public CMAEvolutionStrategy()
: base() {
Parameters.Add(new FixedValueParameter<IntValue>(SeedName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new FixedValueParameter<IntValue>(PopulationSizeName, "λ (lambda) - the size of the offspring population.", new IntValue(20)));
Parameters.Add(new FixedValueParameter<IntValue>(InitialIterationsName, "The number of iterations that should be performed with only axis parallel mutation.", new IntValue(0)));
Parameters.Add(new FixedValueParameter<DoubleArray>(InitialSigmaName, "The initial sigma can be a single value or a value for each dimension. All values need to be > 0.", new DoubleArray(new[] { 0.5 })));
Parameters.Add(new OptionalValueParameter<IntValue>(MuName, "Optional, the mu best offspring that should be considered for update of the new mean and strategy parameters. If not given it will be automatically calculated."));
Parameters.Add(new ConstrainedValueParameter<ICMARecombinator>(CMARecombinatorName, "The operator used to calculate the new mean."));
Parameters.Add(new ConstrainedValueParameter<ICMAManipulator>(CMAMutatorName, "The operator used to manipulate a point."));
Parameters.Add(new ConstrainedValueParameter<ICMAInitializer>(CMAInitializerName, "The operator that initializes the covariance matrix and strategy parameters."));
Parameters.Add(new ConstrainedValueParameter<ICMAUpdater>(CMAUpdaterName, "The operator that updates the covariance matrix and strategy parameters."));
Parameters.Add(new ValueParameter<MultiAnalyzer>(AnalyzerName, "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new FixedValueParameter<IntValue>(MaximumGenerationsName, "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new FixedValueParameter<IntValue>(MaximumEvaluatedSolutionsName, "The maximum number of evaluated solutions that should be computed.", new IntValue(int.MaxValue)));
Parameters.Add(new FixedValueParameter<DoubleValue>(TargetQualityName, "(stopFitness) Surpassing this quality value terminates the algorithm.", new DoubleValue(double.NaN)));
Parameters.Add(new FixedValueParameter<DoubleValue>(MinimumQualityChangeName, "(stopTolFun) If the range of fitness values is less than a certain value the algorithm terminates (set to 0 or positive value to enable).", new DoubleValue(double.NaN)));
Parameters.Add(new FixedValueParameter<DoubleValue>(MinimumQualityHistoryChangeName, "(stopTolFunHist) If the range of fitness values is less than a certain value for a certain time the algorithm terminates (set to 0 or positive to enable).", new DoubleValue(double.NaN)));
Parameters.Add(new FixedValueParameter<DoubleValue>(MinimumStandardDeviationName, "(stopTolXFactor) If the standard deviation falls below a certain value the algorithm terminates (set to 0 or positive to enable).", new DoubleValue(double.NaN)));
Parameters.Add(new FixedValueParameter<DoubleValue>(MaximumStandardDeviationChangeName, "(stopTolUpXFactor) If the standard deviation changes by a value larger than this parameter the algorithm stops (set to a value > 0 to enable).", new DoubleValue(double.NaN)));
var randomCreator = new RandomCreator();
var variableCreator = new VariableCreator();
var resultsCollector = new ResultsCollector();
var cmaInitializer = new Placeholder();
solutionCreator = new Placeholder();
var subScopesCreator = new SubScopesCreator();
var ussp1 = new UniformSubScopesProcessor();
populationSolutionCreator = new Placeholder();
var cmaMutator = new Placeholder();
var ussp2 = new UniformSubScopesProcessor();
evaluator = new Placeholder();
var subScopesCounter = new SubScopesCounter();
sorter = new SubScopesSorter();
var analyzer = new Placeholder();
var cmaRecombinator = new Placeholder();
var generationsCounter = new IntCounter();
var cmaUpdater = new Placeholder();
terminator = new Terminator();
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 = variableCreator;
variableCreator.Name = "Initialize Variables";
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0)));
variableCreator.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("EvaluatedSolutions"));
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = cmaInitializer;
cmaInitializer.Name = "Initialize Strategy Parameters";
cmaInitializer.OperatorParameter.ActualName = CMAInitializerParameter.Name;
cmaInitializer.Successor = subScopesCreator;
subScopesCreator.NumberOfSubScopesParameter.ActualName = PopulationSizeParameter.Name;
subScopesCreator.Successor = ussp1;
ussp1.Name = "Create population";
ussp1.Parallel = new BoolValue(false);
ussp1.Operator = populationSolutionCreator;
ussp1.Successor = solutionCreator;
populationSolutionCreator.Name = "Initialize arx";
// populationSolutionCreator.OperatorParameter will be wired
populationSolutionCreator.Successor = null;
solutionCreator.Name = "Initialize xmean";
// solutionCreator.OperatorParameter will be wired
solutionCreator.Successor = cmaMutator;
cmaMutator.Name = "Sample population";
cmaMutator.OperatorParameter.ActualName = CMAMutatorParameter.Name;
cmaMutator.Successor = ussp2;
ussp2.Name = "Evaluate offspring";
ussp2.Parallel = new BoolValue(true);
ussp2.Operator = evaluator;
ussp2.Successor = subScopesCounter;
evaluator.Name = "Evaluator";
// evaluator.OperatorParameter will be wired
evaluator.Successor = null;
subScopesCounter.Name = "Count EvaluatedSolutions";
subScopesCounter.AccumulateParameter.Value = new BoolValue(true);
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = sorter;
// sorter.ValueParameter will be wired
// sorter.DescendingParameter will be wired
sorter.Successor = analyzer;
analyzer.Name = "Analyzer";
analyzer.OperatorParameter.ActualName = AnalyzerParameter.Name;
analyzer.Successor = cmaRecombinator;
cmaRecombinator.Name = "Create new xmean";
cmaRecombinator.OperatorParameter.ActualName = CMARecombinatorParameter.Name;
cmaRecombinator.Successor = generationsCounter;
generationsCounter.Name = "Generations++";
generationsCounter.IncrementParameter.Value = new IntValue(1);
generationsCounter.ValueParameter.ActualName = "Generations";
generationsCounter.Successor = cmaUpdater;
cmaUpdater.Name = "Update distributions";
cmaUpdater.OperatorParameter.ActualName = CMAUpdaterParameter.Name;
cmaUpdater.Successor = terminator;
terminator.Continue = cmaMutator;
terminator.Terminate = null;
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
cmaAnalyzer = new CMAAnalyzer();
InitializeOperators();
RegisterEventHandlers();
Parameterize();
}
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 OffspringSelectionEvolutionStrategy()
: base() {
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", "µ (mu) - the size of the population.", new IntValue(20)));
Parameters.Add(new ValueParameter<IntValue>("ParentsPerChild", "ρ (rho) - how many parents should be recombined.", new IntValue(1)));
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter<BoolValue>("PlusSelection", "True for plus selection (elitist population), false for comma selection (non-elitist population).", new BoolValue(true)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
Parameters.Add(new OptionalConstrainedValueParameter<ICrossover>("Recombinator", "The operator used to cross solutions."));
Parameters.Add(new ConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterCreator>("StrategyParameterCreator", "The operator that creates the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterCrossover>("StrategyParameterCrossover", "The operator that recombines the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterManipulator>("StrategyParameterManipulator", "The operator that manipulates the strategy parameters."));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
Parameters.Add(new ValueLookupParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(40)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
Parameters.Add(new ValueParameter<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions.", new IntValue(int.MaxValue)));
Parameters.Add(new ValueLookupParameter<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].", new DoubleValue(0.5)));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
UniformSubScopesProcessor strategyVectorProcessor = new UniformSubScopesProcessor();
Placeholder strategyVectorCreator = new Placeholder();
ResultsCollector resultsCollector = new ResultsCollector();
OffspringSelectionEvolutionStrategyMainLoop mainLoop = new OffspringSelectionEvolutionStrategyMainLoop();
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 = strategyVectorProcessor;
strategyVectorProcessor.Operator = strategyVectorCreator;
strategyVectorProcessor.Successor = resultsCollector;
strategyVectorCreator.OperatorParameter.ActualName = "StrategyParameterCreator";
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.PlusSelectionParameter.ActualName = PlusSelectionParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.RecombinatorParameter.ActualName = RecombinatorParameter.Name;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
mainLoop.MaximumEvaluatedSolutionsParameter.ActualName = MaximumEvaluatedSolutionsParameter.Name;
mainLoop.SelectedParentsParameter.ActualName = SelectedParentsParameter.Name;
mainLoop.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
mainLoop.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio";
mainLoop.SelectionPressureParameter.ActualName = "SelectionPressure";
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
selectionPressureAnalyzer = new ValueAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
protected SubScopesCounter(SubScopesCounter original, Cloner cloner)
: base(original, cloner) {
}
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 initializeAgeProsessor = new UniformSubScopesProcessor();
var initializeAge = new VariableCreator() { Name = "Initialize Age" };
var incrEvaluatedSolutionsAfterReseeding = new SubScopesCounter() { Name = "Update EvaluatedSolutions" };
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 = initializeAgeProsessor;
initializeAgeProsessor.Operator = initializeAge;
initializeAgeProsessor.Successor = incrEvaluatedSolutionsAfterReseeding;
initializeAge.CollectedValues.Add(new ValueParameter<DoubleValue>(AgeParameter.Name, new DoubleValue(0)));
incrEvaluatedSolutionsAfterReseeding.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
incrEvaluatedSolutionsAfterReseeding.AccumulateParameter.Value = new BoolValue(true);
return reseeder;
}
private void Initialize() {
#region Create parameters
Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
Parameters.Add(new ValueLookupParameter<IntValue>("SwarmSize", "Size of the particle swarm."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaxIterations", "Maximal number of iterations."));
Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("CurrentInertia", "Inertia weight on a particle's movement (omega)."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("PersonalBestAttraction", "Weight for particle's pull towards its personal best soution (phi_p)."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("NeighborBestAttraction", "Weight for pull towards the neighborhood best solution or global best solution in case of a totally connected topology (phi_g)."));
Parameters.Add(new ValueLookupParameter<IOperator>("ParticleUpdater", "Operator that calculates new position and velocity of a particle"));
Parameters.Add(new ValueLookupParameter<IOperator>("TopologyUpdater", "Updates the neighborhood description vectors"));
Parameters.Add(new ValueLookupParameter<IOperator>("InertiaUpdater", "Updates the omega parameter"));
Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "Evaluates a particle solution."));
Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The variable collection where results should be stored."));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
Parameters.Add(new ValueLookupParameter<ISwarmUpdater>("SwarmUpdater", "The encoding-specific swarm updater."));
#endregion
#region Create operators
ResultsCollector resultsCollector = new ResultsCollector();
Placeholder swarmUpdaterPlaceholer1 = new Placeholder();
Placeholder evaluatorPlaceholder = new Placeholder();
Placeholder analyzerPlaceholder = new Placeholder();
UniformSubScopesProcessor uniformSubScopeProcessor = new UniformSubScopesProcessor();
Placeholder particleUpdaterPlaceholder = new Placeholder();
Placeholder topologyUpdaterPlaceholder = new Placeholder();
UniformSubScopesProcessor evaluationProcessor = new UniformSubScopesProcessor();
Placeholder swarmUpdater = new Placeholder();
IntCounter iterationsCounter = new IntCounter();
Comparator iterationsComparator = new Comparator();
ConditionalBranch conditionalBranch = new ConditionalBranch();
Placeholder inertiaUpdaterPlaceholder = new Placeholder();
SubScopesCounter subScopesCounter = new SubScopesCounter();
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Iterations"));
resultsCollector.CollectedValues.Add(new LookupParameter<DoubleValue>("CurrentInertia"));
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = swarmUpdaterPlaceholer1;
