private CombinedOperator CreateReseeder()
{
var reseeder = new CombinedOperator {
Name = "Reseed Layer Zero if needed"
};
var reseedingController = new ReseedingController {
Name = "Reseeding needed (Generation % AgeGap == 0)?"
};
var removeIndividuals = new SubScopesRemover();
var createIndividuals = new SolutionsCreator();
var initializeAgeProcessor = new UniformSubScopesProcessor();
var initializeAge = new VariableCreator {
Name = "Initialize Age"
};
var incrEvaluatedSolutionsAfterReseeding = new SubScopesCounter {
Name = "Update EvaluatedSolutions"
};
var rankAndCrowdingSorter = new RankAndCrowdingSorter();
reseeder.OperatorGraph.InitialOperator = reseedingController;
reseedingController.GenerationsParameter.ActualName = "Generations";
reseedingController.AgeGapParameter.ActualName = AgeGapParameter.Name;
reseedingController.FirstLayerOperator = removeIndividuals;
reseedingController.Successor = null;
removeIndividuals.Successor = createIndividuals;
createIndividuals.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
createIndividuals.Successor = initializeAgeProcessor;
initializeAgeProcessor.Operator = initializeAge;
initializeAgeProcessor.Successor = incrEvaluatedSolutionsAfterReseeding;
initializeAge.CollectedValues.Add(new ValueParameter <DoubleValue>(AgeParameter.Name, new DoubleValue(0)));
incrEvaluatedSolutionsAfterReseeding.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
incrEvaluatedSolutionsAfterReseeding.AccumulateParameter.Value = new BoolValue(true);
incrEvaluatedSolutionsAfterReseeding.Successor = rankAndCrowdingSorter;
return(reseeder);
}
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
}
public NSGA2() {
Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ValueParameter<PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on two parents.", new PercentValue(0.9)));
Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter<IntValue>("SelectedParents", "Each two parents form a new child, typically this value should be twice the population size, but because the NSGA-II is maximally elitist it can be any multiple of 2 greater than 0.", new IntValue(200)));
Parameters.Add(new FixedValueParameter<BoolValue>("DominateOnEqualQualities", "Flag which determines wether solutions with equal quality values should be treated as dominated.", new BoolValue(false)));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
RankAndCrowdingSorter rankAndCrowdingSorter = new RankAndCrowdingSorter();
ResultsCollector resultsCollector = new ResultsCollector();
NSGA2MainLoop mainLoop = new NSGA2MainLoop();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.RandomParameter.ActualName = "Random";
randomCreator.SeedParameter.ActualName = SeedParameter.Name;
randomCreator.SeedParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.Successor = solutionsCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
solutionsCreator.Successor = subScopesCounter;
subScopesCounter.Name = "Initialize EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = rankAndCrowdingSorter;
rankAndCrowdingSorter.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name;
rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
rankAndCrowdingSorter.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.CrossoverProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is ISingleObjectiveSelector)).OrderBy(x => x.Name))
SelectorParameter.ValidValues.Add(selector);
ISelector tournamentSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("CrowdedTournamentSelector"));
if (tournamentSelector != null) SelectorParameter.Value = tournamentSelector;
ParameterizeSelectors();
paretoFrontAnalyzer = new RankBasedParetoFrontAnalyzer();
paretoFrontAnalyzer.RankParameter.ActualName = "Rank";
paretoFrontAnalyzer.RankParameter.Depth = 1;
paretoFrontAnalyzer.ResultsParameter.ActualName = "Results";
ParameterizeAnalyzers();
UpdateAnalyzers();
AfterDeserialization();
}
private CombinedOperator CreateEldersEmigrator()
{
var eldersEmigrator = new CombinedOperator {
Name = "Emigrate Elders"
};
var selectorProcessor = new UniformSubScopesProcessor();
var eldersSelector = new EldersSelector();
var shiftToRightMigrator = new UnidirectionalRingMigrator {
Name = "Shift elders to next layer"
};
var mergingProcessor = new UniformSubScopesProcessor();
var mergingReducer = new MergingReducer();
var subScopesCounter1 = new SubScopesCounter();
var currentPopulationSizeComparator = new Comparator {
Name = "Is CurrentPopulationSize greater than 1?"
};
var currentPopulationSizeIsGreaterThanOne = new ConditionalBranch {
Name = "CurrentPopulationSize > 1"
};
var reduceToPopulationSizeBranch = new ConditionalBranch {
Name = "ReduceToPopulationSize?"
