public WorstReplacer() : base() { Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The quality of a solution.")); Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); WorstSelector worstSelector = new WorstSelector(); worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; worstSelector.QualityParameter.ActualName = QualityParameter.Name; ReplacedSelectorParameter.Value = worstSelector; ReplacedSelectorParameter.Hidden = true; BestSelector bestSelector = new BestSelector(); bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; bestSelector.QualityParameter.ActualName = QualityParameter.Name; SelectedSelectorParameter.Value = bestSelector; SelectedSelectorParameter.Hidden = true; }
public WorstReplacer() : base() { Parameters.Add(new ScopeTreeLookupParameter <DoubleValue>("Quality", "The quality of a solution.")); Parameters.Add(new ValueLookupParameter <BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); WorstSelector worstSelector = new WorstSelector(); worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; worstSelector.QualityParameter.ActualName = QualityParameter.Name; ReplacedSelectorParameter.Value = worstSelector; ReplacedSelectorParameter.Hidden = true; BestSelector bestSelector = new BestSelector(); bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; bestSelector.QualityParameter.ActualName = QualityParameter.Name; SelectedSelectorParameter.Value = bestSelector; SelectedSelectorParameter.Hidden = true; }
private WorstSelector(WorstSelector original, Cloner cloner) : base(original, cloner) { }
private void Initialize() { #region Create parameters Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization.")); Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated solutions.")); Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.")); Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); Parameters.Add(new LookupParameter<DoubleValue>("ComparisonFactor", "The comparison factor is used to determine whether the offspring should be compared to the better parent, the worse parent or a quality value linearly interpolated between them. It is in the range [0;1].")); Parameters.Add(new LookupParameter<DoubleValue>("CurrentSuccessRatio", "The current success ratio.")); Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.")); Parameters.Add(new LookupParameter<DoubleValue>("SelectionPressure", "The actual selection pressure.")); Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.")); Parameters.Add(new ValueLookupParameter<BoolValue>("OffspringSelectionBeforeMutation", "True if the offspring selection step should be applied before mutation, false if it should be applied after mutation.")); Parameters.Add(new ValueLookupParameter<BoolValue>("FillPopulationWithParents", "True if the population should be filled with parent individual or false if worse children should be used when the maximum selection pressure is exceeded.")); #endregion #region Create operators Placeholder selector = new Placeholder(); SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor(); ChildrenCreator childrenCreator = new ChildrenCreator(); ConditionalBranch osBeforeMutationBranch = new ConditionalBranch(); UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor(); Placeholder crossover1 = new Placeholder(); UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor(); Placeholder evaluator1 = new Placeholder(); SubScopesCounter subScopesCounter1 = new SubScopesCounter(); WeightedParentsQualityComparator qualityComparer1 = new WeightedParentsQualityComparator(); SubScopesRemover subScopesRemover1 = new SubScopesRemover(); UniformSubScopesProcessor uniformSubScopesProcessor3 = new UniformSubScopesProcessor(); StochasticBranch mutationBranch1 = new StochasticBranch(); Placeholder mutator1 = new Placeholder(); VariableCreator variableCreator1 = new VariableCreator(); VariableCreator variableCreator2 = new VariableCreator(); ConditionalSelector conditionalSelector = new ConditionalSelector(); SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor(); UniformSubScopesProcessor uniformSubScopesProcessor4 = new UniformSubScopesProcessor(); Placeholder evaluator2 = new Placeholder(); SubScopesCounter subScopesCounter2 = new SubScopesCounter(); MergingReducer mergingReducer1 = new MergingReducer(); UniformSubScopesProcessor uniformSubScopesProcessor5 = new UniformSubScopesProcessor(); Placeholder