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