swarmUpdaterPlaceholer1.Name = "(Swarm Updater)";
swarmUpdaterPlaceholer1.OperatorParameter.ActualName = SwarmUpdaterParameter.ActualName;
swarmUpdaterPlaceholer1.Successor = analyzerPlaceholder;
analyzerPlaceholder.Name = "(Analyzer)";
analyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name;
analyzerPlaceholder.Successor = uniformSubScopeProcessor;
uniformSubScopeProcessor.Operator = particleUpdaterPlaceholder;
uniformSubScopeProcessor.Successor = evaluationProcessor;
particleUpdaterPlaceholder.Name = "(ParticleUpdater)";
particleUpdaterPlaceholder.OperatorParameter.ActualName = ParticleUpdaterParameter.Name;
evaluationProcessor.Parallel = new BoolValue(true);
evaluationProcessor.Operator = evaluatorPlaceholder;
evaluationProcessor.Successor = subScopesCounter;
evaluatorPlaceholder.Name = "(Evaluator)";
evaluatorPlaceholder.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter.Name = "Increment EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
subScopesCounter.Successor = topologyUpdaterPlaceholder;
topologyUpdaterPlaceholder.Name = "(TopologyUpdater)";
topologyUpdaterPlaceholder.OperatorParameter.ActualName = TopologyUpdaterParameter.Name;
topologyUpdaterPlaceholder.Successor = swarmUpdater;
swarmUpdater.Name = "(Swarm Updater)";
swarmUpdater.OperatorParameter.ActualName = SwarmUpdaterParameter.ActualName;
swarmUpdater.Successor = inertiaUpdaterPlaceholder;
inertiaUpdaterPlaceholder.Name = "(Inertia Updater)";
inertiaUpdaterPlaceholder.OperatorParameter.ActualName = InertiaUpdaterParameter.ActualName;
inertiaUpdaterPlaceholder.Successor = iterationsCounter;
iterationsCounter.Name = "Iterations++";
iterationsCounter.ValueParameter.ActualName = "Iterations";
iterationsCounter.Successor = iterationsComparator;
iterationsComparator.LeftSideParameter.ActualName = "Iterations";
iterationsComparator.Comparison = new Comparison(ComparisonType.Less);
iterationsComparator.RightSideParameter.ActualName = "MaxIterations";
iterationsComparator.ResultParameter.ActualName = "ContinueIteration";
iterationsComparator.Successor = conditionalBranch;
conditionalBranch.Name = "ContinueIteration?";
conditionalBranch.ConditionParameter.ActualName = "ContinueIteration";
conditionalBranch.TrueBranch = analyzerPlaceholder;
#endregion
}
public GeneticAlgorithm()
: base() {
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 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<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
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)));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
GeneticAlgorithmMainLoop mainLoop = new GeneticAlgorithmMainLoop();
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 = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.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.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
SelectorParameter.ValidValues.Add(selector);
ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
ParameterizeSelectors();
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
private CombinedOperator CreateEldersEmigrator() {
var eldersEmigrator = new CombinedOperator() { Name = "Emigrate Elders" };
var selectorProsessor = new UniformSubScopesProcessor();
var eldersSelector = new EldersSelector();
var shiftToRightMigrator = new UnidirectionalRingMigrator() { Name = "Shift elders to next layer" };
var mergingProsessor = new UniformSubScopesProcessor();
var mergingReducer = new MergingReducer();
var subScopesCounter = new SubScopesCounter();
var reduceToPopulationSizeBranch = new ConditionalBranch() { Name = "ReduceToPopulationSize?" };
var countCalculator = new ExpressionCalculator() { Name = "CurrentPopulationSize = Min(CurrentPopulationSize, PopulationSize)" };
var bestSelector = new BestSelector();
var rightReducer = new RightReducer();
eldersEmigrator.OperatorGraph.InitialOperator = selectorProsessor;
selectorProsessor.Operator = eldersSelector;
selectorProsessor.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 = mergingProsessor;
mergingProsessor.Operator = mergingReducer;
mergingReducer.Successor = subScopesCounter;
subScopesCounter.ValueParameter.ActualName = CurrentPopulationSizeParameter.Name;
subScopesCounter.AccumulateParameter.Value = new BoolValue(false);
subScopesCounter.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 = bestSelector;
bestSelector.NumberOfSelectedSubScopesParameter.ActualName = CurrentPopulationSizeParameter.Name;
bestSelector.CopySelected = new BoolValue(false);
bestSelector.Successor = rightReducer;
return eldersEmigrator;
}
public EvolutionStrategy()
: base() {
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", "µ (mu) - the size of the population.", new IntValue(20)));
Parameters.Add(new ValueParameter<IntValue>("ParentsPerChild", "ρ (rho) - how many parents should be recombined.", new IntValue(1)));
Parameters.Add(new ValueParameter<IntValue>("Children", "λ (lambda) - the size of the offspring population.", new IntValue(100)));
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueParameter<BoolValue>("PlusSelection", "True for plus selection (elitist population), false for comma selection (non-elitist population).", new BoolValue(true)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
Parameters.Add(new OptionalConstrainedValueParameter<ICrossover>("Recombinator", "The operator used to cross solutions."));
Parameters.Add(new ConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterCreator>("StrategyParameterCreator", "The operator that creates the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterCrossover>("StrategyParameterCrossover", "The operator that recombines the strategy parameters."));
Parameters.Add(new OptionalConstrainedValueParameter<IStrategyParameterManipulator>("StrategyParameterManipulator", "The operator that manipulates the strategy parameters."));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
UniformSubScopesProcessor strategyVectorProcessor = new UniformSubScopesProcessor();
Placeholder strategyVectorCreator = new Placeholder();
ResultsCollector resultsCollector = new ResultsCollector();
EvolutionStrategyMainLoop mainLoop = new EvolutionStrategyMainLoop();
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 = strategyVectorProcessor;
strategyVectorProcessor.Operator = strategyVectorCreator;
strategyVectorProcessor.Successor = resultsCollector;
strategyVectorCreator.OperatorParameter.ActualName = "StrategyParameterCreator";
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
mainLoop.ChildrenParameter.ActualName = ChildrenParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.PlusSelectionParameter.ActualName = PlusSelectionParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.RecombinatorParameter.ActualName = RecombinatorParameter.Name;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
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 LookupParameter<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 LookupParameter<DoubleValue>("MoveQuality", "The value which represents the quality of a move."));
Parameters.Add(new LookupParameter<DoubleValue>("Temperature", "The current temperature."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("StartTemperature", "The initial temperature."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("EndTemperature", "The end temperature."));
Parameters.Add(new ValueLookupParameter<IntValue>("InnerIterations", "The amount of inner iterations (number of moves before temperature is adjusted again)."));
Parameters.Add(new LookupParameter<IntValue>("Iterations", "The number of iterations."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "The maximum number of iterations which should be processed."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveGenerator", "The operator that generates the moves."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveEvaluator", "The operator that evaluates a move."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveMaker", "The operator that performs a move and updates the quality."));
Parameters.Add(new ValueLookupParameter<IOperator>("AnnealingOperator", "The operator that modifies the temperature."));
Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedMoves", "The number of evaluated moves."));
#endregion
#region Create operators
Assigner temperatureInitializer = new Assigner();
ResultsCollector resultsCollector1 = new ResultsCollector();
SubScopesProcessor subScopesProcessor0 = new SubScopesProcessor();
Placeholder analyzer1 = new Placeholder();
SubScopesProcessor sssp = new SubScopesProcessor();
ResultsCollector resultsCollector = new ResultsCollector();
Placeholder annealingOperator = new Placeholder();
UniformSubScopesProcessor mainProcessor = new UniformSubScopesProcessor();
Placeholder moveGenerator = new Placeholder();
UniformSubScopesProcessor moveEvaluationProcessor = new UniformSubScopesProcessor();
Placeholder moveEvaluator = new Placeholder();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ProbabilisticQualityComparator qualityComparator = new ProbabilisticQualityComparator();
ConditionalBranch improvesQualityBranch = new ConditionalBranch();
Placeholder moveMaker = new Placeholder();
SubScopesRemover subScopesRemover = new SubScopesRemover();
IntCounter iterationsCounter = new IntCounter();
Comparator iterationsComparator = new Comparator();
SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
Placeholder analyzer2 = new Placeholder();
ConditionalBranch iterationsTermination = new ConditionalBranch();
temperatureInitializer.LeftSideParameter.ActualName = TemperatureParameter.ActualName;
temperatureInitializer.RightSideParameter.ActualName = StartTemperatureParameter.Name;
resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>(IterationsParameter.Name));
resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
analyzer1.Name = "Analyzer (placeholder)";
analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
annealingOperator.Name = "Annealing operator (placeholder)";
annealingOperator.OperatorParameter.ActualName = AnnealingOperatorParameter.Name;
moveGenerator.Name = "Move generator (placeholder)";
moveGenerator.OperatorParameter.ActualName = MoveGeneratorParameter.Name;
moveEvaluator.Name = "Move evaluator (placeholder)";
moveEvaluator.OperatorParameter.ActualName = MoveEvaluatorParameter.Name;
subScopesCounter.Name = "Increment EvaluatedMoves";
subScopesCounter.ValueParameter.ActualName = EvaluatedMovesParameter.Name;
qualityComparator.LeftSideParameter.ActualName = MoveQualityParameter.Name;
qualityComparator.RightSideParameter.ActualName = QualityParameter.Name;
qualityComparator.ResultParameter.ActualName = "IsBetter";
qualityComparator.DampeningParameter.ActualName = "Temperature";
improvesQualityBranch.ConditionParameter.ActualName = "IsBetter";
moveMaker.Name = "Move maker (placeholder)";
moveMaker.OperatorParameter.ActualName = MoveMakerParameter.Name;
subScopesRemover.RemoveAllSubScopes = true;
iterationsCounter.Name = "Increment Iterations";
iterationsCounter.Increment = new IntValue(1);
iterationsCounter.ValueParameter.ActualName = IterationsParameter.Name;
iterationsComparator.Name = "Iterations >= MaximumIterations";
iterationsComparator.LeftSideParameter.ActualName = IterationsParameter.Name;
iterationsComparator.RightSideParameter.ActualName = MaximumIterationsParameter.Name;
iterationsComparator.ResultParameter.ActualName = "Terminate";
iterationsComparator.Comparison.Value = ComparisonType.GreaterOrEqual;
analyzer2.Name = "Analyzer (placeholder)";
analyzer2.OperatorParameter.ActualName = AnalyzerParameter.Name;
iterationsTermination.Name = "Iterations termination condition";
iterationsTermination.ConditionParameter.ActualName = "Terminate";
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = temperatureInitializer;
temperatureInitializer.Successor = resultsCollector1;
resultsCollector1.Successor = subScopesProcessor0;
subScopesProcessor0.Operators.Add(analyzer1);
subScopesProcessor0.Successor = sssp;
analyzer1.Successor = null;
sssp.Operators.Add(resultsCollector);
sssp.Successor = annealingOperator;
resultsCollector.Successor = null;
annealingOperator.Successor = mainProcessor;
mainProcessor.Operator = moveGenerator;
mainProcessor.Successor = iterationsCounter;
moveGenerator.Successor = moveEvaluationProcessor;
moveEvaluationProcessor.Operator = moveEvaluator;
moveEvaluationProcessor.Successor = subScopesCounter;
moveEvaluator.Successor = qualityComparator;
qualityComparator.Successor = improvesQualityBranch;
improvesQualityBranch.TrueBranch = moveMaker;
improvesQualityBranch.FalseBranch = null;
improvesQualityBranch.Successor = null;
moveMaker.Successor = null;
subScopesCounter.Successor = subScopesRemover;
subScopesRemover.Successor = null;
iterationsCounter.Successor = iterationsComparator;
iterationsComparator.Successor = subScopesProcessor1;
subScopesProcessor1.Operators.Add(analyzer2);
subScopesProcessor1.Successor = iterationsTermination;
iterationsTermination.TrueBranch = null;