crossover2 = new Placeholder(); StochasticBranch mutationBranch2 = new StochasticBranch(); Placeholder mutator2 = new Placeholder(); UniformSubScopesProcessor uniformSubScopesProcessor6 = new UniformSubScopesProcessor(); Placeholder evaluator3 = new Placeholder(); SubScopesCounter subScopesCounter3 = new SubScopesCounter(); WeightedParentsQualityComparator qualityComparer2 = new WeightedParentsQualityComparator(); SubScopesRemover subScopesRemover2 = new SubScopesRemover(); OffspringSelector offspringSelector = new OffspringSelector(); SubScopesProcessor subScopesProcessor3 = new SubScopesProcessor(); BestSelector bestSelector = new BestSelector(); WorstSelector worstSelector = new WorstSelector(); RightReducer rightReducer = new RightReducer(); LeftReducer leftReducer = new LeftReducer(); MergingReducer mergingReducer2 = new MergingReducer(); ConditionalBranch reevaluateElitesBranch = new ConditionalBranch(); UniformSubScopesProcessor uniformSubScopesProcessor7 = new UniformSubScopesProcessor(); Placeholder evaluator4 = new Placeholder(); SubScopesCounter subScopesCounter4 = new SubScopesCounter(); selector.Name = "Selector (placeholder)"; selector.OperatorParameter.ActualName = SelectorParameter.Name; childrenCreator.ParentsPerChild = new IntValue(2); osBeforeMutationBranch.Name = "Apply OS before mutation?"; osBeforeMutationBranch.ConditionParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name; crossover1.Name = "Crossover (placeholder)"; crossover1.OperatorParameter.ActualName = CrossoverParameter.Name; uniformSubScopesProcessor2.Parallel.Value = true; evaluator1.Name = "Evaluator (placeholder)"; evaluator1.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter1.Name = "Increment EvaluatedSolutions"; subScopesCounter1.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; qualityComparer1.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name; qualityComparer1.LeftSideParameter.ActualName = QualityParameter.Name; qualityComparer1.MaximizationParameter.ActualName = MaximizationParameter.Name; qualityComparer1.RightSideParameter.ActualName = QualityParameter.Name; qualityComparer1.ResultParameter.ActualName = "SuccessfulOffspring"; subScopesRemover1.RemoveAllSubScopes = true; mutationBranch1.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name; mutationBranch1.RandomParameter.ActualName = RandomParameter.Name; mutator1.Name = "Mutator (placeholder)"; mutator1.OperatorParameter.ActualName = MutatorParameter.Name; variableCreator1.Name = "MutatedOffspring = true"; variableCreator1.CollectedValues.Add(new ValueParameter<BoolValue>("MutatedOffspring", null, new BoolValue(true), false)); variableCreator2.Name = "MutatedOffspring = false"; variableCreator2.CollectedValues.Add(new ValueParameter<BoolValue>("MutatedOffspring", null, new BoolValue(false), false)); conditionalSelector.ConditionParameter.ActualName = "MutatedOffspring"; conditionalSelector.ConditionParameter.Depth = 1; conditionalSelector.CopySelected.Value = false; uniformSubScopesProcessor4.Parallel.Value = true; evaluator2.Name = "Evaluator (placeholder)"; evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter2.Name = "Increment EvaluatedSolutions"; subScopesCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; crossover2.Name = "Crossover (placeholder)"; crossover2.OperatorParameter.ActualName = CrossoverParameter.Name; mutationBranch2.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name; mutationBranch2.RandomParameter.ActualName = RandomParameter.Name; mutator2.Name = "Mutator (placeholder)"; mutator2.OperatorParameter.ActualName = MutatorParameter.Name; uniformSubScopesProcessor6.Parallel.Value = true; evaluator3.Name = "Evaluator (placeholder)"; evaluator3.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter3.Name = "Increment EvaluatedSolutions"; subScopesCounter3.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; qualityComparer2.