "Break"; bestSelector.CopySelected = new BoolValue(false); bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; bestSelector.NumberOfSelectedSubScopesParameter.ActualName = "Elites"; bestSelector.QualityParameter.ActualName = QualityParameter.Name; intCounter3.Name = "Increment Generations"; intCounter3.Increment = new IntValue(1); intCounter3.ValueParameter.ActualName = "Generations"; subScopesCounter3.Name = "Update CurrentPopulationSize"; subScopesCounter3.ValueParameter.ActualName = "CurrentPopulationSize"; subScopesCounter3.AccumulateParameter.Value = new BoolValue(false); calculator2.Name = "Evaluate ActualSelectionPressure"; calculator2.CollectedValues.Add(new ValueLookupParameter <IntValue>("NumberOfCreatedOffspring")); calculator2.CollectedValues.Add(new ValueLookupParameter <IntValue>("Elites")); calculator2.CollectedValues.Add(new ValueLookupParameter <IntValue>("CurrentPopulationSize")); calculator2.ExpressionParameter.Value = new StringValue("NumberOfCreatedOffspring Elites + CurrentPopulationSize /"); calculator2.ExpressionResultParameter.ActualName = "ActualSelectionPressure"; comparator2.Name = "CurrentPopulationSize < 1"; comparator2.Comparison = new Comparison(ComparisonType.Less); comparator2.LeftSideParameter.ActualName = "CurrentPopulationSize"; comparator2.RightSideParameter.Value = new IntValue(1); comparator2.ResultParameter.ActualName = "Terminate"; conditionalBranch3.Name = "Terminate?"; conditionalBranch3.ConditionParameter.ActualName = "Terminate"; analyzer2.Name = "Analyzer"; analyzer2.OperatorParameter.ActualName = "Analyzer"; comparator3.Name = "Generations >= MaximumGenerations"; comparator3.Comparison = new Comparison(ComparisonType.GreaterOrEqual); comparator3.LeftSideParameter.ActualName = "Generations"; comparator3.ResultParameter.ActualName = "Terminate"; comparator3.RightSideParameter.ActualName = MaximumGenerationsParameter.Name; conditionalBranch4.Name = "Terminate?"; conditionalBranch4.ConditionParameter.ActualName = "Terminate"; comparator4.Name = "CurrentPopulationSize < MinimumPopulationSize"; comparator4.Comparison = new Comparison(ComparisonType.Less); comparator4.LeftSideParameter.ActualName = "CurrentPopulationSize"; comparator4.RightSideParameter.ActualName = MinimumPopulationSizeParameter.Name; comparator4.ResultParameter.ActualName = "Terminate"; conditionalBranch5.Name = "Terminate?"; conditionalBranch5.ConditionParameter.ActualName = "Terminate"; assigner3.Name = "Reset NumberOfCreatedOffspring"; assigner3.LeftSideParameter.ActualName = "NumberOfCreatedOffspring"; assigner3.RightSideParameter.Value = new IntValue(0); assigner4.Name = "Reset NumberOfSuccessfulOffspring"; assigner4.LeftSideParameter.ActualName = "NumberOfSuccessfulOffspring"; assigner4.RightSideParameter.Value = new IntValue(0); assigner5.Name = "Reset OffspringList"; assigner5.LeftSideParameter.ActualName = "OffspringList"; assigner5.RightSideParameter.Value = new ScopeList(); reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites"; reevaluateElitesBranch.Name = "Reevaluate elites ?"; uniformSubScopesProcessor2.Parallel.Value = true; evaluator2.Name = "Evaluator (placeholder)"; evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter4.Name = "Increment EvaluatedSolutions"; subScopesCounter4.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; #endregion #region Create operator graph OperatorGraph.InitialOperator = variableCreator; variableCreator.Successor = assigner1; assigner1.Successor = resultsCollector; resultsCollector.Successor = analyzer1; analyzer1.Successor = selector; selector.Successor = subScopesProcessor1; subScopesProcessor1.Operators.Add(new EmptyOperator()); subScopesProcessor1.Operators.Add(childrenCreator); subScopesProcessor1.Successor = calculator1; childrenCreator.Successor = uniformSubScopesProcessor; uniformSubScopesProcessor.Operator = crossover; uniformSubScopesProcessor.Successor = intCounter1; crossover.Successor = stochasticBranch; stochasticBranch.FirstBranch = mutator; stochasticBranch.SecondBranch = null; mutator.Successor = null; stochasticBranch.Successor = evaluator; evaluator.Successor = weightedParentsQualityComparator; weightedParentsQualityComparator.Successor = subScopesRemover; intCounter1.Successor = intCounter2; intCounter2.Successor = conditionalSelector; conditionalSelector.Successor = rightReducer1; rightReducer1.Successor = duplicateSelector; duplicateSelector.Successor = leftReducer1; leftReducer1.Successor = progressiveOffspringSelector; progressiveOffspringSelector.Successor = subScopesCounter2; calculator1.Successor = conditionalBranch1; conditionalBranch1.FalseBranch = comparator1; conditionalBranch1.TrueBranch = subScopesProcessor2; comparator1.Successor = conditionalBranch2; conditionalBranch2.FalseBranch = leftReducer2; conditionalBranch2.TrueBranch = subScopesProcessor2; leftReducer2.Successor = selector; subScopesProcessor2.Operators.Add(bestSelector); subScopesProcessor2.Operators.Add(scopeCleaner); subScopesProcessor2.Successor = mergingReducer; bestSelector.Successor = rightReducer2; rightReducer2.Successor = reevaluateElitesBranch; reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor2; uniformSubScopesProcessor2.Operator = evaluator2; uniformSubScopesProcessor2.Successor = subScopesCounter4; evaluator2.Successor = null; subScopesCounter4.Successor = null; reevaluateElitesBranch.FalseBranch = null; reevaluateElitesBranch.Successor = null; scopeCleaner.Successor = scopeRestorer; mergingReducer.Successor = intCounter3; intCounter3.Successor = subScopesCounter3; subScopesCounter3.Successor = calculator2; calculator2.Successor = comparator2; comparator2.Successor = conditionalBranch3; conditionalBranch3.FalseBranch = analyzer2; conditionalBranch3.TrueBranch = null; analyzer2.Successor = comparator3; comparator3.Successor = conditionalBranch4; conditionalBranch4.FalseBranch = comparator4; conditionalBranch4.TrueBranch = null; conditionalBranch4.Successor = null; comparator4.Successor = conditionalBranch5; conditionalBranch5.FalseBranch = assigner3; conditionalBranch5.TrueBranch = null; conditionalBranch5.Successor = null; assigner3.Successor = assigner4; assigner4.Successor = assigner5; assigner5.Successor = selector; #endregion }
