#region License Information /* HeuristicLab * Copyright (C) 2002-2012 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 System; using System.Linq; using HeuristicLab.Analysis; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Operators; using HeuristicLab.Optimization; using HeuristicLab.Optimization.Operators; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.PluginInfrastructure; using HeuristicLab.Random; namespace HeuristicLab.Algorithms.GeneticAlgorithm { /// /// A genetic algorithm. /// [Item("Genetic Algorithm", "A genetic algorithm.")] [Creatable("Algorithms")] [StorableClass] public sealed class GeneticAlgorithm : HeuristicOptimizationEngineAlgorithm, IStorableContent { public string Filename { get; set; } #region Problem Properties public override Type ProblemType { get { return typeof(ISingleObjectiveHeuristicOptimizationProblem); } } public new ISingleObjectiveHeuristicOptimizationProblem Problem { get { return (ISingleObjectiveHeuristicOptimizationProblem)base.Problem; } set { base.Problem = value; } } #endregion #region Parameter Properties private ValueParameter SeedParameter { get { return (ValueParameter)Parameters["Seed"]; } } private ValueParameter SetSeedRandomlyParameter { get { return (ValueParameter)Parameters["SetSeedRandomly"]; } } private ValueParameter PopulationSizeParameter { get { return (ValueParameter)Parameters["PopulationSize"]; } } public ConstrainedValueParameter SelectorParameter { get { return (ConstrainedValueParameter)Parameters["Selector"]; } } public ConstrainedValueParameter CrossoverParameter { get { return (ConstrainedValueParameter)Parameters["Crossover"]; } } private ValueParameter MutationProbabilityParameter { get { return (ValueParameter)Parameters["MutationProbability"]; } } public OptionalConstrainedValueParameter MutatorParameter { get { return (OptionalConstrainedValueParameter)Parameters["Mutator"]; } } private ValueParameter ElitesParameter { get { return (ValueParameter)Parameters["Elites"]; } } private ValueParameter AnalyzerParameter { get { return (ValueParameter)Parameters["Analyzer"]; } } private ValueParameter MaximumGenerationsParameter { get { return (ValueParameter)Parameters["MaximumGenerations"]; } } #endregion #region Properties public IntValue Seed { get { return SeedParameter.Value; } set { SeedParameter.Value = value; } } public BoolValue SetSeedRandomly { get { return SetSeedRandomlyParameter.Value; } set { SetSeedRandomlyParameter.Value = value; } } public IntValue PopulationSize { get { return PopulationSizeParameter.Value; } set { PopulationSizeParameter.Value = value; } } public ISelector Selector { get { return SelectorParameter.Value; } set { SelectorParameter.Value = value; } } public ICrossover Crossover { get { return CrossoverParameter.Value; } set { CrossoverParameter.Value = value; } } public PercentValue MutationProbability { get { return MutationProbabilityParameter.Value; } set { MutationProbabilityParameter.Value = value; } } public IManipulator Mutator { get { return MutatorParameter.Value; } set { MutatorParameter.Value = value; } } public IntValue Elites { get { return ElitesParameter.Value; } set { ElitesParameter.Value = value; } } public MultiAnalyzer Analyzer { get { return AnalyzerParameter.Value; } set { AnalyzerParameter.Value = value; } } public IntValue MaximumGenerations { get { return MaximumGenerationsParameter.Value; } set { MaximumGenerationsParameter.Value = value; } } private RandomCreator RandomCreator { get { return (RandomCreator)OperatorGraph.InitialOperator; } } private SolutionsCreator SolutionsCreator { get { return (SolutionsCreator)RandomCreator.Successor; } } private GeneticAlgorithmMainLoop GeneticAlgorithmMainLoop { get { return FindMainLoop(SolutionsCreator.Successor); } } [Storable] private BestAverageWorstQualityAnalyzer qualityAnalyzer; #endregion public GeneticAlgorithm() : base() { Parameters.Add(new ValueParameter("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0))); Parameters.Add(new ValueParameter("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true))); Parameters.Add(new ValueParameter("PopulationSize", "The size of the population of solutions.", new IntValue(100))); Parameters.Add(new ConstrainedValueParameter("Selector", "The operator used to select solutions for reproduction.")); Parameters.Add(new ConstrainedValueParameter("Crossover", "The operator used to cross solutions.")); Parameters.Add(new ValueParameter("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05))); Parameters.Add(new OptionalConstrainedValueParameter("Mutator", "The operator used to mutate solutions.")); Parameters.Add(new ValueParameter("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1))); Parameters.Add(new ValueParameter("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer())); Parameters.Add(new ValueParameter("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("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.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().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(); } [StorableConstructor] private GeneticAlgorithm(bool deserializing) : base(deserializing) { } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { Initialize(); } private GeneticAlgorithm(GeneticAlgorithm original, Cloner cloner) : base(original, cloner) { qualityAnalyzer = cloner.Clone(original.qualityAnalyzer); Initialize(); } public override IDeepCloneable Clone(Cloner cloner) { return new GeneticAlgorithm(this, cloner); } public override void Prepare() { if (Problem != null) base.Prepare(); } #region Events protected override void OnProblemChanged() { ParameterizeStochasticOperator(Problem.SolutionCreator); ParameterizeStochasticOperator(Problem.Evaluator); foreach (IOperator op in