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name; qualityComparer2.LeftSideParameter.ActualName = QualityParameter.Name; qualityComparer2.MaximizationParameter.ActualName = MaximizationParameter.Name; qualityComparer2.RightSideParameter.ActualName = QualityParameter.Name; qualityComparer2.ResultParameter.ActualName = "SuccessfulOffspring"; subScopesRemover2.RemoveAllSubScopes = true; offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name; offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name; offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name; offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name; offspringSelector.OffspringPopulationParameter.ActualName = "OffspringPopulation"; offspringSelector.OffspringPopulationWinnersParameter.ActualName = "OffspringPopulationWinners"; offspringSelector.SuccessfulOffspringParameter.ActualName = "SuccessfulOffspring"; offspringSelector.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name; bestSelector.CopySelected = new BoolValue(false); bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name; bestSelector.QualityParameter.ActualName = QualityParameter.Name; worstSelector.CopySelected = new BoolValue(false); worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name; worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name; worstSelector.QualityParameter.ActualName = QualityParameter.Name; reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites"; reevaluateElitesBranch.Name = "Reevaluate elites ?"; uniformSubScopesProcessor7.Parallel.Value = true; evaluator4.Name = "Evaluator (placeholder)"; evaluator4.OperatorParameter.ActualName = EvaluatorParameter.Name; subScopesCounter4.Name = "Increment EvaluatedSolutions"; subScopesCounter4.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name; #endregion #region Create operator graph OperatorGraph.InitialOperator = selector; selector.Successor = subScopesProcessor1; subScopesProcessor1.Operators.Add(new EmptyOperator()); subScopesProcessor1.Operators.Add(childrenCreator); subScopesProcessor1.Successor = offspringSelector; childrenCreator.Successor = osBeforeMutationBranch; osBeforeMutationBranch.TrueBranch = uniformSubScopesProcessor1; osBeforeMutationBranch.FalseBranch = uniformSubScopesProcessor5; osBeforeMutationBranch.Successor = null; uniformSubScopesProcessor1.Operator = crossover1; uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2; crossover1.Successor = null; uniformSubScopesProcessor2.Operator = evaluator1; uniformSubScopesProcessor2.Successor = subScopesCounter1; evaluator1.Successor = qualityComparer1; qualityComparer1.Successor = subScopesRemover1; subScopesRemover1.Successor = null; subScopesCounter1.Successor = uniformSubScopesProcessor3; uniformSubScopesProcessor3.Operator = mutationBranch1; uniformSubScopesProcessor3.Successor = conditionalSelector; mutationBranch1.FirstBranch = mutator1; mutationBranch1.SecondBranch = variableCreator2; mutationBranch1.Successor = null; mutator1.Successor = variableCreator1; variableCreator1.Successor = null; variableCreator2.Successor = null; conditionalSelector.Successor = subScopesProcessor2; subScopesProcessor2.Operators.Add(new EmptyOperator()); subScopesProcessor2.Operators.Add(uniformSubScopesProcessor4); subScopesProcessor2.Successor = mergingReducer1; uniformSubScopesProcessor4.Operator = evaluator2; uniformSubScopesProcessor4.Successor = subScopesCounter2; evaluator2.Successor = null; subScopesCounter2.Successor = null; mergingReducer1.Successor = null; uniformSubScopesProcessor5.Operator = crossover2; uniformSubScopesProcessor5.Successor = uniformSubScopesProcessor6; crossover2.Successor = mutationBranch2; mutationBranch2.FirstBranch = mutator2; mutationBranch2.SecondBranch = null; mutationBranch2.Successor = null; mutator2.Successor = null; uniformSubScopesProcessor6.Operator = evaluator3; uniformSubScopesProcessor6.Successor = subScopesCounter3; evaluator3.Successor = qualityComparer2; qualityComparer2.Successor = subScopesRemover2; subScopesRemover2.Successor = null; subScopesCounter3.Successor = null; offspringSelector.OffspringCreator = selector; offspringSelector.Successor = subScopesProcessor3; subScopesProcessor3.Operators.Add(bestSelector); subScopesProcessor3.Operators.Add(worstSelector); subScopesProcessor3.Successor = mergingReducer2; bestSelector.Successor = rightReducer; rightReducer.Successor = reevaluateElitesBranch; reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor7; uniformSubScopesProcessor7.Operator = evaluator4; uniformSubScopesProcessor7.Successor = subScopesCounter4; subScopesCounter4.Successor = null; reevaluateElitesBranch.FalseBranch = null; reevaluateElitesBranch.Successor = null; worstSelector.Successor = leftReducer; leftReducer.Successor = null; mergingReducer2.Successor = null; #endregion }