public IslandGeneticAlgorithmMainLoop() : base() { #region Create parameters Parameters.Add(new ValueLookupParameter <IRandom>("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter <BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter <DoubleValue>("BestKnownQuality", "The best known quality value found so far.")); Parameters.Add(new ValueLookupParameter <IntValue>("NumberOfIslands", "The number of islands.")); Parameters.Add(new ValueLookupParameter <IntValue>("MigrationInterval", "The number of generations that should pass between migration phases.")); Parameters.Add(new ValueLookupParameter <PercentValue>("MigrationRate", "The proportion of individuals that should migrate between the islands.")); Parameters.Add(new ValueLookupParameter <IOperator>("Migrator", "The migration strategy.")); Parameters.Add(new ValueLookupParameter <IOperator>("EmigrantsSelector", "Selects the individuals that will be migrated.")); Parameters.Add(new ValueLookupParameter <IOperator>("ImmigrationReplacer", "Replaces some of the original population with the immigrants.")); Parameters.Add(new ValueLookupParameter <IntValue>("PopulationSize", "The size of the population of solutions.")); Parameters.Add(new ValueLookupParameter <IntValue>("MaximumGenerations", "The maximum number of generations that the algorithm should process.")); Parameters.Add(new ValueLookupParameter <IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter <IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter <IOperator>("Evaluator", "The operator used to evaluate solutions.")); Parameters.Add(new ValueLookupParameter <IntValue>("Elites", "The numer of elite solutions which are kept in each generation.")); Parameters.Add(new ValueLookupParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); Parameters.Add(new ValueLookupParameter <ResultCollection>("Results", "The results collection to store the results.")); Parameters.Add(new ValueLookupParameter <IOperator>("Analyzer", "The operator used to the analyze the islands.")); Parameters.Add(new ValueLookupParameter <IOperator>("IslandAnalyzer", "The operator used to analyze each island.")); Parameters.Add(new LookupParameter <IntValue>("EvaluatedSolutions", "The number of times a solution has been evaluated.")); Parameters.Add(new LookupParameter <IntValue>("IslandGenerations", "The number of generations calculated on one island.")); Parameters.Add(new LookupParameter <IntValue>("IslandEvaluatedSolutions", "The number of times a solution has been evaluated on one island.")); Parameters.Add(new ValueLookupParameter <BoolValue>("Migrate", "Migrate the island?")); #endregion #region Create operators VariableCreator variableCreator = new VariableCreator(); UniformSubScopesProcessor uniformSubScopesProcessor0 = new UniformSubScopesProcessor(); VariableCreator islandVariableCreator = new VariableCreator(); Placeholder islandAnalyzer1 = new Placeholder(); LocalRandomCreator localRandomCreator = new LocalRandomCreator(); Placeholder analyzer1 = new Placeholder(); ResultsCollector resultsCollector1 = new ResultsCollector(); UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor(); Assigner generationsAssigner = new Assigner(); Assigner evaluatedSolutionsAssigner = new Assigner(); Placeholder selector = new Placeholder(); SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor(); ChildrenCreator childrenCreator = new ChildrenCreator(); UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor(); Placeholder crossover = new Placeholder(); StochasticBranch stochasticBranch = new StochasticBranch(); Placeholder mutator = new Placeholder(); SubScopesRemover subScopesRemover = new SubScopesRemover(); UniformSubScopesProcessor uniformSubScopesProcessor3 = new UniformSubScopesProcessor(); Placeholder evaluator = new Placeholder(); SubScopesCounter subScopesCounter = new SubScopesCounter(); SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor(); BestSelector bestSelector = new BestSelector(); RightReducer rightReducer = new RightReducer(); MergingReducer mergingReducer = new MergingReducer(); IntCounter islandGenerationsCounter = new IntCounter(); Comparator checkIslandGenerationsReachedMaximum = new Comparator(); ConditionalBranch checkContinueEvolution = new ConditionalBranch(); DataReducer generationsReducer = new DataReducer(); DataReducer evaluatedSolutionsReducer = new DataReducer(); Placeholder islandAnalyzer2 = new Placeholder(); UniformSubScopesProcessor uniformSubScopesProcessor5 = new UniformSubScopesProcessor(); Placeholder emigrantsSelector = new Placeholder(); IntCounter migrationsCounter = new IntCounter(); Placeholder migrator = new Placeholder(); UniformSubScopesProcessor uniformSubScopesProcessor6 = new UniformSubScopesProcessor(); Placeholder immigrationReplacer = new Placeholder(); Comparator generationsComparator = new Comparator(); Placeholder analyzer2 = new Placeholder(); ConditionalBranch generationsTerminationCondition = new ConditionalBranch(); ConditionalBranch reevaluateElitesBranch = new ConditionalBranch(); variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Migrations", new IntValue(0))); variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("GenerationsSinceLastMigration", new IntValue(0))); variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Generations", new IntValue(0))); // Class IslandGeneticAlgorithm expects this to be called Generations islandVariableCreator.CollectedValues.Add(new ValueParameter <ResultCollection>("Results", new ResultCollection())); islandVariableCreator.CollectedValues.Add(new ValueParameter <IntValue>("IslandGenerations", new IntValue(0))); islandVariableCreator.CollectedValues.Add(new ValueParameter <IntValue>("IslandEvaluatedSolutions", new IntValue(0))); islandAnalyzer1.Name = "Island Analyzer (placeholder)"; islandAnalyzer1.OperatorParameter.ActualName = IslandAnalyzerParameter.Name; analyzer1.Name = "Analyzer (placeholder)"; analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name; resultsCollector1.CollectedValues.Add(new LookupParameter <IntValue>("Migrations")); resultsCollector1.CollectedValues.Add(new LookupParameter <IntValue>("Generations")); resultsCollector1.CollectedValues.Add(new ScopeTreeLookupParameter <ResultCollection>("IslandResults", "Result set for each island", "Results")); resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name; uniformSubScopesProcessor1.Parallel.Value = true; generationsAssigner.Name = "Initialize Island Generations"; generationsAssigner.LeftSideParameter.ActualName = IslandGenerations.Name; generationsAssigner.RightSideParameter.Value = new IntValue(0); evaluatedSolutionsAssigner.Name = "Initialize Island evaluated solutions"; evaluatedSolutionsAssigner.LeftSideParameter.ActualName = IslandEvaluatedSolutions.Name; evaluatedSolutionsAssigner.RightSideParameter.Value = new IntValue(0); selector.Name = "Selector (placeholder)"; selector.OperatorParameter.ActualName = SelectorParameter.Name; childrenCreator.ParentsPerChild = new IntValue(2); crossover.Name = "Crossover (placeholder)"; crossover.OperatorParameter.ActualName = CrossoverParameter.Name; stochasticBranch.