#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 System; using System.Linq; using HEAL.Attic; 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.PluginInfrastructure; using HeuristicLab.Random; namespace HeuristicLab.Algorithms.GeneticAlgorithm { /// /// An island genetic algorithm. /// [Item("Island Genetic Algorithm (Island-GA)", "An island genetic algorithm.")] [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 110)] [StorableType("C36FD509-4EF2-4BA7-9483-8CFCEF7EDA91")] public sealed class IslandGeneticAlgorithm : 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 NumberOfIslandsParameter { get { return (ValueParameter)Parameters["NumberOfIslands"]; } } private ValueParameter MigrationIntervalParameter { get { return (ValueParameter)Parameters["MigrationInterval"]; } } private ValueParameter MigrationRateParameter { get { return (ValueParameter)Parameters["MigrationRate"]; } } public IConstrainedValueParameter MigratorParameter { get { return (IConstrainedValueParameter)Parameters["Migrator"]; } } public IConstrainedValueParameter EmigrantsSelectorParameter { get { return (IConstrainedValueParameter)Parameters["EmigrantsSelector"]; } } public IConstrainedValueParameter ImmigrationReplacerParameter { get { return (IConstrainedValueParameter)Parameters["ImmigrationReplacer"]; } } private ValueParameter PopulationSizeParameter { get { return (ValueParameter)Parameters["PopulationSize"]; } } private ValueParameter MaximumGenerationsParameter { get { return (ValueParameter)Parameters["MaximumGenerations"]; } } public IConstrainedValueParameter SelectorParameter { get { return (IConstrainedValueParameter)Parameters["Selector"]; } } public IConstrainedValueParameter CrossoverParameter { get { return (IConstrainedValueParameter)Parameters["Crossover"]; } } private ValueParameter MutationProbabilityParameter { get { return (ValueParameter)Parameters["MutationProbability"]; } } public IConstrainedValueParameter MutatorParameter { get { return (IConstrainedValueParameter)Parameters["Mutator"]; } } private ValueParameter ElitesParameter { get { return (ValueParameter)Parameters["Elites"]; } } private IFixedValueParameter ReevaluateElitesParameter { get { return (IFixedValueParameter)Parameters["ReevaluateElites"]; } } private ValueParameter AnalyzerParameter { get { return (ValueParameter)Parameters["Analyzer"]; } } private ValueParameter IslandAnalyzerParameter { get { return (ValueParameter)Parameters["IslandAnalyzer"]; } } #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 NumberOfIslands { get { return NumberOfIslandsParameter.Value; } set { NumberOfIslandsParameter.Value = value; } } public IntValue MigrationInterval { get { return MigrationIntervalParameter.Value; } set { MigrationIntervalParameter.Value = value; } } public PercentValue MigrationRate { get { return MigrationRateParameter.Value; } set { MigrationRateParameter.Value = value; } } public IMigrator Migrator { get { return MigratorParameter.Value; } set { MigratorParameter.Value = value; } } public ISelector EmigrantsSelector { get { return EmigrantsSelectorParameter.Value; } set { EmigrantsSelectorParameter.Value = value; } } public IReplacer ImmigrationReplacer { get { return ImmigrationReplacerParameter.Value; } set { ImmigrationReplacerParameter.Value = value; } } public IntValue PopulationSize { get { return PopulationSizeParameter.Value; } set { PopulationSizeParameter.Value = value; } } public IntValue MaximumGenerations { get { return MaximumGenerationsParameter.Value; } set { MaximumGenerationsParameter.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 bool ReevaluteElites { get { return ReevaluateElitesParameter.Value.Value; } set { ReevaluateElitesParameter.Value.Value = value; } } public MultiAnalyzer Analyzer { get { return AnalyzerParameter.Value; } set { AnalyzerParameter.Value = value; } } public MultiAnalyzer IslandAnalyzer { get { return IslandAnalyzerParameter.Value; } set { IslandAnalyzerParameter.Value = value; } } private RandomCreator RandomCreator { get { return (RandomCreator)OperatorGraph.InitialOperator; } } private UniformSubScopesProcessor IslandProcessor { get { return OperatorGraph.Iterate().OfType().First(x => x.Operator is SolutionsCreator); } } private SolutionsCreator SolutionsCreator { get { return (SolutionsCreator)IslandProcessor.Operator; } } private IslandGeneticAlgorithmMainLoop MainLoop { get { return FindMainLoop(IslandProcessor.Successor); } } [Storable] private