protected WeightedParentsQualityComparator(WeightedParentsQualityComparator 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 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
}
protected WeightedParentsQualityComparator(WeightedParentsQualityComparator 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<DoubleValue>("BestKnownQuality", "The best known quality value found so far."));
Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "µ (mu) - the size of the population."));
Parameters.Add(new ValueLookupParameter<IntValue>("ParentsPerChild", "ρ (rho) - how many parents should be recombined."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
Parameters.Add(new ValueLookupParameter<BoolValue>("PlusSelection", "True for plus selection (elitist population), false for comma selection (non-elitist population)."));
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<IOperator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueLookupParameter<IOperator>("Recombinator", "The operator used to cross 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<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 ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the OffspringSelectionEvolutionStrategy should be applied."));
Parameters.Add(new ValueLookupParameter<IOperator>("StrategyParameterManipulator", "The operator to mutate the endogeneous strategy parameters."));
Parameters.Add(new ValueLookupParameter<IOperator>("StrategyParameterCrossover", "The operator to cross the endogeneous strategy parameters."));
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<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions."));
Parameters.Add(new ValueLookupParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems."));
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]."));
#endregion
#region Create operators
VariableCreator variableCreator = new VariableCreator();
ResultsCollector resultsCollector1 = new ResultsCollector();
Placeholder analyzer1 = new Placeholder();
WithoutRepeatingBatchedRandomSelector selector = new WithoutRepeatingBatchedRandomSelector();
SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
Comparator useRecombinationComparator = new Comparator();
ConditionalBranch useRecombinationBranch = new ConditionalBranch();
ChildrenCreator childrenCreator = new ChildrenCreator();
UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
Placeholder recombinator = new Placeholder();
Placeholder strategyRecombinator = new Placeholder();
Placeholder strategyMutator1 = new Placeholder();
Placeholder mutator1 = new Placeholder();
SubScopesRemover subScopesRemover = new SubScopesRemover();
UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
Placeholder strategyMutator2 = new Placeholder();
Placeholder mutator2 = new Placeholder();
UniformSubScopesProcessor uniformSubScopesProcessor3 = new UniformSubScopesProcessor();
Placeholder evaluator = new Placeholder();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ConditionalBranch plusOrCommaReplacementBranch = new ConditionalBranch();
MergingReducer plusReplacement = new MergingReducer();
RightReducer commaReplacement = new RightReducer();
BestSelector bestSelector = new BestSelector();
RightReducer rightReducer = new RightReducer();
IntCounter intCounter = new IntCounter();
Comparator maxGenerationsComparator = new Comparator();
Placeholder analyzer2 = new Placeholder();
ConditionalBranch conditionalBranchTerminate = new ConditionalBranch();
ConditionalBranch reevaluateElitesBranch = new ConditionalBranch();
SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
UniformSubScopesProcessor uniformSubScopesProcessor4 = new UniformSubScopesProcessor();
Placeholder evaluator2 = new Placeholder();
SubScopesCounter subScopesCounter2 = new SubScopesCounter();
WeightedParentsQualityComparator parentsComparator = new WeightedParentsQualityComparator();
SubScopesRemover subScopesRemover_afterCompare = new SubScopesRemover();
EvolutionStrategyOffspringSelector offspringSelector = new EvolutionStrategyOffspringSelector();
ChildrenCopyCreator childrenCopyCreator = new ChildrenCopyCreator();
Comparator maxSelectionPressureComparator = new Comparator();
ConditionalBranch conditionalBranchTerminateSelPressure = new ConditionalBranch();
Comparator maxEvaluatedSolutionsComparator = new Comparator();