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name; //set it to the random number generator of the island stochasticBranch.RandomParameter.ActualName = "LocalRandom"; mutator.Name = "Mutator (placeholder)"; mutator.OperatorParameter.ActualName = MutatorParameter.Name; subScopesRemover.RemoveAllSubScopes = true; evaluator.Name = "Evaluator (placeholder)"; evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter.Name = "Increment EvaluatedSolutions"; subScopesCounter.ValueParameter.ActualName = IslandEvaluatedSolutions.Name; bestSelector.CopySelected = new BoolValue(false); bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name; bestSelector.QualityParameter.ActualName = QualityParameter.Name; islandGenerationsCounter.Name = "Increment island generatrions"; islandGenerationsCounter.ValueParameter.ActualName = IslandGenerations.Name; islandGenerationsCounter.Increment = new IntValue(1); checkIslandGenerationsReachedMaximum.LeftSideParameter.ActualName = IslandGenerations.Name; checkIslandGenerationsReachedMaximum.RightSideParameter.ActualName = MigrationIntervalParameter.Name; checkIslandGenerationsReachedMaximum.Comparison = new Comparison(ComparisonType.GreaterOrEqual); checkIslandGenerationsReachedMaximum.ResultParameter.ActualName = Migrate.Name; checkContinueEvolution.Name = "Migrate?"; checkContinueEvolution.ConditionParameter.ActualName = Migrate.Name; checkContinueEvolution.FalseBranch = selector; islandAnalyzer2.Name = "Island Analyzer (placeholder)"; islandAnalyzer2.OperatorParameter.ActualName = IslandAnalyzerParameter.Name; generationsReducer.Name = "Increment Generations"; generationsReducer.ParameterToReduce.ActualName = islandGenerationsCounter.ValueParameter.ActualName; generationsReducer.TargetParameter.ActualName = "Generations"; generationsReducer.ReductionOperation.Value = new ReductionOperation(ReductionOperations.Min); generationsReducer.TargetOperation.Value = new ReductionOperation(ReductionOperations.Sum); evaluatedSolutionsReducer.Name = "Increment Evaluated Solutions"; evaluatedSolutionsReducer.ParameterToReduce.ActualName = IslandEvaluatedSolutions.Name; evaluatedSolutionsReducer.TargetParameter.ActualName = EvaluatedSolutionsParameter.Name; evaluatedSolutionsReducer.ReductionOperation.Value = new ReductionOperation(ReductionOperations.Sum); evaluatedSolutionsReducer.TargetOperation.Value = new ReductionOperation(ReductionOperations.Sum); emigrantsSelector.Name = "Emigrants Selector (placeholder)"; emigrantsSelector.OperatorParameter.ActualName = EmigrantsSelectorParameter.Name; migrationsCounter.Name = "Increment number of Migrations"; migrationsCounter.ValueParameter.ActualName = "Migrations"; migrationsCounter.Increment = new IntValue(1); migrator.Name = "Migrator (placeholder)"; migrator.OperatorParameter.ActualName = MigratorParameter.Name; immigrationReplacer.Name = "Immigration Replacer (placeholder)"; immigrationReplacer.OperatorParameter.ActualName = ImmigrationReplacerParameter.Name; generationsComparator.Name = "Generations >= MaximumGenerations ?"; generationsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual); generationsComparator.LeftSideParameter.ActualName = "Generations"; generationsComparator.ResultParameter.ActualName = "TerminateGenerations"; generationsComparator.RightSideParameter.ActualName = MaximumGenerationsParameter.Name; analyzer2.Name = "Analyzer (placeholder)"; analyzer2.OperatorParameter.ActualName = AnalyzerParameter.Name; generationsTerminationCondition.Name = "Terminate?"; generationsTerminationCondition.ConditionParameter.ActualName = "TerminateGenerations"; reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites"; reevaluateElitesBranch.Name = "Reevaluate elites ?"; #endregion #region Create operator graph OperatorGraph.InitialOperator = variableCreator; variableCreator.Successor = uniformSubScopesProcessor0; uniformSubScopesProcessor0.Operator = islandVariableCreator; uniformSubScopesProcessor0.Successor = analyzer1; islandVariableCreator.Successor = islandAnalyzer1; // BackwardsCompatibility3.3 //the local randoms are created by the island GA itself and are only here to ensure same algorithm results #region Backwards compatible code, remove local random creator with 3.4 and rewire the operator graph islandAnalyzer1.Successor = localRandomCreator; localRandomCreator.Successor = null; #endregion analyzer1.Successor = resultsCollector1; resultsCollector1.Successor = uniformSubScopesProcessor1; uniformSubScopesProcessor1.Operator = generationsAssigner; uniformSubScopesProcessor1.Successor = generationsReducer; generationsReducer.Successor = evaluatedSolutionsReducer; evaluatedSolutionsReducer.Successor = migrationsCounter; migrationsCounter.Successor = uniformSubScopesProcessor5; generationsAssigner.Successor = evaluatedSolutionsAssigner; evaluatedSolutionsAssigner.Successor = selector; selector.Successor = subScopesProcessor1; subScopesProcessor1.Operators.Add(new EmptyOperator()); subScopesProcessor1.Operators.Add(childrenCreator); subScopesProcessor1.Successor = subScopesProcessor2; childrenCreator.Successor = uniformSubScopesProcessor2; uniformSubScopesProcessor2.Operator = crossover; uniformSubScopesProcessor2.Successor = uniformSubScopesProcessor3; crossover.Successor = stochasticBranch; stochasticBranch.FirstBranch = mutator; stochasticBranch.SecondBranch = null; stochasticBranch.Successor = subScopesRemover; mutator.Successor = null; subScopesRemover.Successor = null; uniformSubScopesProcessor3.Operator = evaluator; uniformSubScopesProcessor3.Successor = subScopesCounter; evaluator.Successor = null; subScopesCounter.Successor = null; subScopesProcessor2.Operators.Add(bestSelector); subScopesProcessor2.Operators.Add(new EmptyOperator()); subScopesProcessor2.Successor = mergingReducer; mergingReducer.Successor = islandAnalyzer2; bestSelector.Successor = rightReducer; rightReducer.Successor = reevaluateElitesBranch; reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor3; reevaluateElitesBranch.FalseBranch = null; reevaluateElitesBranch.Successor = null; islandAnalyzer2.Successor = islandGenerationsCounter; islandGenerationsCounter.Successor = checkIslandGenerationsReachedMaximum; checkIslandGenerationsReachedMaximum.Successor = checkContinueEvolution; uniformSubScopesProcessor5.Operator = emigrantsSelector; emigrantsSelector.Successor = null; uniformSubScopesProcessor5.Successor = migrator; migrator.Successor = uniformSubScopesProcessor6; uniformSubScopesProcessor6.Operator = immigrationReplacer; uniformSubScopesProcessor6.Successor = generationsComparator; generationsComparator.Successor = analyzer2; analyzer2.Successor = generationsTerminationCondition; generationsTerminationCondition.TrueBranch = null; generationsTerminationCondition.FalseBranch = uniformSubScopesProcessor1; generationsTerminationCondition.Successor = null; #endregion }
private void Initialize() { #region Create parameters Parameters.Add(new ValueLookupParameter <IRandom>("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter <BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter <IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter <IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization.")); Parameters.Add(new LookupParameter <IntValue>("EvaluatedSolutions", "The number of evaluated solutions.")); Parameters.Add(new ValueLookupParameter <IntValue>("Elites", "The numer of elite solutions which are kept in each generation.")); Parameters.Add(new ValueLookupParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); Parameters.Add(new LookupParameter <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].")); Parameters.Add(new LookupParameter <DoubleValue>("CurrentSuccessRatio", "The current success ratio.")); Parameters.Add(new ValueLookupParameter <DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.")); Parameters.Add(new LookupParameter <DoubleValue>("SelectionPressure", "The actual selection pressure.")); Parameters.Add(new ValueLookupParameter <DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.")); 