iterationsTermination.FalseBranch = annealingOperator;
#endregion
}
public IslandGeneticAlgorithm()
: base() {
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>("NumberOfIslands", "The number of islands.", new IntValue(5)));
Parameters.Add(new ValueParameter<IntValue>("MigrationInterval", "The number of generations that should pass between migration phases.", new IntValue(20)));
Parameters.Add(new ValueParameter<PercentValue>("MigrationRate", "The proportion of individuals that should migrate between the islands.", new PercentValue(0.15)));
Parameters.Add(new ConstrainedValueParameter<IMigrator>("Migrator", "The migration strategy."));
Parameters.Add(new ConstrainedValueParameter<ISelector>("EmigrantsSelector", "Selects the individuals that will be migrated."));
Parameters.Add(new ConstrainedValueParameter<IReplacer>("ImmigrationReplacer", "Selects the population from the unification of the original population and the immigrants."));
Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations that should be processed.", new IntValue(1000)));
Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
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<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze the islands.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter<MultiAnalyzer>("IslandAnalyzer", "The operator used to analyze each island.", new MultiAnalyzer()));
RandomCreator randomCreator = new RandomCreator();
UniformSubScopesProcessor ussp0 = new UniformSubScopesProcessor();
LocalRandomCreator localRandomCreator = new LocalRandomCreator();
RandomCreator globalRandomResetter = new RandomCreator();
SubScopesCreator populationCreator = new SubScopesCreator();
UniformSubScopesProcessor ussp1 = new UniformSubScopesProcessor();
SolutionsCreator solutionsCreator = new SolutionsCreator();
VariableCreator variableCreator = new VariableCreator();
UniformSubScopesProcessor ussp2 = new UniformSubScopesProcessor();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
IslandGeneticAlgorithmMainLoop mainLoop = new IslandGeneticAlgorithmMainLoop();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.RandomParameter.ActualName = "GlobalRandom";
randomCreator.SeedParameter.ActualName = SeedParameter.Name;
randomCreator.SeedParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.Successor = populationCreator;
populationCreator.NumberOfSubScopesParameter.ActualName = NumberOfIslandsParameter.Name;
populationCreator.Successor = ussp0;
ussp0.Operator = localRandomCreator;
ussp0.Successor = globalRandomResetter;
// BackwardsCompatibility3.3
// the global random is resetted to ensure the same algorithm results
#region Backwards compatible code, remove global random resetter with 3.4 and rewire the operator graph
globalRandomResetter.RandomParameter.ActualName = "GlobalRandom";
globalRandomResetter.SeedParameter.ActualName = SeedParameter.Name;
globalRandomResetter.SeedParameter.Value = null;
globalRandomResetter.SetSeedRandomlyParameter.Value = new BoolValue(false);
globalRandomResetter.Successor = ussp1;
#endregion
ussp1.Operator = solutionsCreator;
ussp1.Successor = variableCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
//don't create solutions in parallel because the hive engine would distribute these tasks
solutionsCreator.ParallelParameter.Value = new BoolValue(false);
solutionsCreator.Successor = null;
variableCreator.Name = "Initialize EvaluatedSolutions";
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("EvaluatedSolutions", new IntValue()));
variableCreator.Successor = ussp2;
ussp2.Operator = subScopesCounter;
ussp2.Successor = resultsCollector;
subScopesCounter.Name = "Count EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = null;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.EmigrantsSelectorParameter.ActualName = EmigrantsSelectorParameter.Name;
mainLoop.ImmigrationReplacerParameter.ActualName = ImmigrationReplacerParameter.Name;
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.MigrationIntervalParameter.ActualName = MigrationIntervalParameter.Name;
mainLoop.MigrationRateParameter.ActualName = MigrationRateParameter.Name;
mainLoop.MigratorParameter.ActualName = MigratorParameter.Name;
mainLoop.NumberOfIslandsParameter.ActualName = NumberOfIslandsParameter.Name;
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.RandomParameter.ActualName = randomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.IslandAnalyzerParameter.ActualName = IslandAnalyzerParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.Successor = null;
foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
SelectorParameter.ValidValues.Add(selector);
ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
EmigrantsSelectorParameter.ValidValues.Add(selector);
foreach (IReplacer replacer in ApplicationManager.Manager.GetInstances<IReplacer>().OrderBy(x => x.Name))
ImmigrationReplacerParameter.ValidValues.Add(replacer);
ParameterizeSelectors();
foreach (IMigrator migrator in ApplicationManager.Manager.GetInstances<IMigrator>().OrderBy(x => x.Name)) {
// BackwardsCompatibility3.3
// Set the migration direction to counterclockwise
var unidirectionalRing = migrator as UnidirectionalRingMigrator;
if (unidirectionalRing != null) unidirectionalRing.ClockwiseMigrationParameter.Value = new BoolValue(false);
MigratorParameter.ValidValues.Add(migrator);
}
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
islandQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
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 LookupParameter<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 LookupParameter<DoubleValue>("MoveQuality", "The value which represents the quality of a move."));
Parameters.Add(new LookupParameter<BoolValue>("MoveTabu", "The value that indicates if a move is tabu or not."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "The maximum number of generations which should be processed."));
Parameters.Add(new ValueLookupParameter<IntValue>("TabuTenure", "The length of the tabu list, and also means the number of iterations a move is kept tabu"));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveGenerator", "The operator that generates the moves."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveMaker", "The operator that performs a move and updates the quality."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveEvaluator", "The operator that evaluates a move."));
Parameters.Add(new ValueLookupParameter<IOperator>("TabuChecker", "The operator that checks whether a move is tabu."));
Parameters.Add(new ValueLookupParameter<IOperator>("TabuMaker", "The operator that declares a move tabu."));
Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze the solution and moves."));
Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedMoves", "The number of evaluated moves."));
#endregion
#region Create operators
VariableCreator variableCreator = new VariableCreator();
SubScopesProcessor subScopesProcessor0 = new SubScopesProcessor();
Assigner bestQualityInitializer = new Assigner();
Placeholder analyzer1 = new Placeholder();
ResultsCollector resultsCollector1 = new ResultsCollector();
SubScopesProcessor solutionProcessor = new SubScopesProcessor();
Placeholder moveGenerator = new Placeholder();
UniformSubScopesProcessor moveEvaluationProcessor = new UniformSubScopesProcessor();
Placeholder moveEvaluator = new Placeholder();
Placeholder tabuChecker = new Placeholder();
SubScopesCounter subScopesCounter = new SubScopesCounter();
SubScopesSorter moveQualitySorter = new SubScopesSorter();
TabuSelector tabuSelector = new TabuSelector();
ConditionalBranch emptyNeighborhoodBranch1 = new ConditionalBranch();
SubScopesProcessor moveMakingProcessor = new SubScopesProcessor();
UniformSubScopesProcessor selectedMoveMakingProcesor = new UniformSubScopesProcessor();
Placeholder tabuMaker = new Placeholder();
Placeholder moveMaker = new Placeholder();
MergingReducer mergingReducer = new MergingReducer();
Placeholder analyzer2 = new Placeholder();
SubScopesRemover subScopesRemover = new SubScopesRemover();
ConditionalBranch emptyNeighborhoodBranch2 = new ConditionalBranch();
BestQualityMemorizer bestQualityUpdater = new BestQualityMemorizer();
IntCounter iterationsCounter = new IntCounter();
Comparator iterationsComparator = new Comparator();
ConditionalBranch iterationsTermination = new ConditionalBranch();
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Iterations", new IntValue(0))); // Class TabuSearch expects this to be called Iterations
variableCreator.CollectedValues.Add(new ValueParameter<BoolValue>("EmptyNeighborhood", new BoolValue(false)));
variableCreator.CollectedValues.Add(new ValueParameter<ItemList<IItem>>("TabuList", new ItemList<IItem>()));
variableCreator.CollectedValues.Add(new ValueParameter<VariableCollection>("Memories", new VariableCollection()));
variableCreator.CollectedValues.Add(new ValueParameter<DoubleValue>("BestQuality", new DoubleValue(0)));
bestQualityInitializer.Name = "Initialize BestQuality";
bestQualityInitializer.LeftSideParameter.ActualName = "BestQuality";
bestQualityInitializer.RightSideParameter.ActualName = QualityParameter.Name;
analyzer1.Name = "Analyzer (placeholder)";
analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
resultsCollector1.CopyValue = new BoolValue(false);
resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Iterations"));
resultsCollector1.CollectedValues.Add(new LookupParameter<DoubleValue>("Best Quality", null, "BestQuality"));
resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
moveGenerator.Name = "MoveGenerator (placeholder)";
moveGenerator.OperatorParameter.ActualName = MoveGeneratorParameter.Name;
moveEvaluationProcessor.Parallel = new BoolValue(true);
moveEvaluator.Name = "MoveEvaluator (placeholder)";
moveEvaluator.OperatorParameter.ActualName = MoveEvaluatorParameter.Name;
tabuChecker.Name = "TabuChecker (placeholder)";
tabuChecker.OperatorParameter.ActualName = TabuCheckerParameter.Name;
subScopesCounter.Name = "Increment EvaluatedMoves";
subScopesCounter.ValueParameter.ActualName = EvaluatedMovesParameter.Name;
moveQualitySorter.DescendingParameter.ActualName = MaximizationParameter.Name;
moveQualitySorter.ValueParameter.ActualName = MoveQualityParameter.Name;
tabuSelector.AspirationParameter.Value = new BoolValue(true);
tabuSelector.BestQualityParameter.ActualName = "BestQuality";
tabuSelector.CopySelected = new BoolValue(false);
tabuSelector.EmptyNeighborhoodParameter.ActualName = "EmptyNeighborhood";
tabuSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
tabuSelector.MoveQualityParameter.ActualName = MoveQualityParameter.Name;
tabuSelector.MoveTabuParameter.ActualName = MoveTabuParameter.Name;
moveMakingProcessor.Name = "MoveMaking processor (UniformSubScopesProcessor)";
emptyNeighborhoodBranch1.Name = "Neighborhood empty?";
emptyNeighborhoodBranch1.ConditionParameter.ActualName = "EmptyNeighborhood";
tabuMaker.Name = "TabuMaker (placeholder)";
tabuMaker.OperatorParameter.ActualName = TabuMakerParameter.Name;
moveMaker.Name = "MoveMaker (placeholder)";
moveMaker.OperatorParameter.ActualName = MoveMakerParameter.Name;
analyzer2.Name = "Analyzer (placeholder)";
analyzer2.OperatorParameter.ActualName = AnalyzerParameter.Name;
subScopesRemover.RemoveAllSubScopes = true;
bestQualityUpdater.Name = "Update BestQuality";
bestQualityUpdater.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestQualityUpdater.QualityParameter.ActualName = QualityParameter.Name;
bestQualityUpdater.BestQualityParameter.ActualName = "BestQuality";
iterationsCounter.Name = "Iterations Counter";
iterationsCounter.Increment = new IntValue(1);
iterationsCounter.ValueParameter.ActualName = "Iterations";
iterationsComparator.Name = "Iterations >= MaximumIterations";
iterationsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
iterationsComparator.LeftSideParameter.ActualName = "Iterations";
iterationsComparator.RightSideParameter.ActualName = MaximumIterationsParameter.Name;
iterationsComparator.ResultParameter.ActualName = "Terminate";
emptyNeighborhoodBranch2.Name = "Neighborhood empty?";
emptyNeighborhoodBranch2.ConditionParameter.ActualName = "EmptyNeighborhood";
iterationsTermination.Name = "Iterations Termination Condition";
iterationsTermination.ConditionParameter.ActualName = "Terminate";
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = variableCreator;
variableCreator.Successor = subScopesProcessor0;
subScopesProcessor0.Operators.Add(bestQualityInitializer);
subScopesProcessor0.Successor = resultsCollector1;
bestQualityInitializer.Successor = analyzer1;
analyzer1.Successor = null;
resultsCollector1.Successor = solutionProcessor;
solutionProcessor.Operators.Add(moveGenerator);
solutionProcessor.Successor = iterationsCounter;
moveGenerator.Successor = moveEvaluationProcessor;
moveEvaluationProcessor.Operator = moveEvaluator;
moveEvaluationProcessor.Successor = subScopesCounter;
moveEvaluator.Successor = tabuChecker;
tabuChecker.Successor = null;
subScopesCounter.Successor = moveQualitySorter;
