#region License Information /* HeuristicLab * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Operators; using HeuristicLab.Optimization.Operators; using HeuristicLab.Parameters; using HEAL.Attic; using HeuristicLab.Selection; namespace HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm { /// /// An operator which represents the main loop of an offspring selection genetic algorithm. /// [Item("OffspringSelectionGeneticAlgorithmMainOperator", "An operator that represents the core of an offspring selection genetic algorithm.")] [StorableType("43910E64-FC79-4AFF-8049-F427442E32BF")] public sealed class OffspringSelectionGeneticAlgorithmMainOperator : AlgorithmOperator { #region Parameter properties public ValueLookupParameter RandomParameter { get { return (ValueLookupParameter)Parameters["Random"]; } } public ValueLookupParameter MaximizationParameter { get { return (ValueLookupParameter)Parameters["Maximization"]; } } public ScopeTreeLookupParameter QualityParameter { get { return (ScopeTreeLookupParameter)Parameters["Quality"]; } } public ValueLookupParameter SelectorParameter { get { return (ValueLookupParameter)Parameters["Selector"]; } } public ValueLookupParameter CrossoverParameter { get { return (ValueLookupParameter)Parameters["Crossover"]; } } public ValueLookupParameter MutationProbabilityParameter { get { return (ValueLookupParameter)Parameters["MutationProbability"]; } } public ValueLookupParameter MutatorParameter { get { return (ValueLookupParameter)Parameters["Mutator"]; } } public ValueLookupParameter EvaluatorParameter { get { return (ValueLookupParameter)Parameters["Evaluator"]; } } public LookupParameter EvaluatedSolutionsParameter { get { return (LookupParameter)Parameters["EvaluatedSolutions"]; } } public ValueLookupParameter ElitesParameter { get { return (ValueLookupParameter)Parameters["Elites"]; } } public IValueLookupParameter ReevaluateElitesParameter { get { return (IValueLookupParameter)Parameters["ReevaluateElites"]; } } public LookupParameter ComparisonFactorParameter { get { return (LookupParameter)Parameters["ComparisonFactor"]; } } public LookupParameter CurrentSuccessRatioParameter { get { return (LookupParameter)Parameters["CurrentSuccessRatio"]; } } public ValueLookupParameter SuccessRatioParameter { get { return (ValueLookupParameter)Parameters["SuccessRatio"]; } } public LookupParameter SelectionPressureParameter { get { return (LookupParameter)Parameters["SelectionPressure"]; } } public ValueLookupParameter MaximumSelectionPressureParameter { get { return (ValueLookupParameter)Parameters["MaximumSelectionPressure"]; } } public ValueLookupParameter OffspringSelectionBeforeMutationParameter { get { return (ValueLookupParameter)Parameters["OffspringSelectionBeforeMutation"]; } } public IValueLookupParameter FillPopulationWithParentsParameter { get { return (IValueLookupParameter)Parameters["FillPopulationWithParents"]; } } #endregion [StorableConstructor] private OffspringSelectionGeneticAlgorithmMainOperator(StorableConstructorFlag _) : base(_) { } private OffspringSelectionGeneticAlgorithmMainOperator(OffspringSelectionGeneticAlgorithmMainOperator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new OffspringSelectionGeneticAlgorithmMainOperator(this, cloner); } public OffspringSelectionGeneticAlgorithmMainOperator() : base() { Initialize(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { // BackwardsCompatibility3.3 #region Backwards compatible code, remove with 3.4 if (!Parameters.ContainsKey("ReevaluateElites")) { Parameters.Add(new ValueLookupParameter("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); } if (!Parameters.ContainsKey("FillPopulationWithParents")) Parameters.Add(new ValueLookupParameter("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 } private void Initialize() { #region Create parameters Parameters.Add(new ValueLookupParameter("Random", "A pseudo random number generator.")); Parameters.Add(new ValueLookupParameter("Maximization", "True if the problem is a maximization problem, otherwise false.")); Parameters.Add(new ScopeTreeLookupParameter("Quality", "The value which represents the quality of a solution.")); Parameters.Add(new ValueLookupParameter("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ValueLookupParameter("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueLookupParameter("MutationProbability", "The probability that the mutation operator is applied on a solution.")); Parameters.Add(new ValueLookupParameter("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueLookupParameter("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("EvaluatedSolutions", "The number of evaluated solutions.")); Parameters.Add(new ValueLookupParameter("Elites", "The numer of elite solutions which are kept in each generation.")); Parameters.Add(new ValueLookupParameter("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)")); Parameters.Add(new LookupParameter("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("CurrentSuccessRatio", "The current success ratio.")); Parameters.Add(new ValueLookupParameter("SuccessRatio", "The ratio of successful to total children that should be achieved.")); Parameters.Add(new LookupParameter("SelectionPressure", "The actual selection pressure.")); Parameters.Add(new ValueLookupParameter("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.")); Parameters.Add(new ValueLookupParameter("OffspringSelectionBeforeMutation", "True if the offspring selection step should be applied before mutation, false if it should be applied after mutation.")); Parameters.Add(new ValueLookupParameter("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("MutatedOffspring", null, new BoolValue(true), false)); variableCreator2.Name = "MutatedOffspring = false"; variableCreator2.CollectedValues.Add(new ValueParameter("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 override IOperation Apply() { if (CrossoverParameter.ActualValue == null) return null; return base.Apply(); } } }