public Replacer() { Parameters.Add(new ValueLookupParameter<ISelector>("ReplacedSelector", "The selection operator to select those scopes that are to be replaced. If no selection operator is defined, random selection will be applied.")); Parameters.Add(new ValueLookupParameter<ISelector>("SelectedSelector", "The selection operator to select those scopes that are replacing the others. If no selection operator is defined, random selection will be applied.")); SubScopesProcessor ssp = new SubScopesProcessor(); Placeholder replacedSelector = new Placeholder(); replacedSelector.OperatorParameter.ActualName = ReplacedSelectorParameter.Name; LeftReducer leftReducer = new LeftReducer(); Placeholder selectedSelector = new Placeholder(); selectedSelector.OperatorParameter.ActualName = SelectedSelectorParameter.Name; RightReducer rightReducer = new RightReducer(); MergingReducer merger = new MergingReducer(); OperatorGraph.InitialOperator = ssp; ssp.Operators.Add(replacedSelector); ssp.Operators.Add(selectedSelector); ssp.Successor = merger; replacedSelector.Successor = leftReducer; leftReducer.Successor = null; selectedSelector.Successor = rightReducer; rightReducer.Successor = null; merger.Successor = null; }
public Replacer() { Parameters.Add(new ValueLookupParameter <ISelector>("ReplacedSelector", "The selection operator to select those scopes that are to be replaced. If no selection operator is defined, random selection will be applied.")); Parameters.Add(new ValueLookupParameter <ISelector>("SelectedSelector", "The selection operator to select those scopes that are replacing the others. If no selection operator is defined, random selection will be applied.")); SubScopesProcessor ssp = new SubScopesProcessor(); Placeholder replacedSelector = new Placeholder(); replacedSelector.OperatorParameter.ActualName = ReplacedSelectorParameter.Name; LeftReducer leftReducer = new LeftReducer(); Placeholder selectedSelector = new Placeholder(); selectedSelector.OperatorParameter.ActualName = SelectedSelectorParameter.Name; RightReducer rightReducer = new RightReducer(); MergingReducer merger = new MergingReducer(); OperatorGraph.InitialOperator = ssp; ssp.Operators.Add(replacedSelector); ssp.Operators.Add(selectedSelector); ssp.Successor = merger; replacedSelector.Successor = leftReducer; leftReducer.Successor = null; selectedSelector.Successor = rightReducer; rightReducer.Successor = null; merger.Successor = null; }
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 }
private LeftReducer(LeftReducer original, Cloner cloner) : base(original, cloner) { }
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 }