};
var countCalculator = new ExpressionCalculator {
Name = "CurrentPopulationSize = Min(CurrentPopulationSize, PopulationSize)"
};
var leftSelector = new LeftSelector();
var rankAndCrowdingSorter = new RankAndCrowdingSorter();
var subScopesCounter2 = new SubScopesCounter();
var rightReducer = new RightReducer();
eldersEmigrator.OperatorGraph.InitialOperator = selectorProcessor;
selectorProcessor.Operator = eldersSelector;
selectorProcessor.Successor = shiftToRightMigrator;
eldersSelector.AgeParameter.ActualName = AgeParameter.Name;
eldersSelector.AgeLimitsParameter.ActualName = AgeLimitsParameter.Name;
eldersSelector.NumberOfLayersParameter.ActualName = NumberOfLayersParameter.Name;
eldersSelector.LayerParameter.ActualName = "Layer";
eldersSelector.Successor = null;
shiftToRightMigrator.ClockwiseMigrationParameter.Value = new BoolValue(true);
shiftToRightMigrator.Successor = mergingProcessor;
mergingProcessor.Operator = mergingReducer;
mergingReducer.Successor = subScopesCounter1;
subScopesCounter1.ValueParameter.ActualName = CurrentPopulationSizeParameter.Name;
subScopesCounter1.AccumulateParameter.Value = new BoolValue(false);
subScopesCounter1.Successor = currentPopulationSizeComparator;
currentPopulationSizeComparator.LeftSideParameter.ActualName = CurrentPopulationSizeParameter.Name;
currentPopulationSizeComparator.RightSideParameter.ActualName = OneParameter.Name;
currentPopulationSizeComparator.ResultParameter.ActualName = "CurrentPopulationSizeIsGreaterThanOne";
currentPopulationSizeComparator.Comparison = new Comparison(ComparisonType.Greater);
currentPopulationSizeComparator.Successor = currentPopulationSizeIsGreaterThanOne;
currentPopulationSizeIsGreaterThanOne.ConditionParameter.ActualName = "CurrentPopulationSizeIsGreaterThanOne";
currentPopulationSizeIsGreaterThanOne.TrueBranch = rankAndCrowdingSorter;
// We have to sort individuals before reducing, because if we shifted some of them to another layer, it can happen that they are not correctly sorted
rankAndCrowdingSorter.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name;
rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
rankAndCrowdingSorter.Successor = reduceToPopulationSizeBranch;
reduceToPopulationSizeBranch.ConditionParameter.ActualName = ReduceToPopulationSizeParameter.Name;
reduceToPopulationSizeBranch.TrueBranch = countCalculator;
countCalculator.CollectedValues.Add(new LookupParameter <IntValue>(PopulationSizeParameter.Name));
countCalculator.CollectedValues.Add(new LookupParameter <IntValue>(CurrentPopulationSizeParameter.Name));
countCalculator.ExpressionParameter.Value = new StringValue("CurrentPopulationSize PopulationSize CurrentPopulationSize PopulationSize < if toint");
countCalculator.ExpressionResultParameter.ActualName = CurrentPopulationSizeParameter.Name;
countCalculator.Successor = leftSelector;
leftSelector.CopySelected = new BoolValue(false);
leftSelector.NumberOfSelectedSubScopesParameter.ActualName = CurrentPopulationSizeParameter.Name;
leftSelector.Successor = rightReducer;
rightReducer.Successor = subScopesCounter2;
subScopesCounter2.ValueParameter.ActualName = CurrentPopulationSizeParameter.Name;
subScopesCounter2.AccumulateParameter.Value = new BoolValue(false);
return(eldersEmigrator);
}
private void Initialize()
{
#region Create parameters
Parameters.Add(new ValueLookupParameter <IRandom>("GlobalRandom", "A pseudo random number generator."));
Parameters.Add(new ValueLookupParameter <IRandom>("LocalRandom", "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>("NumberOfLayers", "The number of layers."));
Parameters.Add(new ValueLookupParameter <IntValue>("PopulationSize", "The population size."));
Parameters.Add(new LookupParameter <IntValue>("CurrentPopulationSize", "The current size of the population."));
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 ScopeTreeLookupParameter <DoubleValue>("Age", "The age of individuals."));
Parameters.Add(new ValueLookupParameter <IntValue>("AgeGap", "The frequency of reseeding the lowest layer and scaling factor for the age-limits for the layers."));
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 <IntArray>("AgeLimits", "The maximum age an individual is allowed to reach in a certain layer."));
Parameters.Add(new ValueLookupParameter <IntValue>("MatingPoolRange", "The range of sub - populations used for creating a mating pool. (1 = current + previous sub-population)"));