moveQualitySorter.Successor = tabuSelector;
tabuSelector.Successor = emptyNeighborhoodBranch1;
emptyNeighborhoodBranch1.FalseBranch = moveMakingProcessor;
emptyNeighborhoodBranch1.TrueBranch = null;
emptyNeighborhoodBranch1.Successor = subScopesRemover;
moveMakingProcessor.Operators.Add(new EmptyOperator());
moveMakingProcessor.Operators.Add(selectedMoveMakingProcesor);
moveMakingProcessor.Successor = mergingReducer;
selectedMoveMakingProcesor.Operator = tabuMaker;
selectedMoveMakingProcesor.Successor = null;
tabuMaker.Successor = moveMaker;
moveMaker.Successor = null;
mergingReducer.Successor = analyzer2;
analyzer2.Successor = null;
subScopesRemover.Successor = null;
iterationsCounter.Successor = iterationsComparator;
iterationsComparator.Successor = emptyNeighborhoodBranch2;
emptyNeighborhoodBranch2.TrueBranch = null;
emptyNeighborhoodBranch2.FalseBranch = iterationsTermination;
emptyNeighborhoodBranch2.Successor = null;
iterationsTermination.TrueBranch = null;
iterationsTermination.FalseBranch = solutionProcessor;
#endregion
}
private void Initialize() {
#region Create parameters
Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
Parameters.Add(new ValueLookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "The size of the population."));
Parameters.Add(new ValueLookupParameter<IntValue>("MinimumPopulationSize", "The minimum size of the population of solutions."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumPopulationSize", "The maximum size of the population of solutions."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactor", "The comparison factor."));
Parameters.Add(new ValueLookupParameter<IntValue>("Effort", "The maximum number of offspring created in each generation."));
Parameters.Add(new ValueLookupParameter<IntValue>("BatchSize", "The number of children that should be created during one iteration of the offspring creation process."));
Parameters.Add(new ValueLookupParameter<ISolutionSimilarityCalculator>("SimilarityCalculator", "The operator used to calculate the similarity between two solutions."));
Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the genetic algorithm should be applied."));
#endregion
#region Create operators
VariableCreator variableCreator = new VariableCreator();
Assigner assigner1 = new Assigner();
ResultsCollector resultsCollector = new ResultsCollector();
Placeholder analyzer1 = new Placeholder();
Placeholder selector = new Placeholder();
SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
ChildrenCreator childrenCreator = new ChildrenCreator();
UniformSubScopesProcessor uniformSubScopesProcessor = new UniformSubScopesProcessor();
Placeholder crossover = new Placeholder();
StochasticBranch stochasticBranch = new StochasticBranch();
Placeholder mutator = new Placeholder();
Placeholder evaluator = new Placeholder();
WeightedParentsQualityComparator weightedParentsQualityComparator = new WeightedParentsQualityComparator();
SubScopesRemover subScopesRemover = new SubScopesRemover();
IntCounter intCounter1 = new IntCounter();
IntCounter intCounter2 = new IntCounter();
ConditionalSelector conditionalSelector = new ConditionalSelector();
RightReducer rightReducer1 = new RightReducer();
DuplicatesSelector duplicateSelector = new DuplicatesSelector();
LeftReducer leftReducer1 = new LeftReducer();
ProgressiveOffspringPreserver progressiveOffspringSelector = new ProgressiveOffspringPreserver();
SubScopesCounter subScopesCounter2 = new SubScopesCounter();
ExpressionCalculator calculator1 = new ExpressionCalculator();
ConditionalBranch conditionalBranch1 = new ConditionalBranch();
Comparator comparator1 = new Comparator();
ConditionalBranch conditionalBranch2 = new ConditionalBranch();
LeftReducer leftReducer2 = new LeftReducer();
SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
BestSelector bestSelector = new BestSelector();
RightReducer rightReducer2 = new RightReducer();
ScopeCleaner scopeCleaner = new ScopeCleaner();
ScopeRestorer scopeRestorer = new ScopeRestorer();
MergingReducer mergingReducer = new MergingReducer();
IntCounter intCounter3 = new IntCounter();
SubScopesCounter subScopesCounter3 = new SubScopesCounter();
ExpressionCalculator calculator2 = new ExpressionCalculator();
Comparator comparator2 = new Comparator();
ConditionalBranch conditionalBranch3 = new ConditionalBranch();
Placeholder analyzer2 = new Placeholder();
Comparator comparator3 = new Comparator();
ConditionalBranch conditionalBranch4 = new ConditionalBranch();
Comparator comparator4 = new Comparator();
ConditionalBranch conditionalBranch5 = new ConditionalBranch();
Assigner assigner3 = new Assigner();
Assigner assigner4 = new Assigner();
Assigner assigner5 = new Assigner();
ConditionalBranch reevaluateElitesBranch = new ConditionalBranch();
UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
Placeholder evaluator2 = new Placeholder();
SubScopesCounter subScopesCounter4 = new SubScopesCounter();
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class RAPGA expects this to be called Generations
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("CurrentPopulationSize", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("NumberOfCreatedOffspring", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("NumberOfSuccessfulOffspring", new IntValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<ScopeList>("OffspringList", new ScopeList()));
assigner1.Name = "Initialize CurrentPopulationSize";
assigner1.LeftSideParameter.ActualName = "CurrentPopulationSize";
assigner1.RightSideParameter.ActualName = PopulationSizeParameter.Name;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("CurrentPopulationSize"));
resultsCollector.ResultsParameter.ActualName = "Results";
analyzer1.Name = "Analyzer";
analyzer1.OperatorParameter.ActualName = "Analyzer";
selector.Name = "Selector";
selector.OperatorParameter.ActualName = "Selector";
childrenCreator.ParentsPerChild = new IntValue(2);
uniformSubScopesProcessor.Parallel.Value = true;
crossover.Name = "Crossover";
crossover.OperatorParameter.ActualName = "Crossover";
stochasticBranch.ProbabilityParameter.ActualName = "MutationProbability";
stochasticBranch.RandomParameter.ActualName = "Random";
mutator.Name = "Mutator";
mutator.OperatorParameter.ActualName = "Mutator";
evaluator.Name = "Evaluator";
evaluator.OperatorParameter.ActualName = "Evaluator";
weightedParentsQualityComparator.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
weightedParentsQualityComparator.LeftSideParameter.ActualName = QualityParameter.Name;
weightedParentsQualityComparator.MaximizationParameter.ActualName = MaximizationParameter.Name;
weightedParentsQualityComparator.RightSideParameter.ActualName = QualityParameter.Name;
weightedParentsQualityComparator.ResultParameter.ActualName = "SuccessfulOffspring";
subScopesRemover.RemoveAllSubScopes = true;
intCounter1.Name = "Increment NumberOfCreatedOffspring";
intCounter1.ValueParameter.ActualName = "NumberOfCreatedOffspring";
intCounter1.Increment = null;
intCounter1.IncrementParameter.ActualName = BatchSizeParameter.Name;
intCounter2.Name = "Increment EvaluatedSolutions";
intCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
intCounter2.Increment = null;
intCounter2.IncrementParameter.ActualName = BatchSizeParameter.Name;
conditionalSelector.ConditionParameter.ActualName = "SuccessfulOffspring";
conditionalSelector.ConditionParameter.Depth = 1;
conditionalSelector.CopySelected.Value = false;
duplicateSelector.CopySelected.Value = false;
progressiveOffspringSelector.OffspringListParameter.ActualName = "OffspringList";
progressiveOffspringSelector.ElitesParameter.ActualName = ElitesParameter.Name;
progressiveOffspringSelector.MaximumPopulationSizeParameter.ActualName = MaximumPopulationSizeParameter.Name;
subScopesCounter2.Name = "Count Successful Offspring";
subScopesCounter2.ValueParameter.ActualName = "NumberOfSuccessfulOffspring";
calculator1.Name = "NumberOfSuccessfulOffspring == MaximumPopulationSize - Elites";
calculator1.CollectedValues.Add(new ValueLookupParameter<IntValue>("NumberOfSuccessfulOffspring"));
calculator1.CollectedValues.Add(new ValueLookupParameter<IntValue>("MaximumPopulationSize"));
calculator1.CollectedValues.Add(new ValueLookupParameter<IntValue>("Elites"));
calculator1.ExpressionParameter.Value = new StringValue("NumberOfSuccessfulOffspring MaximumPopulationSize Elites - ==");
calculator1.ExpressionResultParameter.ActualName = "Break";
conditionalBranch1.Name = "Break?";
conditionalBranch1.ConditionParameter.ActualName = "Break";
comparator1.Name = "NumberOfCreatedOffspring >= Effort";
comparator1.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
comparator1.LeftSideParameter.ActualName = "NumberOfCreatedOffspring";
comparator1.RightSideParameter.ActualName = EffortParameter.Name;
comparator1.ResultParameter.ActualName = "Break";
conditionalBranch2.Name = "Break?";
conditionalBranch2.ConditionParameter.ActualName = "Break";
bestSelector.CopySelected = new BoolValue(false);
bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestSelector.NumberOfSelectedSubScopesParameter.ActualName = "Elites";
bestSelector.QualityParameter.ActualName = QualityParameter.Name;
intCounter3.Name = "Increment Generations";
intCounter3.Increment = new IntValue(1);
intCounter3.ValueParameter.ActualName = "Generations";
subScopesCounter3.Name = "Update CurrentPopulationSize";
subScopesCounter3.ValueParameter.ActualName = "CurrentPopulationSize";
subScopesCounter3.AccumulateParameter.Value = new BoolValue(false);
calculator2.Name = "Evaluate ActualSelectionPressure";
calculator2.CollectedValues.Add(new ValueLookupParameter<IntValue>("NumberOfCreatedOffspring"));
calculator2.CollectedValues.Add(new ValueLookupParameter<IntValue>("Elites"));
calculator2.CollectedValues.Add(new ValueLookupParameter<IntValue>("CurrentPopulationSize"));
calculator2.ExpressionParameter.Value = new StringValue("NumberOfCreatedOffspring Elites + CurrentPopulationSize /");
calculator2.ExpressionResultParameter.ActualName = "ActualSelectionPressure";
comparator2.Name = "CurrentPopulationSize < 1";
comparator2.Comparison = new Comparison(ComparisonType.Less);
comparator2.LeftSideParameter.ActualName = "CurrentPopulationSize";
comparator2.RightSideParameter.Value = new IntValue(1);
comparator2.ResultParameter.ActualName = "Terminate";
conditionalBranch3.Name = "Terminate?";
conditionalBranch3.ConditionParameter.ActualName = "Terminate";
analyzer2.Name = "Analyzer";
analyzer2.OperatorParameter.ActualName = "Analyzer";
comparator3.Name = "Generations >= MaximumGenerations";
comparator3.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
comparator3.LeftSideParameter.ActualName = "Generations";
comparator3.ResultParameter.ActualName = "Terminate";
comparator3.RightSideParameter.ActualName = MaximumGenerationsParameter.Name;
conditionalBranch4.Name = "Terminate?";
conditionalBranch4.ConditionParameter.ActualName = "Terminate";
comparator4.Name = "CurrentPopulationSize < MinimumPopulationSize";
comparator4.Comparison = new Comparison(ComparisonType.Less);
comparator4.LeftSideParameter.ActualName = "CurrentPopulationSize";
comparator4.RightSideParameter.ActualName = MinimumPopulationSizeParameter.Name;
comparator4.ResultParameter.ActualName = "Terminate";
conditionalBranch5.Name = "Terminate?";
conditionalBranch5.ConditionParameter.ActualName = "Terminate";
assigner3.Name = "Reset NumberOfCreatedOffspring";
assigner3.LeftSideParameter.ActualName = "NumberOfCreatedOffspring";
assigner3.RightSideParameter.Value = new IntValue(0);
assigner4.Name = "Reset NumberOfSuccessfulOffspring";
assigner4.LeftSideParameter.ActualName = "NumberOfSuccessfulOffspring";
assigner4.RightSideParameter.Value = new IntValue(0);
assigner5.Name = "Reset OffspringList";
assigner5.LeftSideParameter.ActualName = "OffspringList";
assigner5.RightSideParameter.Value = new ScopeList();
reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
reevaluateElitesBranch.Name = "Reevaluate elites ?";
uniformSubScopesProcessor2.Parallel.Value = true;
evaluator2.Name = "Evaluator (placeholder)";
evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter4.Name = "Increment EvaluatedSolutions";
subScopesCounter4.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = variableCreator;
variableCreator.Successor = assigner1;
assigner1.Successor = resultsCollector;
resultsCollector.Successor = analyzer1;
analyzer1.Successor = selector;
selector.Successor = subScopesProcessor1;
subScopesProcessor1.Operators.Add(new EmptyOperator());
subScopesProcessor1.Operators.Add(childrenCreator);
subScopesProcessor1.Successor = calculator1;
childrenCreator.Successor = uniformSubScopesProcessor;
uniformSubScopesProcessor.Operator = crossover;
uniformSubScopesProcessor.Successor = intCounter1;
crossover.Successor = stochasticBranch;
stochasticBranch.FirstBranch = mutator;
stochasticBranch.SecondBranch = null;
mutator.Successor = null;
stochasticBranch.Successor = evaluator;
evaluator.Successor = weightedParentsQualityComparator;