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.")); Parameters.Add(new ValueLookupParameter <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.")); #endregion #region Create operators Placeholder selector = new Placeholder(); SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor(); ChildrenCreator childrenCreator = new ChildrenCreator(); ConditionalBranch osBeforeMutationBranch = new ConditionalBranch(); UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor(); Placeholder crossover1 = new Placeholder(); UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor(); Placeholder evaluator1 = new Placeholder(); SubScopesCounter subScopesCounter1 = new SubScopesCounter(); WeightedParentsQualityComparator qualityComparer1 = new WeightedParentsQualityComparator(); SubScopesRemover subScopesRemover1 = new SubScopesRemover(); UniformSubScopesProcessor uniformSubScopesProcessor3 = new UniformSubScopesProcessor(); StochasticBranch mutationBranch1 = new StochasticBranch(); Placeholder mutator1 = new Placeholder(); VariableCreator variableCreator1 = new VariableCreator(); VariableCreator variableCreator2 = new VariableCreator(); ConditionalSelector conditionalSelector = new ConditionalSelector(); SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor(); UniformSubScopesProcessor uniformSubScopesProcessor4 = new UniformSubScopesProcessor(); Placeholder evaluator2 = new Placeholder(); SubScopesCounter subScopesCounter2 = new SubScopesCounter(); MergingReducer mergingReducer1 = new MergingReducer(); UniformSubScopesProcessor uniformSubScopesProcessor5 = new UniformSubScopesProcessor(); Placeholder crossover2 = new Placeholder(); StochasticBranch mutationBranch2 = new StochasticBranch(); Placeholder mutator2 = new Placeholder(); UniformSubScopesProcessor uniformSubScopesProcessor6 = new UniformSubScopesProcessor(); Placeholder evaluator3 = new Placeholder(); SubScopesCounter subScopesCounter3 = new SubScopesCounter(); WeightedParentsQualityComparator qualityComparer2 = new WeightedParentsQualityComparator(); SubScopesRemover subScopesRemover2 = new SubScopesRemover(); OffspringSelector offspringSelector = new OffspringSelector(); SubScopesProcessor subScopesProcessor3 = new SubScopesProcessor(); BestSelector bestSelector = new BestSelector(); WorstSelector worstSelector = new WorstSelector(); RightReducer rightReducer = new RightReducer(); LeftReducer leftReducer = new LeftReducer(); MergingReducer mergingReducer2 = new MergingReducer(); ConditionalBranch reevaluateElitesBranch = new ConditionalBranch(); UniformSubScopesProcessor uniformSubScopesProcessor7 = new UniformSubScopesProcessor(); Placeholder evaluator4 = new Placeholder(); SubScopesCounter subScopesCounter4 = new SubScopesCounter(); selector.Name = "Selector (placeholder)"; selector.OperatorParameter.ActualName = SelectorParameter.Name; childrenCreator.ParentsPerChild = new IntValue(2); osBeforeMutationBranch.Name = "Apply OS before mutation?"; osBeforeMutationBranch.ConditionParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name; crossover1.Name = "Crossover (placeholder)"; crossover1.OperatorParameter.ActualName = CrossoverParameter.Name; uniformSubScopesProcessor2.Parallel.Value = true; evaluator1.Name = "Evaluator (placeholder)"; evaluator1.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter1.Name = "Increment EvaluatedSolutions"; subScopesCounter1.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; qualityComparer1.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name; qualityComparer1.LeftSideParameter.ActualName = QualityParameter.Name; qualityComparer1.MaximizationParameter.ActualName = MaximizationParameter.Name; qualityComparer1.RightSideParameter.ActualName = QualityParameter.Name; qualityComparer1.ResultParameter.ActualName = "SuccessfulOffspring"; subScopesRemover1.RemoveAllSubScopes = true; mutationBranch1.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name; mutationBranch1.RandomParameter.ActualName = RandomParameter.Name; mutator1.Name = "Mutator (placeholder)"; mutator1.OperatorParameter.ActualName = MutatorParameter.Name; variableCreator1.Name = "MutatedOffspring = true"; variableCreator1.CollectedValues.Add(new ValueParameter <BoolValue>("MutatedOffspring", null, new BoolValue(true), false)); variableCreator2.Name = "MutatedOffspring = false"; variableCreator2.CollectedValues.Add(new ValueParameter <BoolValue>("MutatedOffspring", null, new BoolValue(false), false)); conditionalSelector.ConditionParameter.ActualName = "MutatedOffspring"; conditionalSelector.ConditionParameter.Depth = 1; conditionalSelector.CopySelected.Value = false; uniformSubScopesProcessor4.Parallel.Value = true; evaluator2.Name = "Evaluator (placeholder)"; evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter2.Name = "Increment EvaluatedSolutions"; subScopesCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; crossover2.Name = "Crossover (placeholder)"; crossover2.OperatorParameter.ActualName = CrossoverParameter.Name; mutationBranch2.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name; mutationBranch2.RandomParameter.ActualName = RandomParameter.Name; mutator2.Name = "Mutator (placeholder)"; mutator2.OperatorParameter.ActualName = MutatorParameter.Name; uniformSubScopesProcessor6.Parallel.Value = true; evaluator3.Name = "Evaluator (placeholder)"; evaluator3.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter3.Name = "Increment EvaluatedSolutions"; subScopesCounter3.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; qualityComparer2.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name; qualityComparer2.LeftSideParameter.ActualName = QualityParameter.Name; qualityComparer2.MaximizationParameter.ActualName = MaximizationParameter.Name; qualityComparer2.RightSideParameter.ActualName = QualityParameter.Name; qualityComparer2.ResultParameter.ActualName = "SuccessfulOffspring"; subScopesRemover2.RemoveAllSubScopes = true; offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name; offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name; offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name; offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name; offspringSelector.OffspringPopulationParameter.ActualName = "OffspringPopulation"; offspringSelector.OffspringPopulationWinnersParameter.ActualName = "OffspringPopulationWinners"; offspringSelector.SuccessfulOffspringParameter.ActualName = "SuccessfulOffspring"; offspringSelector.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name; bestSelector.CopySelected = new BoolValue(false); bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name; bestSelector.QualityParameter.ActualName = QualityParameter.Name; worstSelector.CopySelected = new BoolValue(false); worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name; worstSelector.QualityParameter.ActualName = QualityParameter.Name; reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites"; reevaluateElitesBranch.Name = "Reevaluate elites ?"; uniformSubScopesProcessor7.Parallel.Value = true; evaluator4.Name = "Evaluator (placeholder)"; evaluator4.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter4.Name = "Increment EvaluatedSolutions"; subScopesCounter4.