Parameters.Add(new ValueLookupParameter <BoolValue>("ReduceToPopulationSize", "Reduce the CurrentPopulationSize after elder migration to PopulationSize"));
Parameters.Add(new ValueLookupParameter <IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
Parameters.Add(new ValueLookupParameter <VariableCollection>("Results", "The variable collection where results should be stored."));
Parameters.Add(new ValueLookupParameter <IOperator>("Analyzer", "The operator used to analyze each generation."));
Parameters.Add(new ValueLookupParameter <IOperator>("LayerAnalyzer", "The operator used to analyze each layer."));
Parameters.Add(new ValueLookupParameter <IOperator>("FinalAnalyzer", "The operator used to finally analyze the solution (after termination of the algorithm)."));
Parameters.Add(new LookupParameter <IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
Parameters.Add(new ValueLookupParameter <BoolValue>("DominateOnEqualQualities", "Flag which determines whether solutions with equal quality values should be treated as dominated."));
Parameters.Add(new ValueLookupParameter <IOperator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop"));
Parameters.Add(new FixedValueParameter <IntValue>("Zero", "Zero Value.", new IntValue(0)));
Parameters.Add(new FixedValueParameter <IntValue>("One", "1 as a Value.", new IntValue(1)));
#endregion
#region Create operators and operator graph
var variableCreator = new VariableCreator {
Name = "Initialize"
};
var resultsCollector1 = new ResultsCollector();
var initLayerAnalyzerProcessor = new SubScopesProcessor();
var layerVariableCreator = new VariableCreator {
Name = "Initialize Layer"
};
var initLayerAnalyzerPlaceholder = new Placeholder {
Name = "LayerAnalyzer (Placeholder)"
};
var initAnalyzerPlaceholder = new Placeholder {
Name = "Analyzer (Placeholder)"
};
var initFinalAnalyzerPlaceholder = new Placeholder {
Name = "FinalAnalyzer (Placeholder)"
};
var matingPoolCreator = new MatingPoolCreator {
Name = "Create Mating Pools"
};
var matingPoolProcessor = new UniformSubScopesProcessor {
Name = "Process Mating Pools"
};
var initializeLayer = new Assigner {
Name = "Reset LayerEvaluatedSolutions"
};
var mainOperator = new AlpsNsga2MainOperator();
var generationsIncrementer = new IntCounter {
Name = "Increment Generations"
};
var evaluatedSolutionsReducer = new DataReducer {
Name = "Increment EvaluatedSolutions"
};
var eldersEmigrator = CreateEldersEmigrator();
var layerOpener = CreateLayerOpener();
var layerReseeder = CreateReseeder();
var currentPopulationSizeComparator = new Comparator {
Name = "Isn't CurrentPopulationSize 0?"
};
var currentPopulationSizeIsNotZeroBranch = new ConditionalBranch {
Name = "CurrentPopulationSize != Zero"
};
var layerAnalyzerProcessor = new UniformSubScopesProcessor();
var layerAnalyzerPlaceholder = new Placeholder {
Name = "LayerAnalyzer (Placeholder)"
};
var analyzerPlaceholder = new Placeholder {
Name = "Analyzer (Placeholder)"
};
var termination = new TerminationOperator();
var rankAndCrowdingSorter = new RankAndCrowdingSorter();
var mergingReducer = new MergingReducer();
var leftSelector = new LeftSelector();
var rightReducer = new RightReducer();
OperatorGraph.InitialOperator = variableCreator;
variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Generations", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter <IntValue>("OpenLayers", new IntValue(1)));
variableCreator.Successor = initLayerAnalyzerProcessor;
initLayerAnalyzerProcessor.Operators.Add(layerVariableCreator);
initLayerAnalyzerProcessor.Successor = initAnalyzerPlaceholder;
layerVariableCreator.CollectedValues.Add(new ValueParameter <IntValue>("Layer", new IntValue(0)));
layerVariableCreator.CollectedValues.Add(new ValueParameter <ResultCollection>("LayerResults"));
layerVariableCreator.Successor = initLayerAnalyzerPlaceholder;
initLayerAnalyzerPlaceholder.OperatorParameter.ActualName = LayerAnalyzerParameter.Name;
initLayerAnalyzerPlaceholder.Successor = null;
initAnalyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name;
initAnalyzerPlaceholder.Successor = resultsCollector1;
resultsCollector1.CollectedValues.Add(new LookupParameter <IntValue>("Generations"));
resultsCollector1.CollectedValues.Add(new ScopeTreeLookupParameter <ResultCollection>("LayerResults", "Result set for each Layer", "LayerResults"));