ConditionalBranch conditionalBranchTerminateEvalSolutions = new ConditionalBranch();
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class OffspringSelectionEvolutionStrategy expects this to be called Generations
variableCreator.CollectedValues.Add(new ValueParameter<DoubleValue>("SelectionPressure", new DoubleValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<DoubleValue>("CurrentSuccessRatio", new DoubleValue(0)));
resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
resultsCollector1.CollectedValues.Add(new LookupParameter<DoubleValue>("Current Selection Pressure", "Displays the rising selection pressure during a generation.", "SelectionPressure"));
resultsCollector1.CollectedValues.Add(new LookupParameter<DoubleValue>("Current Success Ratio", "Indicates how many successful children were already found during a generation (relative to the population size).", "CurrentSuccessRatio"));
resultsCollector1.CopyValue = new BoolValue(false);
resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
analyzer1.Name = "Analyzer (placeholder)";
analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
selector.Name = "ES Random Selector";
selector.RandomParameter.ActualName = RandomParameter.Name;
selector.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
selector.ChildrenParameter.ActualName = SelectedParentsParameter.Name;
useRecombinationComparator.Name = "ParentsPerChild > 1";
useRecombinationComparator.LeftSideParameter.ActualName = ParentsPerChildParameter.Name;
useRecombinationComparator.RightSideParameter.Value = new IntValue(1);
useRecombinationComparator.Comparison = new Comparison(ComparisonType.Greater);
useRecombinationComparator.ResultParameter.ActualName = "UseRecombination";
useRecombinationBranch.Name = "Use Recombination?";
useRecombinationBranch.ConditionParameter.ActualName = "UseRecombination";
childrenCreator.ParentsPerChild = null;
childrenCreator.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
recombinator.Name = "Recombinator (placeholder)";
recombinator.OperatorParameter.ActualName = RecombinatorParameter.Name;
strategyRecombinator.Name = "Strategy Parameter Recombinator (placeholder)";
strategyRecombinator.OperatorParameter.ActualName = StrategyParameterCrossoverParameter.Name;
strategyMutator1.Name = "Strategy Parameter Manipulator (placeholder)";
strategyMutator1.OperatorParameter.ActualName = StrategyParameterManipulatorParameter.Name;
mutator1.Name = "Mutator (placeholder)";
mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
subScopesRemover.RemoveAllSubScopes = true;
strategyMutator2.Name = "Strategy Parameter Manipulator (placeholder)";
strategyMutator2.OperatorParameter.ActualName = StrategyParameterManipulatorParameter.Name;
mutator2.Name = "Mutator (placeholder)";
mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
uniformSubScopesProcessor3.Parallel.Value = true;
evaluator.Name = "Evaluator (placeholder)";
evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter.Name = "Increment EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
plusOrCommaReplacementBranch.ConditionParameter.ActualName = PlusSelectionParameter.Name;
bestSelector.CopySelected = new BoolValue(false);
bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name;
bestSelector.QualityParameter.ActualName = QualityParameter.Name;
intCounter.Increment = new IntValue(1);
intCounter.ValueParameter.ActualName = "Generations";
maxGenerationsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
maxGenerationsComparator.LeftSideParameter.ActualName = "Generations";
maxGenerationsComparator.ResultParameter.ActualName = "Terminate";
maxGenerationsComparator.RightSideParameter.ActualName = MaximumGenerationsParameter.Name;
analyzer2.Name = "Analyzer (placeholder)";
analyzer2.OperatorParameter.ActualName = AnalyzerParameter.Name;
conditionalBranchTerminate.ConditionParameter.ActualName = "Terminate";
reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
reevaluateElitesBranch.Name = "Reevaluate elites ?";
uniformSubScopesProcessor4.Parallel.Value = true;
evaluator2.Name = "Evaluator (placeholder)";
evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter2.Name = "Increment EvaluatedSolutions";
subScopesCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
parentsComparator.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