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; #endregion #region Create operator graph OperatorGraph.InitialOperator = selector; selector.Successor = subScopesProcessor1; subScopesProcessor1.Operators.Add(new EmptyOperator()); subScopesProcessor1.Operators.Add(childrenCreator); subScopesProcessor1.Successor = offspringSelector; childrenCreator.Successor = osBeforeMutationBranch; osBeforeMutationBranch.TrueBranch = uniformSubScopesProcessor1; osBeforeMutationBranch.FalseBranch = uniformSubScopesProcessor5; osBeforeMutationBranch.Successor = null; uniformSubScopesProcessor1.Operator = crossover1; uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2; crossover1.Successor = null; uniformSubScopesProcessor2.Operator = evaluator1; uniformSubScopesProcessor2.Successor = subScopesCounter1; evaluator1.Successor = qualityComparer1; qualityComparer1.Successor = subScopesRemover1; subScopesRemover1.Successor = null; subScopesCounter1.Successor = uniformSubScopesProcessor3; uniformSubScopesProcessor3.Operator = mutationBranch1; uniformSubScopesProcessor3.Successor = conditionalSelector; mutationBranch1.FirstBranch = mutator1; mutationBranch1.SecondBranch = variableCreator2; mutationBranch1.Successor = null; mutator1.Successor = variableCreator1; variableCreator1.Successor = null; variableCreator2.Successor = null; conditionalSelector.Successor = subScopesProcessor2; subScopesProcessor2.Operators.Add(new EmptyOperator()); subScopesProcessor2.Operators.Add(uniformSubScopesProcessor4); subScopesProcessor2.Successor = mergingReducer1; uniformSubScopesProcessor4.Operator = evaluator2; uniformSubScopesProcessor4.Successor = subScopesCounter2; evaluator2.Successor = null; subScopesCounter2.Successor = null; mergingReducer1.Successor = null; uniformSubScopesProcessor5.Operator = crossover2; uniformSubScopesProcessor5.Successor = uniformSubScopesProcessor6; crossover2.Successor = mutationBranch2; mutationBranch2.FirstBranch = mutator2; mutationBranch2.SecondBranch = null; mutationBranch2.Successor = null; mutator2.Successor = null; uniformSubScopesProcessor6.Operator = evaluator3; uniformSubScopesProcessor6.Successor = subScopesCounter3; evaluator3.Successor = qualityComparer2; qualityComparer2.Successor = subScopesRemover2; subScopesRemover2.Successor = null; subScopesCounter3.Successor = null; offspringSelector.OffspringCreator = selector; offspringSelector.Successor = subScopesProcessor3; subScopesProcessor3.Operators.Add(bestSelector); subScopesProcessor3.Operators.Add(worstSelector); subScopesProcessor3.Successor = mergingReducer2; bestSelector.Successor = rightReducer; rightReducer.Successor = reevaluateElitesBranch; reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor7; uniformSubScopesProcessor7.Operator = evaluator4; uniformSubScopesProcessor7.Successor = subScopesCounter4; subScopesCounter4.Successor = null; reevaluateElitesBranch.FalseBranch = null; reevaluateElitesBranch.Successor = null; worstSelector.Successor = leftReducer; leftReducer.Successor = null; mergingReducer2.Successor = null; #endregion }
public AlpsGeneticAlgorithmMainOperator() : base() { Parameters.Add(new ValueLookupParameter <IRandom>("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter <IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization.")); Parameters.Add(new ValueLookupParameter <IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter <BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ValueLookupParameter <IntValue>("PopulationSize", "The size of the population of solutions in each layer.")); Parameters.Add(new ValueLookupParameter <IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter <IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter <IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter <IntValue>("Elites", "The numer of elite solutions which are kept in each generation.")); Parameters.Add(new ValueLookupParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); Parameters.Add(new ValueLookupParameter <BoolValue>("PlusSelection", "Include the parents in the selection of the invividuals for the next generation.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Age", "The age of individuals.")); Parameters.Add(new ValueLookupParameter <DoubleValue>("AgeInheritance", "A weight that determines the age of a child after crossover based on the older (1.0) and younger (0.0) parent.")); Parameters.Add(new ValueLookupParameter <DoubleValue>("AgeIncrement", "The value the age the individuals is incremented if they survives a generation.")); var numberOfSelectedParentsCalculator = new ExpressionCalculator() { Name = "NumberOfSelectedParents = 2 * (PopulationSize - (PlusSelection ? 0 : Elites))" }; var selector = new Placeholder() { Name = "Selector (Placeholder)" }; var subScopesProcessor1 = new SubScopesProcessor(); var childrenCreator = new ChildrenCreator(); var uniformSubScopesProcessor1 = new UniformSubScopesProcessor(); var crossover = new Placeholder() { Name = "Crossover (Placeholder)" }; var stochasticBranch = new StochasticBranch() { Name = "MutationProbability" }; var mutator = new Placeholder() { Name = "Mutator (Placeholder)" }; var ageCalculator = new WeightingReducer() { Name = "Calculate Age" }; var subScopesRemover = new SubScopesRemover(); var uniformSubScopesProcessor2 = new UniformSubScopesProcessor(); var evaluator = new Placeholder() { Name = "Evaluator (Placeholder)" }; var subScopesCounter = new SubScopesCounter() { Name = "Increment EvaluatedSolutions" }; var replacementBranch = new ConditionalBranch() { Name = "PlusSelection?" }; var replacementMergingReducer = new MergingReducer(); var replacementBestSelector = new BestSelector(); var replacementRightReducer = new RightReducer(); var subScopesProcessor2 = new SubScopesProcessor(); var bestSelector = new BestSelector(); var rightReducer = new RightReducer(); var mergingReducer = new MergingReducer(); var reevaluateElitesBranch = new ConditionalBranch() { Name = "Reevaluate elites ?" }; var incrementAgeProcessor = new UniformSubScopesProcessor(); var ageIncrementor = new DoubleCounter() { Name = "Increment Age" }; OperatorGraph.InitialOperator = numberOfSelectedParentsCalculator; numberOfSelectedParentsCalculator.CollectedValues.Add(new LookupParameter <IntValue>(PopulationSizeParameter.Name)); numberOfSelectedParentsCalculator.CollectedValues.Add(new LookupParameter <IntValue>(ElitesParameter.Name)); numberOfSelectedParentsCalculator.CollectedValues.Add(new LookupParameter <BoolValue>(PlusSelectionParameter.Name)); numberOfSelectedParentsCalculator.ExpressionResultParameter.ActualName = "NumberOfSelectedSubScopes"; numberOfSelectedParentsCalculator.ExpressionParameter.Value = new StringValue("PopulationSize 0 Elites PlusSelection if - 2 * toint"); numberOfSelectedParentsCalculator.Successor = selector; selector.OperatorParameter.ActualName = SelectorParameter.Name; selector.Successor = subScopesProcessor1; subScopesProcessor1.Operators.Add(new EmptyOperator()); subScopesProcessor1.Operators.Add(childrenCreator); subScopesProcessor1.Successor = replacementBranch; childrenCreator.ParentsPerChild = new IntValue(2); childrenCreator.Successor = uniformSubScopesProcessor1; uniformSubScopesProcessor1.Operator = crossover; uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2; crossover.OperatorParameter.ActualName = CrossoverParameter.Name; crossover.Successor = stochasticBranch; stochasticBranch.