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
}
private ProgressiveOffspringPreserver(ProgressiveOffspringPreserver original, Cloner cloner) : base(original, cloner)
{
}
private ProgressiveOffspringPreserver(ProgressiveOffspringPreserver 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
}