weightedParentsQualityComparator.Successor = subScopesRemover;
intCounter1.Successor = intCounter2;
intCounter2.Successor = conditionalSelector;
conditionalSelector.Successor = rightReducer1;
rightReducer1.Successor = duplicateSelector;
duplicateSelector.Successor = leftReducer1;
leftReducer1.Successor = progressiveOffspringSelector;
progressiveOffspringSelector.Successor = subScopesCounter2;
calculator1.Successor = conditionalBranch1;
conditionalBranch1.FalseBranch = comparator1;
conditionalBranch1.TrueBranch = subScopesProcessor2;
comparator1.Successor = conditionalBranch2;
conditionalBranch2.FalseBranch = leftReducer2;
conditionalBranch2.TrueBranch = subScopesProcessor2;
leftReducer2.Successor = selector;
subScopesProcessor2.Operators.Add(bestSelector);
subScopesProcessor2.Operators.Add(scopeCleaner);
subScopesProcessor2.Successor = mergingReducer;
bestSelector.Successor = rightReducer2;
rightReducer2.Successor = reevaluateElitesBranch;
reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor2;
uniformSubScopesProcessor2.Operator = evaluator2;
uniformSubScopesProcessor2.Successor = subScopesCounter4;
evaluator2.Successor = null;
subScopesCounter4.Successor = null;
reevaluateElitesBranch.FalseBranch = null;
reevaluateElitesBranch.Successor = null;
scopeCleaner.Successor = scopeRestorer;
mergingReducer.Successor = intCounter3;
intCounter3.Successor = subScopesCounter3;
subScopesCounter3.Successor = calculator2;
calculator2.Successor = comparator2;
comparator2.Successor = conditionalBranch3;
conditionalBranch3.FalseBranch = analyzer2;
conditionalBranch3.TrueBranch = null;
analyzer2.Successor = comparator3;
comparator3.Successor = conditionalBranch4;
conditionalBranch4.FalseBranch = comparator4;
conditionalBranch4.TrueBranch = null;
conditionalBranch4.Successor = null;
comparator4.Successor = conditionalBranch5;
conditionalBranch5.FalseBranch = assigner3;
conditionalBranch5.TrueBranch = null;
conditionalBranch5.Successor = null;
assigner3.Successor = assigner4;
assigner4.Successor = assigner5;
assigner5.Successor = selector;
#endregion
}
public OffspringSelectionGeneticAlgorithm()
: base() {
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 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<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorLowerBound", "The lower bound of the comparison factor (start).", new DoubleValue(0)));
Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorUpperBound", "The upper bound of the comparison factor (end).", new DoubleValue(1)));
Parameters.Add(new OptionalConstrainedValueParameter<IDiscreteDoubleValueModifier>("ComparisonFactorModifier", "The operator used to modify the comparison factor.", new ItemSet<IDiscreteDoubleValueModifier>(new IDiscreteDoubleValueModifier[] { new LinearDiscreteDoubleValueModifier() }), new LinearDiscreteDoubleValueModifier()));
Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
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.", new BoolValue(false)));
Parameters.Add(new ValueLookupParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(200)));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions (approximately).", new IntValue(int.MaxValue)));
Parameters.Add(new FixedValueParameter<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.", new BoolValue(false)) { Hidden = true });
RandomCreator randomCreator = new RandomCreator();
SolutionsCreator solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ResultsCollector resultsCollector = new ResultsCollector();
OffspringSelectionGeneticAlgorithmMainLoop mainLoop = new OffspringSelectionGeneticAlgorithmMainLoop();
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 = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", "", "EvaluatedSolutions"));
resultsCollector.ResultsParameter.ActualName = "Results";
resultsCollector.Successor = mainLoop;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.ComparisonFactorModifierParameter.ActualName = ComparisonFactorModifierParameter.Name;
mainLoop.ComparisonFactorParameter.ActualName = "ComparisonFactor";
mainLoop.ComparisonFactorStartParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
mainLoop.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
SelectorParameter.ValidValues.Add(selector);
ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
ParameterizeSelectors();
foreach (IDiscreteDoubleValueModifier modifier in ApplicationManager.Manager.GetInstances<IDiscreteDoubleValueModifier>().OrderBy(x => x.Name))
ComparisonFactorModifierParameter.ValidValues.Add(modifier);
IDiscreteDoubleValueModifier linearModifier = ComparisonFactorModifierParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("LinearDiscreteDoubleValueModifier"));
if (linearModifier != null) ComparisonFactorModifierParameter.Value = linearModifier;
ParameterizeComparisonFactorModifiers();
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
selectionPressureAnalyzer = new ValueAnalyzer();
successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
ParameterizeAnalyzers();
UpdateAnalyzers();
Initialize();
}
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 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 LookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
Parameters.Add(new LookupParameter<DoubleValue>("BestLocalQuality", "The value which represents the best quality found so far."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("BestKnownQuality", "The problem's best known quality value found so far."));
Parameters.Add(new LookupParameter<DoubleValue>("MoveQuality", "The value which represents the quality of a move."));
Parameters.Add(new LookupParameter<IntValue>("Iterations", "The number of iterations performed."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumIterations", "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>("MoveGenerator", "The operator that generates the moves."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveMaker", "The operator that performs a move and updates the quality."));
Parameters.Add(new ValueLookupParameter<IOperator>("MoveEvaluator", "The operator that evaluates a move."));
Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze the solution and moves."));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedMoves", "The number of evaluated moves."));
#endregion
#region Create operators
SubScopesProcessor subScopesProcessor0 = new SubScopesProcessor();
Assigner bestQualityInitializer = new Assigner();
Placeholder analyzer1 = new Placeholder();
ResultsCollector resultsCollector1 = new ResultsCollector();
SubScopesProcessor mainProcessor = new SubScopesProcessor();
Placeholder moveGenerator = new Placeholder();
UniformSubScopesProcessor moveEvaluationProcessor = new UniformSubScopesProcessor();
Placeholder moveEvaluator = new Placeholder();
SubScopesCounter subScopesCounter = new SubScopesCounter();
BestSelector bestSelector = new BestSelector();
SubScopesProcessor moveMakingProcessor = new SubScopesProcessor();
UniformSubScopesProcessor selectedMoveMakingProcessor = new UniformSubScopesProcessor();
QualityComparator qualityComparator = new QualityComparator();
ConditionalBranch improvesQualityBranch = new ConditionalBranch();
Placeholder moveMaker = new Placeholder();
Assigner bestQualityUpdater = new Assigner();
ResultsCollector resultsCollector2 = new ResultsCollector();
MergingReducer mergingReducer = new MergingReducer();
Placeholder analyzer2 = new Placeholder();
SubScopesRemover subScopesRemover = new SubScopesRemover();
IntCounter iterationsCounter = new IntCounter();
Comparator iterationsComparator = new Comparator();
ConditionalBranch iterationsTermination = new ConditionalBranch();
bestQualityInitializer.Name = "Initialize BestQuality";
bestQualityInitializer.LeftSideParameter.ActualName = BestLocalQualityParameter.Name;
bestQualityInitializer.RightSideParameter.ActualName = QualityParameter.Name;
analyzer1.Name = "Analyzer (placeholder)";
analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
resultsCollector1.CopyValue = new BoolValue(false);
resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>(IterationsParameter.Name));
resultsCollector1.CollectedValues.Add(new LookupParameter<DoubleValue>(BestLocalQualityParameter.Name, null, BestLocalQualityParameter.Name));
resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
moveGenerator.Name = "MoveGenerator (placeholder)";
moveGenerator.OperatorParameter.ActualName = MoveGeneratorParameter.Name;
moveEvaluationProcessor.Parallel = new BoolValue(true);
moveEvaluator.Name = "MoveEvaluator (placeholder)";
moveEvaluator.OperatorParameter.ActualName = MoveEvaluatorParameter.Name;
subScopesCounter.Name = "Increment EvaluatedMoves";
subScopesCounter.ValueParameter.ActualName = EvaluatedMovesParameter.Name;
bestSelector.CopySelected = new BoolValue(false);
bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestSelector.NumberOfSelectedSubScopesParameter.Value = new IntValue(1);
bestSelector.QualityParameter.ActualName = MoveQualityParameter.Name;
qualityComparator.LeftSideParameter.ActualName = MoveQualityParameter.Name;
qualityComparator.RightSideParameter.ActualName = QualityParameter.Name;
qualityComparator.ResultParameter.ActualName = "IsBetter";
improvesQualityBranch.ConditionParameter.ActualName = "IsBetter";
moveMaker.Name = "MoveMaker (placeholder)";
moveMaker.OperatorParameter.ActualName = MoveMakerParameter.Name;
bestQualityUpdater.Name = "Update BestQuality";
bestQualityUpdater.LeftSideParameter.ActualName = BestLocalQualityParameter.Name;
bestQualityUpdater.RightSideParameter.ActualName = QualityParameter.Name;
resultsCollector2.CopyValue = new BoolValue(false);
resultsCollector2.CollectedValues.Add(new LookupParameter<DoubleValue>(BestLocalQualityParameter.Name, null, BestLocalQualityParameter.Name));
resultsCollector2.ResultsParameter.ActualName = ResultsParameter.Name;
analyzer2.Name = "Analyzer (placeholder)";
analyzer2.OperatorParameter.ActualName = AnalyzerParameter.Name;
subScopesRemover.RemoveAllSubScopes = true;
iterationsCounter.Name = "Iterations Counter";
iterationsCounter.Increment = new IntValue(1);
iterationsCounter.ValueParameter.ActualName = IterationsParameter.Name;
iterationsComparator.Name = "Iterations >= MaximumIterations";
iterationsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
iterationsComparator.LeftSideParameter.ActualName = IterationsParameter.Name;
iterationsComparator.RightSideParameter.ActualName = MaximumIterationsParameter.Name;
iterationsComparator.ResultParameter.ActualName = "Terminate";
iterationsTermination.Name = "Iterations Termination Condition";
iterationsTermination.ConditionParameter.ActualName = "Terminate";
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = subScopesProcessor0; // don't change this without adapting the constructor of LocalSearchImprovementOperator
subScopesProcessor0.Operators.Add(bestQualityInitializer);
subScopesProcessor0.Successor = resultsCollector1;
bestQualityInitializer.Successor = analyzer1;
analyzer1.Successor = null;
resultsCollector1.Successor = mainProcessor;
mainProcessor.Operators.Add(moveGenerator);
mainProcessor.Successor = iterationsCounter;
moveGenerator.Successor = moveEvaluationProcessor;
moveEvaluationProcessor.Operator = moveEvaluator;
moveEvaluationProcessor.Successor = subScopesCounter;
moveEvaluator.Successor = null;
subScopesCounter.Successor = bestSelector;
bestSelector.Successor = moveMakingProcessor;
moveMakingProcessor.Operators.Add(new EmptyOperator());
moveMakingProcessor.Operators.Add(selectedMoveMakingProcessor);
moveMakingProcessor.Successor = mergingReducer;
selectedMoveMakingProcessor.Operator = qualityComparator;
qualityComparator.Successor = improvesQualityBranch;
improvesQualityBranch.TrueBranch = moveMaker;
improvesQualityBranch.FalseBranch = null;
improvesQualityBranch.Successor = null;
moveMaker.Successor = bestQualityUpdater;
bestQualityUpdater.Successor = null;
mergingReducer.Successor = analyzer2;
analyzer2.Successor = subScopesRemover;
subScopesRemover.Successor = null;
iterationsCounter.Successor = iterationsComparator;
iterationsComparator.Successor = iterationsTermination;
iterationsTermination.TrueBranch = null;
iterationsTermination.FalseBranch = mainProcessor;
#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 CombinedOperator CreateLayerOpener() {
var layerOpener = new CombinedOperator() { Name = "Open new Layer if needed" };
var maxLayerReached = new Comparator() { Name = "MaxLayersReached = OpenLayers >= NumberOfLayers" };
var maxLayerReachedBranch = new ConditionalBranch() { Name = "MaxLayersReached?" };
var openNewLayerCalculator = new ExpressionCalculator() { Name = "OpenNewLayer = Generations >= AgeLimits[OpenLayers - 1]" };
var openNewLayerBranch = new ConditionalBranch() { Name = "OpenNewLayer?" };
var layerCreator = new LastLayerCloner() { Name = "Create Layer" };
var updateLayerNumber = new Assigner() { Name = "Layer = OpenLayers" };
var historyWiper = new ResultsHistoryWiper() { Name = "Clear History in Results" };