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name; stochasticBranch.RandomParameter.ActualName = RandomParameter.Name; stochasticBranch.FirstBranch = mutator; stochasticBranch.SecondBranch = null; stochasticBranch.Successor = ageCalculator; mutator.OperatorParameter.ActualName = MutatorParameter.Name; mutator.Successor = null; ageCalculator.ParameterToReduce.ActualName = AgeParameter.Name; ageCalculator.TargetParameter.ActualName = AgeParameter.Name; ageCalculator.WeightParameter.ActualName = AgeInheritanceParameter.Name; ageCalculator.Successor = subScopesRemover; subScopesRemover.RemoveAllSubScopes = true; subScopesRemover.Successor = null; uniformSubScopesProcessor2.Parallel.Value = true; uniformSubScopesProcessor2.Operator = evaluator; uniformSubScopesProcessor2.Successor = subScopesCounter; evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name; evaluator.Successor = null; subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; subScopesCounter.AccumulateParameter.Value = new BoolValue(true); subScopesCounter.Successor = null; replacementBranch.ConditionParameter.ActualName = PlusSelectionParameter.Name; replacementBranch.TrueBranch = replacementMergingReducer; replacementBranch.FalseBranch = subScopesProcessor2; replacementBranch.Successor = incrementAgeProcessor; replacementMergingReducer.Successor = replacementBestSelector; replacementBestSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name; replacementBestSelector.CopySelected = new BoolValue(false); replacementBestSelector.Successor = replacementRightReducer; replacementRightReducer.Successor = reevaluateElitesBranch; subScopesProcessor2.Operators.Add(bestSelector); subScopesProcessor2.Operators.Add(new EmptyOperator()); subScopesProcessor2.Successor = mergingReducer; bestSelector.CopySelected = new BoolValue(false); bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name; bestSelector.QualityParameter.ActualName = QualityParameter.Name; bestSelector.Successor = rightReducer; rightReducer.Successor = reevaluateElitesBranch; mergingReducer.Successor = null; reevaluateElitesBranch.ConditionParameter.ActualName = ReevaluateElitesParameter.Name; reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor2; reevaluateElitesBranch.FalseBranch = null; reevaluateElitesBranch.Successor = null; incrementAgeProcessor.Operator = ageIncrementor; incrementAgeProcessor.Successor = null; ageIncrementor.ValueParameter.ActualName = AgeParameter.Name; ageIncrementor.IncrementParameter.Value = null; ageIncrementor.IncrementParameter.ActualName = AgeIncrementParameter.Name; ageIncrementor.Successor = null; }
private void Initialize() { #region Create parameters Parameters.Add(new ValueLookupParameter <IRandom>("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter <BoolArray>("Maximization", "True if an objective should be maximized, or false if it should be minimized.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleArray>("Qualities", "The vector of quality values.")); Parameters.Add(new ValueLookupParameter <IntValue>("PopulationSize", "The population size.")); Parameters.Add(new ValueLookupParameter <IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter <PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter <IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization.")); Parameters.Add(new ValueLookupParameter <IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.")); Parameters.Add(new ValueLookupParameter <VariableCollection>("Results", "The variable collection where results should be stored.")); Parameters.Add(new ValueLookupParameter <IOperator>("Analyzer", "The operator used to analyze each generation.")); Parameters.Add(new LookupParameter <IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated.")); Parameters.Add(new ValueLookupParameter <BoolValue>("DominateOnEqualQualities", "Flag which determines wether solutions with equal quality values should be treated as dominated.")); #endregion #region Create operators VariableCreator variableCreator = new VariableCreator(); ResultsCollector resultsCollector1 = new ResultsCollector(); Placeholder analyzer1 = new Placeholder(); Placeholder selector = new Placeholder(); SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor(); ChildrenCreator childrenCreator = new ChildrenCreator(); UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor(); StochasticBranch crossoverStochasticBranch = new StochasticBranch(); Placeholder crossover = new Placeholder(); ParentCopyCrossover noCrossover = new ParentCopyCrossover(); StochasticBranch mutationStochasticBranch = new StochasticBranch(); Placeholder mutator = new Placeholder(); SubScopesRemover subScopesRemover = new SubScopesRemover(); UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor(); Placeholder evaluator = new Placeholder(); SubScopesCounter subScopesCounter = new SubScopesCounter(); MergingReducer mergingReducer = new MergingReducer(); RankAndCrowdingSorter rankAndCrowdingSorter = new RankAndCrowdingSorter(); LeftSelector leftSelector = new LeftSelector(); RightReducer rightReducer = new RightReducer(); IntCounter intCounter = new IntCounter(); Comparator comparator = new Comparator(); Placeholder analyzer2 = new Placeholder(); ConditionalBranch conditionalBranch = new ConditionalBranch(); variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Generations", new IntValue(0))); resultsCollector1.CollectedValues.Add(new LookupParameter <IntValue>("Generations")); resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name; analyzer1.Name = "Analyzer"; analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name; selector.Name = "Selector"; selector.OperatorParameter.ActualName = SelectorParameter.Name; childrenCreator.ParentsPerChild = new IntValue(2); crossoverStochasticBranch.ProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name; crossoverStochasticBranch.RandomParameter.ActualName = RandomParameter.Name; crossover.Name = "Crossover"; crossover.OperatorParameter.ActualName = CrossoverParameter.Name; noCrossover.Name = "Clone parent"; noCrossover.RandomParameter.ActualName = RandomParameter.Name; mutationStochasticBranch.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name; mutationStochasticBranch.RandomParameter.ActualName = RandomParameter.Name; mutator.Name = "Mutator"; mutator.OperatorParameter.ActualName = MutatorParameter.Name; subScopesRemover.RemoveAllSubScopes = true; uniformSubScopesProcessor2.Parallel.Value = true; evaluator.Name = "Evaluator"; evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter.Name = "Increment EvaluatedSolutions"; subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; rankAndCrowdingSorter.