parentsComparator.LeftSideParameter.ActualName = QualityParameter.Name;
parentsComparator.RightSideParameter.ActualName = QualityParameter.Name;
parentsComparator.MaximizationParameter.ActualName = MaximizationParameter.Name;
parentsComparator.ResultParameter.ActualName = "SuccessfulOffspring";
subScopesRemover_afterCompare.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.QualityParameter.ActualName = QualityParameter.Name;
maxSelectionPressureComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
maxSelectionPressureComparator.LeftSideParameter.ActualName = "SelectionPressure";
maxSelectionPressureComparator.ResultParameter.ActualName = "TerminateSelectionPressure";
maxSelectionPressureComparator.RightSideParameter.ActualName = MaximumSelectionPressureParameter.Name;
conditionalBranchTerminateSelPressure.ConditionParameter.ActualName = "TerminateSelectionPressure";
maxEvaluatedSolutionsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
maxEvaluatedSolutionsComparator.LeftSideParameter.ActualName = "EvaluatedSolutions";
maxEvaluatedSolutionsComparator.ResultParameter.ActualName = "TerminateEvaluatedSolutions";
maxEvaluatedSolutionsComparator.RightSideParameter.ActualName = MaximumEvaluatedSolutionsParameter.Name;
conditionalBranchTerminateEvalSolutions.ConditionParameter.ActualName = "TerminateEvaluatedSolutions";
#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(useRecombinationComparator);
subScopesProcessor1.Successor = offspringSelector;
offspringSelector.OffspringCreator = selector;
offspringSelector.Successor = plusOrCommaReplacementBranch;
useRecombinationComparator.Successor = useRecombinationBranch;
useRecombinationBranch.TrueBranch = childrenCreator;
useRecombinationBranch.FalseBranch = childrenCopyCreator;
childrenCopyCreator.Successor = uniformSubScopesProcessor2;
useRecombinationBranch.Successor = uniformSubScopesProcessor3;
childrenCreator.Successor = uniformSubScopesProcessor1;
uniformSubScopesProcessor1.Operator = recombinator;
uniformSubScopesProcessor1.Successor = null;
recombinator.Successor = strategyRecombinator;
strategyRecombinator.Successor = strategyMutator1;
strategyMutator1.Successor = mutator1;
mutator1.Successor = null;
uniformSubScopesProcessor2.Operator = strategyMutator2;
uniformSubScopesProcessor2.Successor = null;
strategyMutator2.Successor = mutator2;
mutator2.Successor = null;
uniformSubScopesProcessor3.Operator = evaluator;
uniformSubScopesProcessor3.Successor = subScopesCounter;
evaluator.Successor = parentsComparator;
parentsComparator.Successor = subScopesRemover_afterCompare;
subScopesRemover_afterCompare.Successor = null;
subScopesCounter.Successor = null;
plusOrCommaReplacementBranch.TrueBranch = reevaluateElitesBranch;
reevaluateElitesBranch.TrueBranch = subScopesProcessor2;
reevaluateElitesBranch.FalseBranch = null;
subScopesProcessor2.Operators.Add(uniformSubScopesProcessor4);
subScopesProcessor2.Operators.Add(new EmptyOperator());
uniformSubScopesProcessor4.Operator = evaluator2;
uniformSubScopesProcessor4.Successor = subScopesCounter2;
subScopesCounter2.Successor = null;
reevaluateElitesBranch.Successor = plusReplacement;
plusReplacement.Successor = bestSelector;
bestSelector.Successor = rightReducer;
plusOrCommaReplacementBranch.FalseBranch = commaReplacement;
plusOrCommaReplacementBranch.Successor = intCounter;
intCounter.Successor = maxGenerationsComparator;
maxGenerationsComparator.Successor = maxSelectionPressureComparator;
maxSelectionPressureComparator.Successor = maxEvaluatedSolutionsComparator;
maxEvaluatedSolutionsComparator.Successor = analyzer2;
analyzer2.Successor = conditionalBranchTerminate;
conditionalBranchTerminate.FalseBranch = conditionalBranchTerminateSelPressure;
conditionalBranchTerminate.TrueBranch = null;
conditionalBranchTerminate.Successor = null;
conditionalBranchTerminateSelPressure.FalseBranch = conditionalBranchTerminateEvalSolutions;
conditionalBranchTerminateSelPressure.TrueBranch = null;
conditionalBranchTerminateSelPressure.Successor = null;
conditionalBranchTerminateEvalSolutions.FalseBranch = selector;
conditionalBranchTerminateEvalSolutions.TrueBranch = null;
conditionalBranchTerminateEvalSolutions.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 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
}