var createChildrenViaCrossover = new AlpsOffspringSelectionGeneticAlgorithmMainOperator();
var incrEvaluatedSolutionsForNewLayer = new SubScopesCounter() { Name = "Update EvaluatedSolutions" };
var incrOpenLayers = new IntCounter() { Name = "Incr. OpenLayers" };
var newLayerResultsCollector = new ResultsCollector() { Name = "Collect new Layer Results" };
layerOpener.OperatorGraph.InitialOperator = maxLayerReached;
maxLayerReached.LeftSideParameter.ActualName = "OpenLayers";
maxLayerReached.RightSideParameter.ActualName = NumberOfLayersParameter.Name;
maxLayerReached.ResultParameter.ActualName = "MaxLayerReached";
maxLayerReached.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
maxLayerReached.Successor = maxLayerReachedBranch;
maxLayerReachedBranch.ConditionParameter.ActualName = "MaxLayerReached";
maxLayerReachedBranch.FalseBranch = openNewLayerCalculator;
openNewLayerCalculator.CollectedValues.Add(new LookupParameter<IntArray>(AgeLimitsParameter.Name));
openNewLayerCalculator.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
openNewLayerCalculator.CollectedValues.Add(new LookupParameter<IntValue>(NumberOfLayersParameter.Name));
openNewLayerCalculator.CollectedValues.Add(new LookupParameter<IntValue>("OpenLayers"));
openNewLayerCalculator.ExpressionResultParameter.ActualName = "OpenNewLayer";
openNewLayerCalculator.ExpressionParameter.Value = new StringValue("Generations 1 + AgeLimits OpenLayers 1 - [] >");
openNewLayerCalculator.Successor = openNewLayerBranch;
openNewLayerBranch.ConditionParameter.ActualName = "OpenNewLayer";
openNewLayerBranch.TrueBranch = layerCreator;
layerCreator.NewLayerOperator = updateLayerNumber;
layerCreator.Successor = incrOpenLayers;
updateLayerNumber.LeftSideParameter.ActualName = "Layer";
updateLayerNumber.RightSideParameter.ActualName = "OpenLayers";
updateLayerNumber.Successor = historyWiper;
historyWiper.ResultsParameter.ActualName = "LayerResults";
historyWiper.Successor = createChildrenViaCrossover;
// Maybe use only crossover and no elitism instead of "default operator"
createChildrenViaCrossover.RandomParameter.ActualName = LocalRandomParameter.Name;
createChildrenViaCrossover.EvaluatorParameter.ActualName = EvaluatorParameter.Name;
createChildrenViaCrossover.EvaluatedSolutionsParameter.ActualName = "LayerEvaluatedSolutions";
createChildrenViaCrossover.QualityParameter.ActualName = QualityParameter.Name;
createChildrenViaCrossover.MaximizationParameter.ActualName = MaximizationParameter.Name;
createChildrenViaCrossover.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
createChildrenViaCrossover.SelectorParameter.ActualName = SelectorParameter.Name;
createChildrenViaCrossover.CrossoverParameter.ActualName = CrossoverParameter.Name;
createChildrenViaCrossover.MutatorParameter.ActualName = MutatorParameter.ActualName;
createChildrenViaCrossover.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
createChildrenViaCrossover.ElitesParameter.ActualName = ElitesParameter.Name;
createChildrenViaCrossover.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
createChildrenViaCrossover.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
createChildrenViaCrossover.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
createChildrenViaCrossover.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio";
createChildrenViaCrossover.SelectionPressureParameter.ActualName = "SelectionPressure";
createChildrenViaCrossover.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
createChildrenViaCrossover.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
createChildrenViaCrossover.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
createChildrenViaCrossover.AgeParameter.ActualName = AgeParameter.Name;
createChildrenViaCrossover.AgeInheritanceParameter.ActualName = AgeInheritanceParameter.Name;
createChildrenViaCrossover.AgeIncrementParameter.Value = new DoubleValue(0.0);
createChildrenViaCrossover.Successor = incrEvaluatedSolutionsForNewLayer;
incrEvaluatedSolutionsForNewLayer.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
incrEvaluatedSolutionsForNewLayer.AccumulateParameter.Value = new BoolValue(true);
incrOpenLayers.ValueParameter.ActualName = "OpenLayers";
incrOpenLayers.Increment = new IntValue(1);
incrOpenLayers.Successor = newLayerResultsCollector;
newLayerResultsCollector.CollectedValues.Add(new ScopeTreeLookupParameter<ResultCollection>("LayerResults", "Result set for each layer", "LayerResults"));
newLayerResultsCollector.CopyValue = new BoolValue(false);
newLayerResultsCollector.Successor = null;
return layerOpener;
}
private void Initialize() {
#region Create parameters
Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("BestKnownQuality", "The best known quality value found so far."));
Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "µ (mu) - the size of the population."));
Parameters.Add(new ValueLookupParameter<IntValue>("ParentsPerChild", "ρ (rho) - how many parents should be recombined."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
Parameters.Add(new ValueLookupParameter<BoolValue>("PlusSelection", "True for plus selection (elitist population), false for comma selection (non-elitist population)."));
Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueLookupParameter<IOperator>("Recombinator", "The operator used to cross solutions."));
Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the OffspringSelectionEvolutionStrategy should be applied."));
Parameters.Add(new ValueLookupParameter<IOperator>("StrategyParameterManipulator", "The operator to mutate the endogeneous strategy parameters."));
Parameters.Add(new ValueLookupParameter<IOperator>("StrategyParameterCrossover", "The operator to cross the endogeneous strategy parameters."));
Parameters.Add(new LookupParameter<DoubleValue>("CurrentSuccessRatio", "The current success ratio."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved."));
Parameters.Add(new LookupParameter<DoubleValue>("SelectionPressure", "The actual selection pressure."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions."));
Parameters.Add(new ValueLookupParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems."));
Parameters.Add(new LookupParameter<DoubleValue>("ComparisonFactor", "The comparison factor is used to determine whether the offspring should be compared to the better parent, the worse parent or a quality value linearly interpolated between them. It is in the range [0;1]."));
#endregion
#region Create operators
VariableCreator variableCreator = new VariableCreator();
ResultsCollector resultsCollector1 = new ResultsCollector();
Placeholder analyzer1 = new Placeholder();
WithoutRepeatingBatchedRandomSelector selector = new WithoutRepeatingBatchedRandomSelector();
SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
Comparator useRecombinationComparator = new Comparator();
ConditionalBranch useRecombinationBranch = new ConditionalBranch();
ChildrenCreator childrenCreator = new ChildrenCreator();
UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
Placeholder recombinator = new Placeholder();
Placeholder strategyRecombinator = new Placeholder();
Placeholder strategyMutator1 = new Placeholder();
Placeholder mutator1 = new Placeholder();
SubScopesRemover subScopesRemover = new SubScopesRemover();
UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
Placeholder strategyMutator2 = new Placeholder();
Placeholder mutator2 = new Placeholder();
UniformSubScopesProcessor uniformSubScopesProcessor3 = new UniformSubScopesProcessor();
Placeholder evaluator = new Placeholder();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ConditionalBranch plusOrCommaReplacementBranch = new ConditionalBranch();
MergingReducer plusReplacement = new MergingReducer();
RightReducer commaReplacement = new RightReducer();
BestSelector bestSelector = new BestSelector();
RightReducer rightReducer = new RightReducer();
IntCounter intCounter = new IntCounter();
Comparator maxGenerationsComparator = new Comparator();
Placeholder analyzer2 = new Placeholder();
ConditionalBranch conditionalBranchTerminate = new ConditionalBranch();
ConditionalBranch reevaluateElitesBranch = new ConditionalBranch();
SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
UniformSubScopesProcessor uniformSubScopesProcessor4 = new UniformSubScopesProcessor();
Placeholder evaluator2 = new Placeholder();
SubScopesCounter subScopesCounter2 = new SubScopesCounter();
WeightedParentsQualityComparator parentsComparator = new WeightedParentsQualityComparator();
SubScopesRemover subScopesRemover_afterCompare = new SubScopesRemover();
EvolutionStrategyOffspringSelector offspringSelector = new EvolutionStrategyOffspringSelector();
ChildrenCopyCreator childrenCopyCreator = new ChildrenCopyCreator();
Comparator maxSelectionPressureComparator = new Comparator();
ConditionalBranch conditionalBranchTerminateSelPressure = new ConditionalBranch();
Comparator maxEvaluatedSolutionsComparator = new Comparator();
ConditionalBranch conditionalBranchTerminateEvalSolutions = new ConditionalBranch();
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class OffspringSelectionEvolutionStrategy expects this to be called Generations
variableCreator.CollectedValues.Add(new ValueParameter<DoubleValue>("SelectionPressure", new DoubleValue(0)));
variableCreator.CollectedValues.Add(new ValueParameter<DoubleValue>("CurrentSuccessRatio", new DoubleValue(0)));
resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
resultsCollector1.CollectedValues.Add(new LookupParameter<DoubleValue>("Current Selection Pressure", "Displays the rising selection pressure during a generation.", "SelectionPressure"));
resultsCollector1.CollectedValues.Add(new LookupParameter<DoubleValue>("Current Success Ratio", "Indicates how many successful children were already found during a generation (relative to the population size).", "CurrentSuccessRatio"));
resultsCollector1.CopyValue = new BoolValue(false);
resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
analyzer1.Name = "Analyzer (placeholder)";
analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
selector.Name = "ES Random Selector";
selector.RandomParameter.ActualName = RandomParameter.Name;
selector.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
selector.ChildrenParameter.ActualName = SelectedParentsParameter.Name;
useRecombinationComparator.Name = "ParentsPerChild > 1";
useRecombinationComparator.LeftSideParameter.ActualName = ParentsPerChildParameter.Name;
useRecombinationComparator.RightSideParameter.Value = new IntValue(1);
useRecombinationComparator.Comparison = new Comparison(ComparisonType.Greater);
useRecombinationComparator.ResultParameter.ActualName = "UseRecombination";
useRecombinationBranch.Name = "Use Recombination?";
useRecombinationBranch.ConditionParameter.ActualName = "UseRecombination";
childrenCreator.ParentsPerChild = null;
childrenCreator.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
recombinator.Name = "Recombinator (placeholder)";
recombinator.OperatorParameter.ActualName = RecombinatorParameter.Name;
strategyRecombinator.Name = "Strategy Parameter Recombinator (placeholder)";
strategyRecombinator.OperatorParameter.ActualName = StrategyParameterCrossoverParameter.Name;
strategyMutator1.Name = "Strategy Parameter Manipulator (placeholder)";
strategyMutator1.OperatorParameter.ActualName = StrategyParameterManipulatorParameter.Name;
mutator1.Name = "Mutator (placeholder)";
mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
subScopesRemover.RemoveAllSubScopes = true;
strategyMutator2.Name = "Strategy Parameter Manipulator (placeholder)";
strategyMutator2.OperatorParameter.ActualName = StrategyParameterManipulatorParameter.Name;
mutator2.Name = "Mutator (placeholder)";
mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
uniformSubScopesProcessor3.Parallel.Value = true;
evaluator.Name = "Evaluator (placeholder)";
evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter.Name = "Increment EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
plusOrCommaReplacementBranch.ConditionParameter.ActualName = PlusSelectionParameter.Name;
bestSelector.CopySelected = new BoolValue(false);
bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name;
bestSelector.QualityParameter.ActualName = QualityParameter.Name;
intCounter.Increment = new IntValue(1);
intCounter.ValueParameter.ActualName = "Generations";
maxGenerationsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
maxGenerationsComparator.LeftSideParameter.ActualName = "Generations";
maxGenerationsComparator.ResultParameter.ActualName = "Terminate";
maxGenerationsComparator.RightSideParameter.ActualName = MaximumGenerationsParameter.Name;
analyzer2.Name = "Analyzer (placeholder)";
analyzer2.OperatorParameter.ActualName = AnalyzerParameter.Name;
conditionalBranchTerminate.ConditionParameter.ActualName = "Terminate";
reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
reevaluateElitesBranch.Name = "Reevaluate elites ?";
uniformSubScopesProcessor4.Parallel.Value = true;
evaluator2.Name = "Evaluator (placeholder)";
evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter2.Name = "Increment EvaluatedSolutions";
subScopesCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
parentsComparator.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
parentsComparator.LeftSideParameter.ActualName = QualityParameter.Name;
parentsComparator.RightSideParameter.ActualName = QualityParameter.Name;
parentsComparator.MaximizationParameter.ActualName = MaximizationParameter.Name;
parentsComparator.ResultParameter.ActualName = "SuccessfulOffspring";
subScopesRemover_afterCompare.RemoveAllSubScopes = true;
offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name;
offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name;
offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
offspringSelector.OffspringPopulationParameter.ActualName = "OffspringPopulation";
offspringSelector.OffspringPopulationWinnersParameter.ActualName = "OffspringPopulationWinners";
offspringSelector.SuccessfulOffspringParameter.ActualName = "SuccessfulOffspring";
offspringSelector.QualityParameter.ActualName = QualityParameter.Name;
maxSelectionPressureComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
maxSelectionPressureComparator.LeftSideParameter.ActualName = "SelectionPressure";
maxSelectionPressureComparator.ResultParameter.ActualName = "TerminateSelectionPressure";
maxSelectionPressureComparator.RightSideParameter.ActualName = MaximumSelectionPressureParameter.Name;
conditionalBranchTerminateSelPressure.ConditionParameter.ActualName = "TerminateSelectionPressure";
maxEvaluatedSolutionsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
maxEvaluatedSolutionsComparator.LeftSideParameter.ActualName = "EvaluatedSolutions";
maxEvaluatedSolutionsComparator.ResultParameter.ActualName = "TerminateEvaluatedSolutions";
maxEvaluatedSolutionsComparator.RightSideParameter.ActualName = MaximumEvaluatedSolutionsParameter.Name;
conditionalBranchTerminateEvalSolutions.ConditionParameter.ActualName = "TerminateEvaluatedSolutions";
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = variableCreator;
variableCreator.Successor = resultsCollector1;
resultsCollector1.Successor = analyzer1;
analyzer1.Successor = selector;
selector.Successor = subScopesProcessor1;
subScopesProcessor1.Operators.Add(new EmptyOperator());
subScopesProcessor1.Operators.Add(useRecombinationComparator);
subScopesProcessor1.Successor = offspringSelector;
offspringSelector.OffspringCreator = selector;
offspringSelector.Successor = plusOrCommaReplacementBranch;
useRecombinationComparator.Successor = useRecombinationBranch;
useRecombinationBranch.TrueBranch = childrenCreator;
useRecombinationBranch.FalseBranch = childrenCopyCreator;
childrenCopyCreator.Successor = uniformSubScopesProcessor2;
useRecombinationBranch.Successor = uniformSubScopesProcessor3;
childrenCreator.Successor = uniformSubScopesProcessor1;
uniformSubScopesProcessor1.Operator = recombinator;
uniformSubScopesProcessor1.Successor = null;
recombinator.Successor = strategyRecombinator;
strategyRecombinator.Successor = strategyMutator1;
strategyMutator1.Successor = mutator1;
mutator1.Successor = null;
uniformSubScopesProcessor2.Operator = strategyMutator2;
uniformSubScopesProcessor2.Successor = null;
strategyMutator2.Successor = mutator2;
mutator2.Successor = null;
uniformSubScopesProcessor3.Operator = evaluator;
uniformSubScopesProcessor3.Successor = subScopesCounter;
evaluator.Successor = parentsComparator;
parentsComparator.Successor = subScopesRemover_afterCompare;
subScopesRemover_afterCompare.Successor = null;
subScopesCounter.Successor = null;
plusOrCommaReplacementBranch.TrueBranch = reevaluateElitesBranch;
reevaluateElitesBranch.TrueBranch = subScopesProcessor2;
reevaluateElitesBranch.FalseBranch = null;
subScopesProcessor2.Operators.Add(uniformSubScopesProcessor4);
subScopesProcessor2.Operators.Add(new EmptyOperator());
uniformSubScopesProcessor4.Operator = evaluator2;
uniformSubScopesProcessor4.Successor = subScopesCounter2;
subScopesCounter2.Successor = null;
reevaluateElitesBranch.Successor = plusReplacement;
plusReplacement.Successor = bestSelector;
bestSelector.Successor = rightReducer;
plusOrCommaReplacementBranch.FalseBranch = commaReplacement;
plusOrCommaReplacementBranch.Successor = intCounter;
intCounter.Successor = maxGenerationsComparator;
maxGenerationsComparator.Successor = maxSelectionPressureComparator;
maxSelectionPressureComparator.Successor = maxEvaluatedSolutionsComparator;
maxEvaluatedSolutionsComparator.Successor = analyzer2;
analyzer2.Successor = conditionalBranchTerminate;
conditionalBranchTerminate.FalseBranch = conditionalBranchTerminateSelPressure;
conditionalBranchTerminate.TrueBranch = null;
conditionalBranchTerminate.Successor = null;
conditionalBranchTerminateSelPressure.FalseBranch = conditionalBranchTerminateEvalSolutions;
conditionalBranchTerminateSelPressure.TrueBranch = null;
conditionalBranchTerminateSelPressure.Successor = null;
conditionalBranchTerminateEvalSolutions.FalseBranch = selector;
conditionalBranchTerminateEvalSolutions.TrueBranch = null;
conditionalBranchTerminateEvalSolutions.Successor = null;
#endregion
}
public AlpsGeneticAlgorithm()
: base() {
#region Add parameters
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 FixedValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze all individuals from all layers combined.", new MultiAnalyzer()));
Parameters.Add(new FixedValueParameter<MultiAnalyzer>("LayerAnalyzer", "The operator used to analyze each layer.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter<IntValue>("NumberOfLayers", "The number of layers.", new IntValue(10)));
Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions in each layer.", new IntValue(100)));
Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate 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 ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
Parameters.Add(new ValueParameter<BoolValue>("PlusSelection", "Include the parents in the selection of the invividuals for the next generation.", new BoolValue(false)));
Parameters.Add(new ValueParameter<EnumValue<AgingScheme>>("AgingScheme", "The aging scheme for setting the age-limits for the layers.", new EnumValue<AgingScheme>(ALPS.AgingScheme.Polynomial)));
Parameters.Add(new ValueParameter<IntValue>("AgeGap", "The frequency of reseeding the lowest layer and scaling factor for the age-limits for the layers.", new IntValue(20)));
Parameters.Add(new ValueParameter<DoubleValue>("AgeInheritance", "A weight that determines the age of a child after crossover based on the older (1.0) and younger (0.0) parent.", new DoubleValue(1.0)) { Hidden = true });
Parameters.Add(new ValueParameter<IntArray>("AgeLimits", "The maximum age an individual is allowed to reach in a certain layer.", new IntArray(new int[0])) { Hidden = true });
Parameters.Add(new ValueParameter<IntValue>("MatingPoolRange", "The range of layers used for creating a mating pool. (1 = current + previous layer)", new IntValue(1)) { Hidden = true });
Parameters.Add(new ValueParameter<BoolValue>("ReduceToPopulationSize", "Reduce the CurrentPopulationSize after elder migration to PopulationSize", new BoolValue(true)) { Hidden = true });
Parameters.Add(new ValueParameter<MultiTerminator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop.", new MultiTerminator()));
#endregion
#region Create operators
var globalRandomCreator = new RandomCreator();
var layer0Creator = new SubScopesCreator() { Name = "Create Layer Zero" };
var layer0Processor = new SubScopesProcessor();
var localRandomCreator = new LocalRandomCreator();
var layerSolutionsCreator = new SolutionsCreator();
var initializeAgeProcessor = new UniformSubScopesProcessor();
var initializeAge = new VariableCreator() { Name = "Initialize Age" };
var initializeCurrentPopulationSize = new SubScopesCounter() { Name = "Initialize CurrentPopulationCounter" };
var initializeLocalEvaluatedSolutions = new Assigner() { Name = "Initialize LayerEvaluatedSolutions" };
var initializeGlobalEvaluatedSolutions = new DataReducer() { Name = "Initialize EvaluatedSolutions" };
var resultsCollector = new ResultsCollector();
var mainLoop = new AlpsGeneticAlgorithmMainLoop();
#endregion
#region Create and parameterize operator graph
OperatorGraph.InitialOperator = globalRandomCreator;
globalRandomCreator.RandomParameter.ActualName = "GlobalRandom";
globalRandomCreator.SeedParameter.Value = null;
globalRandomCreator.SeedParameter.ActualName = SeedParameter.Name;
globalRandomCreator.SetSeedRandomlyParameter.Value = null;
globalRandomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
globalRandomCreator.Successor = layer0Creator;
layer0Creator.NumberOfSubScopesParameter.Value = new IntValue(1);
layer0Creator.Successor = layer0Processor;
layer0Processor.Operators.Add(localRandomCreator);
layer0Processor.Successor = initializeGlobalEvaluatedSolutions;
localRandomCreator.Successor = layerSolutionsCreator;
layerSolutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
layerSolutionsCreator.Successor = initializeAgeProcessor;
initializeAgeProcessor.Operator = initializeAge;
initializeAgeProcessor.Successor = initializeCurrentPopulationSize;
initializeCurrentPopulationSize.ValueParameter.ActualName = "CurrentPopulationSize";
initializeCurrentPopulationSize.Successor = initializeLocalEvaluatedSolutions;
initializeAge.CollectedValues.Add(new ValueParameter<DoubleValue>("Age", new DoubleValue(0)));
initializeAge.Successor = null;
initializeLocalEvaluatedSolutions.LeftSideParameter.ActualName = "LayerEvaluatedSolutions";
initializeLocalEvaluatedSolutions.RightSideParameter.ActualName = "CurrentPopulationSize";
initializeLocalEvaluatedSolutions.Successor = null;
initializeGlobalEvaluatedSolutions.ReductionOperation.Value.Value = ReductionOperations.Sum;
initializeGlobalEvaluatedSolutions.TargetOperation.Value.Value = ReductionOperations.Assign;
initializeGlobalEvaluatedSolutions.ParameterToReduce.ActualName = "LayerEvaluatedSolutions";
initializeGlobalEvaluatedSolutions.TargetParameter.ActualName = "EvaluatedSolutions";
initializeGlobalEvaluatedSolutions.Successor = resultsCollector;
resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
resultsCollector.Successor = mainLoop;
mainLoop.GlobalRandomParameter.ActualName = "GlobalRandom";
mainLoop.LocalRandomParameter.ActualName = localRandomCreator.LocalRandomParameter.Name;
mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.LayerAnalyzerParameter.ActualName = LayerAnalyzerParameter.Name;
mainLoop.NumberOfLayersParameter.ActualName = NumberOfLayersParameter.Name;
mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
mainLoop.CurrentPopulationSizeParameter.ActualName = "CurrentPopulationSize";
mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
mainLoop.PlusSelectionParameter.ActualName = PlusSelectionParameter.Name;
mainLoop.AgeParameter.ActualName = "Age";
mainLoop.AgeGapParameter.ActualName = AgeGapParameter.Name;
mainLoop.AgeInheritanceParameter.ActualName = AgeInheritanceParameter.Name;
mainLoop.AgeLimitsParameter.ActualName = AgeLimitsParameter.Name;
mainLoop.MatingPoolRangeParameter.ActualName = MatingPoolRangeParameter.Name;
mainLoop.ReduceToPopulationSizeParameter.ActualName = ReduceToPopulationSizeParameter.Name;
mainLoop.TerminatorParameter.ActualName = TerminatorParameter.Name;
#endregion
#region Set selectors
foreach (var selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(s => !(s is IMultiObjectiveSelector)).OrderBy(s => Name))
SelectorParameter.ValidValues.Add(selector);
var defaultSelector = SelectorParameter.ValidValues.OfType<GeneralizedRankSelector>().FirstOrDefault();
if (defaultSelector != null) {
defaultSelector.PressureParameter.Value = new DoubleValue(4.0);
SelectorParameter.Value = defaultSelector;
}
#endregion
#region Create analyzers
qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
layerQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
ageAnalyzer = new OldestAverageYoungestAgeAnalyzer();
layerAgeAnalyzer = new OldestAverageYoungestAgeAnalyzer();
ageDistributionAnalyzer = new AgeDistributionAnalyzer();
layerAgeDistributionAnalyzer = new AgeDistributionAnalyzer();
#endregion
#region Create terminators
generationsTerminator = new ComparisonTerminator<IntValue>("Generations", ComparisonType.Less, new IntValue(1000)) { Name = "Generations" };
evaluationsTerminator = new ComparisonTerminator<IntValue>("EvaluatedSolutions", ComparisonType.Less, new IntValue(int.MaxValue)) { Name = "Evaluations" };
qualityTerminator = new SingleObjectiveQualityTerminator() { Name = "Quality" };
executionTimeTerminator = new ExecutionTimeTerminator(this, new TimeSpanValue(TimeSpan.FromMinutes(5)));
#endregion
#region Parameterize
UpdateAnalyzers();
ParameterizeAnalyzers();
ParameterizeSelectors();
UpdateTerminators();
ParameterizeAgeLimits();
#endregion
Initialize();
}
private void Initialize() {
#region Create parameters
Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