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name; rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance"; rankAndCrowdingSorter.RankParameter.ActualName = "Rank"; leftSelector.CopySelected = new BoolValue(false); leftSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name; intCounter.Increment = new IntValue(1); intCounter.ValueParameter.ActualName = "Generations"; comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual); comparator.LeftSideParameter.ActualName = "Generations"; comparator.ResultParameter.ActualName = "Terminate"; comparator.RightSideParameter.ActualName = MaximumGenerationsParameter.Name; analyzer2.Name = "Analyzer"; analyzer2.OperatorParameter.ActualName = "Analyzer"; conditionalBranch.ConditionParameter.ActualName = "Terminate"; #endregion #region Create operator graph OperatorGraph.InitialOperator = variableCreator; variableCreator.Successor = resultsCollector1; resultsCollector1.Successor = analyzer1; analyzer1.Successor = selector; selector.Successor = subScopesProcessor1; subScopesProcessor1.Operators.Add(new EmptyOperator()); subScopesProcessor1.Operators.Add(childrenCreator); subScopesProcessor1.Successor = mergingReducer; childrenCreator.Successor = uniformSubScopesProcessor1; uniformSubScopesProcessor1.Operator = crossoverStochasticBranch; uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2; crossoverStochasticBranch.FirstBranch = crossover; crossoverStochasticBranch.SecondBranch = noCrossover; crossoverStochasticBranch.Successor = mutationStochasticBranch; crossover.Successor = null; noCrossover.Successor = null; mutationStochasticBranch.FirstBranch = mutator; mutationStochasticBranch.SecondBranch = null; mutationStochasticBranch.Successor = subScopesRemover; mutator.Successor = null; subScopesRemover.Successor = null; uniformSubScopesProcessor2.Operator = evaluator; uniformSubScopesProcessor2.Successor = subScopesCounter; evaluator.Successor = null; subScopesCounter.Successor = null; mergingReducer.Successor = rankAndCrowdingSorter; rankAndCrowdingSorter.Successor = leftSelector; leftSelector.Successor = rightReducer; rightReducer.Successor = intCounter; intCounter.Successor = comparator; comparator.Successor = analyzer2; analyzer2.Successor = conditionalBranch; conditionalBranch.FalseBranch = selector; conditionalBranch.TrueBranch = null; conditionalBranch.Successor = null; #endregion }
private void Initialize() { #region Create parameters Parameters.Add(new ValueLookupParameter <IRandom>("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter <BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter <IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter <IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization.")); Parameters.Add(new ValueLookupParameter <IntValue>("Elites", "The numer of elite solutions which are kept in each generation.")); Parameters.Add(new ValueLookupParameter <BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); Parameters.Add(new ValueLookupParameter <IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.")); Parameters.Add(new ValueLookupParameter <VariableCollection>("Results", "The variable collection where results should be stored.")); Parameters.Add(new ValueLookupParameter <IOperator>("Analyzer", "The operator used to analyze each generation.")); Parameters.Add(new ValueLookupParameter <IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated.")); Parameters.Add(new ValueLookupParameter <IntValue>("PopulationSize", "The size of the population.")); Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the genetic algorithm should be applied.")); #endregion #region Create operators VariableCreator variableCreator = new VariableCreator(); ResultsCollector resultsCollector1 = new ResultsCollector(); Placeholder analyzer1 = new Placeholder(); Placeholder selector = new Placeholder(); SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor(); ChildrenCreator childrenCreator = new ChildrenCreator(); UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor(); Placeholder crossover = new Placeholder(); StochasticBranch stochasticBranch = new StochasticBranch(); Placeholder mutator = new Placeholder(); SubScopesRemover subScopesRemover = new SubScopesRemover(); UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor(); Placeholder evaluator = new Placeholder(); SubScopesCounter subScopesCounter = new SubScopesCounter(); SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor(); BestSelector bestSelector = new BestSelector(); RightReducer rightReducer = new RightReducer(); MergingReducer mergingReducer = new MergingReducer(); IntCounter intCounter = new IntCounter(); Comparator comparator = new Comparator(); Placeholder analyzer2 = new Placeholder(); ConditionalBranch conditionalBranch = new ConditionalBranch(); ConditionalBranch reevaluateElitesBranch = new ConditionalBranch(); variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Generations", new IntValue(0))); // Class GeneticAlgorithm expects this to be called Generations resultsCollector1.CollectedValues.Add(new LookupParameter <IntValue>("Generations")); resultsCollector1.ResultsParameter.ActualName = "Results"; analyzer1.Name = "Analyzer"; analyzer1.OperatorParameter.ActualName = "Analyzer"; selector.Name = "Selector"; selector.OperatorParameter.ActualName = "Selector"; childrenCreator.ParentsPerChild = new IntValue(2); crossover.Name = "Crossover"; crossover.OperatorParameter.ActualName = "Crossover"; stochasticBranch.ProbabilityParameter.ActualName = "MutationProbability"; stochasticBranch.RandomParameter.ActualName = "Random"; mutator.Name = "Mutator"; mutator.OperatorParameter.ActualName = "Mutator"; subScopesRemover.RemoveAllSubScopes = true; uniformSubScopesProcessor2.Parallel.Value = true; evaluator.Name = "Evaluator"; evaluator.OperatorParameter.ActualName = "Evaluator"; subScopesCounter.Name = "Increment EvaluatedSolutions"; subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; bestSelector.CopySelected = new BoolValue(false); bestSelector.MaximizationParameter.ActualName = "Maximization"; bestSelector.NumberOfSelectedSubScopesParameter.ActualName = "Elites"; bestSelector.QualityParameter.ActualName = "Quality"; intCounter.Increment = new IntValue(1); intCounter.ValueParameter.ActualName = "Generations"; comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual); comparator.LeftSideParameter.ActualName = "Generations"; comparator.ResultParameter.ActualName = "Terminate"; comparator.RightSideParameter.ActualName = "MaximumGenerations"; analyzer2.Name = "Analyzer"; analyzer2.OperatorParameter.ActualName = "Analyzer"; conditionalBranch.ConditionParameter.ActualName = "Terminate"; reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites"; reevaluateElitesBranch.Name = "Reevaluate elites ?"; #endregion #region Create operator graph OperatorGraph.InitialOperator = variableCreator; variableCreator.Successor = resultsCollector1; resultsCollector1.Successor = analyzer1; analyzer1.Successor = selector; selector.Successor = subScopesProcessor1; subScopesProcessor1.Operators.Add(new EmptyOperator()); subScopesProcessor1.Operators.Add(childrenCreator); subScopesProcessor1.Successor = subScopesProcessor2; childrenCreator.Successor = uniformSubScopesProcessor1; uniformSubScopesProcessor1.Operator = crossover; uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2; crossover.Successor = stochasticBranch; stochasticBranch.FirstBranch = mutator; stochasticBranch.SecondBranch = null; stochasticBranch.Successor = subScopesRemover; mutator.Successor = null; subScopesRemover.Successor = null; uniformSubScopesProcessor2.Operator = evaluator; uniformSubScopesProcessor2.Successor = subScopesCounter; evaluator.Successor = null; subScopesCounter.Successor = null; subScopesProcessor2.Operators.Add(bestSelector); subScopesProcessor2.Operators.Add(new EmptyOperator()); subScopesProcessor2.Successor = mergingReducer; bestSelector.Successor = rightReducer; rightReducer.Successor = reevaluateElitesBranch; reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor2; reevaluateElitesBranch.FalseBranch = null; reevaluateElitesBranch.Successor = null; mergingReducer.Successor = intCounter; intCounter.Successor = comparator; comparator.Successor = analyzer2; analyzer2.Successor = conditionalBranch; conditionalBranch.FalseBranch = selector; conditionalBranch.TrueBranch = null; conditionalBranch.Successor = null; #endregion }
public AlpsNsga2MainOperator() : base() { Parameters.Add(new ValueLookupParameter <IRandom>("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter <IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization.")); Parameters.Add(new ValueLookupParameter <IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter <BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ValueLookupParameter <IntValue>("PopulationSize", "The size of the population of solutions in each layer.")); Parameters.Add(new ValueLookupParameter <IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter <IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter <PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter <IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter <PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Age", "The age of individuals.")); Parameters.Add(new ValueLookupParameter <DoubleValue>("AgeInheritance", "A weight that determines the age of a child after crossover based on the older (1.0) and younger (0.0) parent.")); Parameters.Add(new ValueLookupParameter <DoubleValue>("AgeIncrement", "The value the age the individuals is incremented if they survives a generation.")); Parameters.Add(new ValueLookupParameter <BoolValue>("DominateOnEqualQualities", "Flag which determines whether solutions with equal quality values should be treated as dominated.")); var selector = new Placeholder { Name = "Selector (Placeholder)" }; var subScopesProcessor1 = new SubScopesProcessor(); var childrenCreator = new ChildrenCreator(); var uniformSubScopesProcessor1 = new UniformSubScopesProcessor(); var crossoverStochasticBranch = new StochasticBranch { Name = "CrossoverProbability" }; var crossover = new Placeholder { Name = "Crossover (Placeholder)" }; var noCrossover = new ParentCopyCrossover(); var mutationStochasticBranch = new StochasticBranch { Name = "MutationProbability" }; var mutator = new Placeholder { Name = "Mutator (Placeholder)" }; var ageCalculator = new WeightingReducer { Name = "Calculate Age" }; var subScopesRemover = new SubScopesRemover(); var uniformSubScopesProcessor2 = new UniformSubScopesProcessor(); var evaluator = new Placeholder { Name = "Evaluator (Placeholder)" }; var subScopesCounter = new SubScopesCounter { Name = "Increment EvaluatedSolutions" }; var mergingReducer = new MergingReducer(); var rankAndCrowdingSorter1 = new RankAndCrowdingSorter(); var rankAndCrowdingSorter2 = new RankAndCrowdingSorter(); var leftSelector = new LeftSelector(); var rightReducer = new RightReducer(); var incrementAgeProcessor = new UniformSubScopesProcessor(); var ageIncrementer = new DoubleCounter { Name = "Increment Age" }; OperatorGraph.InitialOperator = rankAndCrowdingSorter1; rankAndCrowdingSorter1.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name; rankAndCrowdingSorter1.CrowdingDistanceParameter.ActualName = "CrowdingDistance"; rankAndCrowdingSorter1.RankParameter.ActualName = "Rank"; rankAndCrowdingSorter1.Successor = selector; selector.OperatorParameter.ActualName = SelectorParameter.Name; selector.Successor = subScopesProcessor1; subScopesProcessor1.Operators.Add(new EmptyOperator()); subScopesProcessor1.Operators.Add(childrenCreator); subScopesProcessor1.Successor = mergingReducer; childrenCreator.ParentsPerChild = new IntValue(2); childrenCreator.Successor = uniformSubScopesProcessor1; uniformSubScopesProcessor1.Operator = crossoverStochasticBranch; uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2; crossoverStochasticBranch.ProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name; crossoverStochasticBranch.RandomParameter.ActualName = RandomParameter.Name; crossoverStochasticBranch.FirstBranch = crossover; crossoverStochasticBranch.SecondBranch = noCrossover; crossoverStochasticBranch.Successor = mutationStochasticBranch; crossover.Name = "Crossover"; crossover.OperatorParameter.ActualName = CrossoverParameter.Name; crossover.Successor = null; noCrossover.Name = "Clone parent"; noCrossover.RandomParameter.ActualName = RandomParameter.Name; noCrossover.Successor = null; mutationStochasticBranch.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name; mutationStochasticBranch.RandomParameter.ActualName = RandomParameter.Name; mutationStochasticBranch.FirstBranch = mutator; mutationStochasticBranch.SecondBranch = null; mutationStochasticBranch.Successor = ageCalculator; mutator.Name = "Mutator"; mutator.OperatorParameter.ActualName = MutatorParameter.Name; mutator.Successor = null; ageCalculator.ParameterToReduce.ActualName = AgeParameter.Name; ageCalculator.TargetParameter.ActualName = AgeParameter.Name; ageCalculator.WeightParameter.ActualName = AgeInheritanceParameter.Name; ageCalculator.Successor = subScopesRemover; subScopesRemover.RemoveAllSubScopes = true; subScopesRemover.Successor = null; uniformSubScopesProcessor2.Parallel.Value = true; uniformSubScopesProcessor2.Operator = evaluator; uniformSubScopesProcessor2.Successor = subScopesCounter; evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name; evaluator.Successor = null; subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; subScopesCounter.AccumulateParameter.Value = new BoolValue(true); subScopesCounter.Successor = null; mergingReducer.Successor = rankAndCrowdingSorter2; rankAndCrowdingSorter2.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name; rankAndCrowdingSorter2.CrowdingDistanceParameter.ActualName = "CrowdingDistance"; rankAndCrowdingSorter2.RankParameter.ActualName = "Rank"; rankAndCrowdingSorter2.Successor = leftSelector; leftSelector.CopySelected = new BoolValue(false); leftSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name; leftSelector.Successor = rightReducer; rightReducer.Successor = incrementAgeProcessor; incrementAgeProcessor.Operator = ageIncrementer; incrementAgeProcessor.Successor = null; ageIncrementer.ValueParameter.ActualName = AgeParameter.Name; ageIncrementer.IncrementParameter.Value = null; ageIncrementer.IncrementParameter.ActualName = AgeIncrementParameter.Name; ageIncrementer.Successor = null; }