resultsCollector1.CollectedValues.Add(new LookupParameter <IntValue>("OpenLayers"));
resultsCollector1.CopyValue = new BoolValue(false);
resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
resultsCollector1.Successor = matingPoolCreator;
matingPoolCreator.MatingPoolRangeParameter.Value = null;
matingPoolCreator.MatingPoolRangeParameter.ActualName = MatingPoolRangeParameter.Name;
matingPoolCreator.Successor = matingPoolProcessor;
matingPoolProcessor.Parallel.Value = true;
matingPoolProcessor.Operator = initializeLayer;
matingPoolProcessor.Successor = generationsIncrementer;
initializeLayer.LeftSideParameter.ActualName = "LayerEvaluatedSolutions";
initializeLayer.RightSideParameter.Value = new IntValue(0);
initializeLayer.Successor = mainOperator;
mainOperator.RandomParameter.ActualName = LocalRandomParameter.Name;
mainOperator.EvaluatorParameter.ActualName = EvaluatorParameter.Name;
mainOperator.EvaluatedSolutionsParameter.ActualName = "LayerEvaluatedSolutions";
mainOperator.QualityParameter.ActualName = QualitiesParameter.Name;
mainOperator.MaximizationParameter.ActualName = MaximizationParameter.Name;
mainOperator.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainOperator.SelectorParameter.ActualName = SelectorParameter.Name;
mainOperator.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainOperator.CrossoverProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
mainOperator.MutatorParameter.ActualName = MutatorParameter.ActualName;
mainOperator.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainOperator.AgeParameter.ActualName = AgeParameter.Name;
mainOperator.AgeInheritanceParameter.ActualName = AgeInheritanceParameter.Name;
mainOperator.AgeIncrementParameter.Value = new DoubleValue(1.0);
mainOperator.Successor = null;
generationsIncrementer.ValueParameter.ActualName = "Generations";
generationsIncrementer.Increment = new IntValue(1);
generationsIncrementer.Successor = evaluatedSolutionsReducer;
evaluatedSolutionsReducer.ParameterToReduce.ActualName = "LayerEvaluatedSolutions";
evaluatedSolutionsReducer.TargetParameter.ActualName = EvaluatedSolutionsParameter.Name;
evaluatedSolutionsReducer.ReductionOperation.Value = new ReductionOperation(ReductionOperations.Sum);
evaluatedSolutionsReducer.TargetOperation.Value = new ReductionOperation(ReductionOperations.Sum);
evaluatedSolutionsReducer.Successor = eldersEmigrator;
eldersEmigrator.Successor = layerOpener;
layerOpener.Successor = layerReseeder;
layerReseeder.Successor = layerAnalyzerProcessor;
// Layer analyzer is only performed if individuals count is not 0
layerAnalyzerProcessor.Operator = currentPopulationSizeComparator;
layerAnalyzerProcessor.Successor = analyzerPlaceholder;
currentPopulationSizeComparator.LeftSideParameter.ActualName = CurrentPopulationSizeParameter.Name;
currentPopulationSizeComparator.RightSideParameter.ActualName = ZeroParameter.Name;
currentPopulationSizeComparator.ResultParameter.ActualName = "CurrentPopulationSizeIsNotZero";
currentPopulationSizeComparator.Comparison = new Comparison(ComparisonType.NotEqual);
currentPopulationSizeComparator.Successor = currentPopulationSizeIsNotZeroBranch;
currentPopulationSizeIsNotZeroBranch.ConditionParameter.ActualName = "CurrentPopulationSizeIsNotZero";
currentPopulationSizeIsNotZeroBranch.TrueBranch = layerAnalyzerPlaceholder;
layerAnalyzerPlaceholder.OperatorParameter.ActualName = LayerAnalyzerParameter.Name;
analyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name;
analyzerPlaceholder.Successor = termination;
termination.TerminatorParameter.ActualName = TerminatorParameter.Name;
termination.ContinueBranch = matingPoolCreator;
termination.TerminateBranch = mergingReducer;
mergingReducer.Successor = rankAndCrowdingSorter;
rankAndCrowdingSorter.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name;
rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
rankAndCrowdingSorter.Successor = leftSelector;
leftSelector.CopySelected = new BoolValue(false);
leftSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name;
leftSelector.Successor = rightReducer;
rightReducer.Successor = initFinalAnalyzerPlaceholder;
initFinalAnalyzerPlaceholder.OperatorParameter.ActualName = FinalAnalyzerParameter.Name;
#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;
}