Parameters.Add(new ValueLookupParameter<DoubleValue>("BestKnownQuality", "The best known quality value found so far."));
Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "µ (mu) - the size of the population."));
Parameters.Add(new ValueLookupParameter<IntValue>("ParentsPerChild", "ρ (rho) - how many parents should be recombined."));
Parameters.Add(new ValueLookupParameter<IntValue>("Children", "λ (lambda) - the size of the offspring population."));
Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
Parameters.Add(new ValueLookupParameter<BoolValue>("PlusSelection", "True for plus selection (elitist population), false for comma selection (non-elitist population)."));
Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueLookupParameter<IOperator>("Recombinator", "The operator used to cross solutions."));
Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the EvolutionStrategy should be applied."));
Parameters.Add(new ValueLookupParameter<IOperator>("StrategyParameterManipulator", "The operator to mutate the endogeneous strategy parameters."));
Parameters.Add(new ValueLookupParameter<IOperator>("StrategyParameterCrossover", "The operator to cross the endogeneous strategy parameters."));
#endregion
#region Create operators
VariableCreator variableCreator = new VariableCreator();
ResultsCollector resultsCollector1 = new ResultsCollector();
Placeholder analyzer1 = new Placeholder();
WithoutRepeatingBatchedRandomSelector selector = new WithoutRepeatingBatchedRandomSelector();
SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
Comparator useRecombinationComparator = new Comparator();
ConditionalBranch useRecombinationBranch = new ConditionalBranch();
ChildrenCreator childrenCreator = new ChildrenCreator();
UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
Placeholder recombinator = new Placeholder();
Placeholder strategyRecombinator = new Placeholder();
Placeholder strategyMutator1 = new Placeholder();
Placeholder mutator1 = new Placeholder();
SubScopesRemover subScopesRemover = new SubScopesRemover();
UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
Placeholder strategyMutator2 = new Placeholder();
Placeholder mutator2 = new Placeholder();
UniformSubScopesProcessor uniformSubScopesProcessor3 = new UniformSubScopesProcessor();
Placeholder evaluator = new Placeholder();
SubScopesCounter subScopesCounter = new SubScopesCounter();
ConditionalBranch plusOrCommaReplacementBranch = new ConditionalBranch();
MergingReducer plusReplacement = new MergingReducer();
RightReducer commaReplacement = new RightReducer();
BestSelector bestSelector = new BestSelector();
RightReducer rightReducer = new RightReducer();
IntCounter intCounter = new IntCounter();
Comparator comparator = new Comparator();
Placeholder analyzer2 = new Placeholder();
ConditionalBranch conditionalBranch = new ConditionalBranch();
ConditionalBranch reevaluateElitesBranch = new ConditionalBranch();
SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
UniformSubScopesProcessor uniformSubScopesProcessor4 = new UniformSubScopesProcessor();
Placeholder evaluator2 = new Placeholder();
SubScopesCounter subScopesCounter2 = new SubScopesCounter();
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class EvolutionStrategy expects this to be called Generations
resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
resultsCollector1.ResultsParameter.ActualName = "Results";
analyzer1.Name = "Analyzer (placeholder)";
analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
selector.Name = "ES Random Selector";
selector.RandomParameter.ActualName = RandomParameter.Name;
selector.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
selector.ChildrenParameter.ActualName = ChildrenParameter.Name;
useRecombinationComparator.Name = "ParentsPerChild > 1";
useRecombinationComparator.LeftSideParameter.ActualName = ParentsPerChildParameter.Name;
useRecombinationComparator.RightSideParameter.Value = new IntValue(1);
useRecombinationComparator.Comparison = new Comparison(ComparisonType.Greater);
useRecombinationComparator.ResultParameter.ActualName = "UseRecombination";
useRecombinationBranch.Name = "Use Recombination?";
useRecombinationBranch.ConditionParameter.ActualName = "UseRecombination";
childrenCreator.ParentsPerChild = null;
childrenCreator.ParentsPerChildParameter.ActualName = ParentsPerChildParameter.Name;
recombinator.Name = "Recombinator (placeholder)";
recombinator.OperatorParameter.ActualName = RecombinatorParameter.Name;
strategyRecombinator.Name = "Strategy Parameter Recombinator (placeholder)";
strategyRecombinator.OperatorParameter.ActualName = StrategyParameterCrossoverParameter.Name;
strategyMutator1.Name = "Strategy Parameter Manipulator (placeholder)";
strategyMutator1.OperatorParameter.ActualName = StrategyParameterManipulatorParameter.Name;
mutator1.Name = "Mutator (placeholder)";
mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
subScopesRemover.RemoveAllSubScopes = true;
strategyMutator2.Name = "Strategy Parameter Manipulator (placeholder)";
strategyMutator2.OperatorParameter.ActualName = StrategyParameterManipulatorParameter.Name;
mutator2.Name = "Mutator (placeholder)";
mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
uniformSubScopesProcessor3.Parallel.Value = true;
evaluator.Name = "Evaluator (placeholder)";
evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter.Name = "Increment EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
plusOrCommaReplacementBranch.ConditionParameter.ActualName = PlusSelectionParameter.Name;
bestSelector.CopySelected = new BoolValue(false);
bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name;
bestSelector.QualityParameter.ActualName = QualityParameter.Name;
intCounter.Increment = new IntValue(1);
intCounter.ValueParameter.ActualName = "Generations";
comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
comparator.LeftSideParameter.ActualName = "Generations";
comparator.ResultParameter.ActualName = "Terminate";
comparator.RightSideParameter.ActualName = MaximumGenerationsParameter.Name;
analyzer2.Name = "Analyzer (placeholder)";
analyzer2.OperatorParameter.ActualName = AnalyzerParameter.Name;
conditionalBranch.ConditionParameter.ActualName = "Terminate";
reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
reevaluateElitesBranch.Name = "Reevaluate elites ?";
uniformSubScopesProcessor4.Parallel.Value = true;
evaluator2.Name = "Evaluator (placeholder)";
evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
subScopesCounter2.Name = "Increment EvaluatedSolutions";
subScopesCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = variableCreator;
variableCreator.Successor = resultsCollector1;
resultsCollector1.Successor = analyzer1;
analyzer1.Successor = selector;
selector.Successor = subScopesProcessor1;
subScopesProcessor1.Operators.Add(new EmptyOperator());
subScopesProcessor1.Operators.Add(useRecombinationComparator);
subScopesProcessor1.Successor = plusOrCommaReplacementBranch;
useRecombinationComparator.Successor = useRecombinationBranch;
useRecombinationBranch.TrueBranch = childrenCreator;
useRecombinationBranch.FalseBranch = uniformSubScopesProcessor2;
useRecombinationBranch.Successor = uniformSubScopesProcessor3;
childrenCreator.Successor = uniformSubScopesProcessor1;
uniformSubScopesProcessor1.Operator = recombinator;
uniformSubScopesProcessor1.Successor = null;
recombinator.Successor = strategyRecombinator;
strategyRecombinator.Successor = strategyMutator1;
strategyMutator1.Successor = mutator1;
mutator1.Successor = subScopesRemover;
subScopesRemover.Successor = null;
uniformSubScopesProcessor2.Operator = strategyMutator2;
uniformSubScopesProcessor2.Successor = null;
strategyMutator2.Successor = mutator2;
mutator2.Successor = null;
uniformSubScopesProcessor3.Operator = evaluator;
uniformSubScopesProcessor3.Successor = subScopesCounter;
evaluator.Successor = null;
subScopesCounter.Successor = null;
plusOrCommaReplacementBranch.TrueBranch = reevaluateElitesBranch;
reevaluateElitesBranch.TrueBranch = subScopesProcessor2;
reevaluateElitesBranch.FalseBranch = null;
subScopesProcessor2.Operators.Add(uniformSubScopesProcessor4);
subScopesProcessor2.Operators.Add(new EmptyOperator());
uniformSubScopesProcessor4.Operator = evaluator2;
uniformSubScopesProcessor4.Successor = subScopesCounter2;
subScopesCounter2.Successor = null;
reevaluateElitesBranch.Successor = plusReplacement;
plusOrCommaReplacementBranch.FalseBranch = commaReplacement;
plusOrCommaReplacementBranch.Successor = bestSelector;
bestSelector.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<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 ParticleSwarmOptimization()
: base() {
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>("SwarmSize", "Size of the particle swarm.", new IntValue(10)));
Parameters.Add(new ValueParameter<IntValue>("MaxIterations", "Maximal number of iterations.", new IntValue(1000)));
Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
Parameters.Add(new ValueParameter<DoubleValue>("Inertia", "Inertia weight on a particle's movement (omega).", new DoubleValue(1)));
Parameters.Add(new ValueParameter<DoubleValue>("PersonalBestAttraction", "Weight for particle's pull towards its personal best soution (phi_p).", new DoubleValue(-0.01)));
Parameters.Add(new ValueParameter<DoubleValue>("NeighborBestAttraction", "Weight for pull towards the neighborhood best solution or global best solution in case of a totally connected topology (phi_g).", new DoubleValue(3.7)));
Parameters.Add(new ConstrainedValueParameter<IParticleCreator>("ParticleCreator", "Operator that creates a new particle."));
Parameters.Add(new ConstrainedValueParameter<IParticleUpdater>("ParticleUpdater", "Operator that updates a particle."));
Parameters.Add(new OptionalConstrainedValueParameter<ITopologyInitializer>("TopologyInitializer", "Creates neighborhood description vectors."));
Parameters.Add(new OptionalConstrainedValueParameter<ITopologyUpdater>("TopologyUpdater", "Updates the neighborhood description vectors."));
Parameters.Add(new OptionalConstrainedValueParameter<IDiscreteDoubleValueModifier>("InertiaUpdater", "Updates the omega parameter."));
Parameters.Add(new ConstrainedValueParameter<ISwarmUpdater>("SwarmUpdater", "Encoding-specific parameter which is provided by the problem. May provide additional encoding-specific parameters, such as velocity bounds for real valued problems"));
ParticleUpdaterParameter.Hidden = true;
RandomCreator randomCreator = new RandomCreator();
VariableCreator variableCreator = new VariableCreator();
Assigner currentInertiaAssigner = new Assigner();
solutionsCreator = new SolutionsCreator();
SubScopesCounter subScopesCounter = new SubScopesCounter();
Placeholder topologyInitializerPlaceholder = new Placeholder();
mainLoop = new ParticleSwarmOptimizationMainLoop();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.SeedParameter.Value = null;
randomCreator.Successor = variableCreator;
variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Iterations", new IntValue(0)));
variableCreator.Successor = currentInertiaAssigner;
currentInertiaAssigner.Name = "CurrentInertia := Inertia";
currentInertiaAssigner.LeftSideParameter.ActualName = "CurrentInertia";
currentInertiaAssigner.RightSideParameter.ActualName = "Inertia";
currentInertiaAssigner.Successor = solutionsCreator;
solutionsCreator.NumberOfSolutionsParameter.ActualName = "SwarmSize";
ParameterizeSolutionsCreator();
solutionsCreator.Successor = subScopesCounter;
subScopesCounter.Name = "Initialize EvaluatedSolutions";
subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
subScopesCounter.Successor = topologyInitializerPlaceholder;
topologyInitializerPlaceholder.Name = "(TopologyInitializer)";
topologyInitializerPlaceholder.OperatorParameter.ActualName = "TopologyInitializer";
topologyInitializerPlaceholder.Successor = mainLoop;
mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
mainLoop.InertiaParameter.ActualName = "CurrentInertia";
mainLoop.MaxIterationsParameter.ActualName = MaxIterationsParameter.Name;
mainLoop.NeighborBestAttractionParameter.ActualName = NeighborBestAttractionParameter.Name;
mainLoop.InertiaUpdaterParameter.ActualName = InertiaUpdaterParameter.Name;
mainLoop.ParticleUpdaterParameter.ActualName = ParticleUpdaterParameter.Name;
mainLoop.PersonalBestAttractionParameter.ActualName = PersonalBestAttractionParameter.Name;
mainLoop.RandomParameter.ActualName = randomCreator.RandomParameter.ActualName;
mainLoop.SwarmSizeParameter.ActualName = SwarmSizeParameter.Name;
mainLoop.TopologyUpdaterParameter.ActualName = TopologyUpdaterParameter.Name;
mainLoop.RandomParameter.ActualName = randomCreator.RandomParameter.ActualName;
mainLoop.ResultsParameter.ActualName = "Results";
InitializeAnalyzers();
InitializeParticleCreator();
InitializeSwarmUpdater();
ParameterizeSolutionsCreator();
UpdateAnalyzers();
UpdateInertiaUpdater();
InitInertiaUpdater();
UpdateTopologyInitializer();
Initialize();
ParameterizeMainLoop();
}
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;
}