Problem.Operators.OfType()) ParameterizeStochasticOperator(op); ParameterizeSolutionsCreator(); ParameterizeGeneticAlgorithmMainLoop(); ParameterizeSelectors(); ParameterizeAnalyzers(); ParameterizeIterationBasedOperators(); UpdateCrossovers(); UpdateMutators(); UpdateAnalyzers(); Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged); base.OnProblemChanged(); } protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) { ParameterizeStochasticOperator(Problem.SolutionCreator); ParameterizeSolutionsCreator(); base.Problem_SolutionCreatorChanged(sender, e); } protected override void Problem_EvaluatorChanged(object sender, EventArgs e) { ParameterizeStochasticOperator(Problem.Evaluator); ParameterizeSolutionsCreator(); ParameterizeGeneticAlgorithmMainLoop(); ParameterizeSelectors(); ParameterizeAnalyzers(); Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged); base.Problem_EvaluatorChanged(sender, e); } protected override void Problem_OperatorsChanged(object sender, EventArgs e) { foreach (IOperator op in Problem.Operators) ParameterizeStochasticOperator(op); ParameterizeIterationBasedOperators(); UpdateCrossovers(); UpdateMutators(); UpdateAnalyzers(); base.Problem_OperatorsChanged(sender, e); } private void ElitesParameter_ValueChanged(object sender, EventArgs e) { Elites.ValueChanged += new EventHandler(Elites_ValueChanged); ParameterizeSelectors(); } private void Elites_ValueChanged(object sender, EventArgs e) { ParameterizeSelectors(); } private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) { PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged); ParameterizeSelectors(); } private void PopulationSize_ValueChanged(object sender, EventArgs e) { ParameterizeSelectors(); } private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) { ParameterizeGeneticAlgorithmMainLoop(); ParameterizeSelectors(); ParameterizeAnalyzers(); } #endregion #region Helpers private void Initialize() { PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged); PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged); ElitesParameter.ValueChanged += new EventHandler(ElitesParameter_ValueChanged); Elites.ValueChanged += new EventHandler(Elites_ValueChanged); if (Problem != null) { Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged); } } private void ParameterizeSolutionsCreator() { SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name; SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name; } private void ParameterizeGeneticAlgorithmMainLoop() { GeneticAlgorithmMainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name; GeneticAlgorithmMainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name; GeneticAlgorithmMainLoop.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName; } private void ParameterizeStochasticOperator(IOperator op) { IStochasticOperator stochasticOp = op as IStochasticOperator; if (stochasticOp != null) { stochasticOp.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName; stochasticOp.RandomParameter.Hidden = true; } } private void ParameterizeSelectors() { foreach (ISelector selector in SelectorParameter.ValidValues) { selector.CopySelected = new BoolValue(true); selector.NumberOfSelectedSubScopesParameter.Value = new IntValue(2 * (PopulationSizeParameter.Value.Value - ElitesParameter.Value.Value)); selector.NumberOfSelectedSubScopesParameter.Hidden = true; ParameterizeStochasticOperator(selector); } if (Problem != null) { foreach (ISingleObjectiveSelector selector in SelectorParameter.ValidValues.OfType()) { selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name; selector.MaximizationParameter.Hidden = true; selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName; selector.QualityParameter.Hidden = true; } } } private void ParameterizeAnalyzers() { qualityAnalyzer.ResultsParameter.ActualName = "Results"; qualityAnalyzer.ResultsParameter.Hidden = true; if (Problem != null) { qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name; qualityAnalyzer.MaximizationParameter.Hidden = true; qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName; qualityAnalyzer.QualityParameter.Depth = 1; qualityAnalyzer.QualityParameter.Hidden = true; qualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name; qualityAnalyzer.BestKnownQualityParameter.Hidden = true; } } private void ParameterizeIterationBasedOperators() { if (Problem != null) { foreach (IIterationBasedOperator op in Problem.Operators.OfType()) { op.IterationsParameter.ActualName = "Generations"; op.IterationsParameter.Hidden = true; op.MaximumIterationsParameter.ActualName = "MaximumGenerations"; op.MaximumIterationsParameter.Hidden = true; } } } private void UpdateCrossovers() { ICrossover oldCrossover = CrossoverParameter.Value; CrossoverParameter.ValidValues.Clear(); ICrossover defaultCrossover = Problem.Operators.OfType().FirstOrDefault(); foreach (ICrossover crossover in Problem.Operators.OfType().OrderBy(x => x.Name)) CrossoverParameter.ValidValues.Add(crossover); if (oldCrossover != null) { ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType()); if (crossover != null) CrossoverParameter.Value = crossover; else oldCrossover = null; } if (oldCrossover == null && defaultCrossover != null) CrossoverParameter.Value = defaultCrossover; } private void UpdateMutators() { IManipulator oldMutator = MutatorParameter.Value; MutatorParameter.ValidValues.Clear(); foreach (IManipulator mutator in Problem.Operators.OfType().OrderBy(x => x.Name)) MutatorParameter.ValidValues.Add(mutator); if (oldMutator != null) { IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType()); if (mutator != null) MutatorParameter.Value = mutator; } } private void UpdateAnalyzers() { Analyzer.Operators.Clear(); if (Problem != null) { foreach (IAnalyzer analyzer in Problem.Operators.OfType()) { foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType()) param.Depth = 1; Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault); } } Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault); } private GeneticAlgorithmMainLoop FindMainLoop(IOperator start) { IOperator mainLoop = start; while (mainLoop != null && !(mainLoop is GeneticAlgorithmMainLoop)) mainLoop = ((SingleSuccessorOperator)mainLoop).Successor; if (mainLoop == null) return null; else return (GeneticAlgorithmMainLoop)mainLoop; } #endregion } }