BestAverageWorstQualityAnalyzer islandQualityAnalyzer; [Storable] private BestAverageWorstQualityAnalyzer qualityAnalyzer; #endregion [StorableConstructor] private IslandGeneticAlgorithm(StorableConstructorFlag _) : base(_) { } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { // BackwardsCompatibility3.3 #region Backwards compatible code, remove with 3.4 if (!Parameters.ContainsKey("ReevaluateElites")) { Parameters.Add(new FixedValueParameter("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", (BoolValue)new BoolValue(false).AsReadOnly()) { Hidden = true }); } var optionalMutatorParameter = MutatorParameter as OptionalConstrainedValueParameter; if (optionalMutatorParameter != null) { Parameters.Remove(optionalMutatorParameter); Parameters.Add(new ConstrainedValueParameter("Mutator", "The operator used to mutate solutions.")); foreach (var m in optionalMutatorParameter.ValidValues) MutatorParameter.ValidValues.Add(m); if (optionalMutatorParameter.Value == null) MutationProbability.Value = 0; // to guarantee that the old configuration results in the same behavior else Mutator = optionalMutatorParameter.Value; optionalMutatorParameter.ValidValues.Clear(); // to avoid dangling references to the old parameter its valid values are cleared } #endregion Initialize(); } private IslandGeneticAlgorithm(IslandGeneticAlgorithm original, Cloner cloner) : base(original, cloner) { islandQualityAnalyzer = cloner.Clone(original.islandQualityAnalyzer); qualityAnalyzer = cloner.Clone(original.qualityAnalyzer); Initialize(); } public override IDeepCloneable Clone(Cloner cloner) { return new IslandGeneticAlgorithm(this, cloner); } public IslandGeneticAlgorithm() : 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("NumberOfIslands", "The number of islands.", new IntValue(5))); Parameters.Add(new ValueParameter("MigrationInterval", "The number of generations that should pass between migration phases.", new IntValue(20))); Parameters.Add(new ValueParameter("MigrationRate", "The proportion of individuals that should migrate between the islands.", new PercentValue(0.15))); Parameters.Add(new ConstrainedValueParameter("Migrator", "The migration strategy.")); Parameters.Add(new ConstrainedValueParameter("EmigrantsSelector", "Selects the individuals that will be migrated.")); Parameters.Add(new ConstrainedValueParameter("ImmigrationReplacer", "Selects the population from the unification of the original population and the immigrants.")); Parameters.Add(new ValueParameter("PopulationSize", "The size of the population of solutions.", new IntValue(100))); Parameters.Add(new ValueParameter("MaximumGenerations", "The maximum number of generations that should be processed.", new IntValue(1000))); 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 ConstrainedValueParameter("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 FixedValueParameter("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("Analyzer", "The operator used to analyze the islands.", new MultiAnalyzer())); Parameters.Add(new ValueParameter("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("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("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().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().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name)) EmigrantsSelectorParameter.ValidValues.Add(selector); foreach (IReplacer replacer in ApplicationManager.Manager.GetInstances().OrderBy(x => x.Name)) ImmigrationReplacerParameter.ValidValues.Add(replacer); ParameterizeSelectors(); foreach (IMigrator migrator in ApplicationManager.Manager.GetInstances().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(); } public override void Prepare() { if (Problem != null) base.Prepare(); } #region Events protected override void OnProblemChanged() { ParameterizeStochasticOperator(Problem.SolutionCreator); foreach (IOperator op in Problem.Operators.OfType()) ParameterizeStochasticOperator(op); ParameterizeStochasticOperatorForIsland(Problem.Evaluator); ParameterizeSolutionsCreator(); ParameterizeMainLoop(); 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) { ParameterizeStochasticOperatorForIsland(Problem.Evaluator); ParameterizeSolutionsCreator(); ParameterizeMainLoop(); 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.OfType()) ParameterizeStochasticOperator(op); ParameterizeStochasticOperatorForIsland(Problem.Evaluator); 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) { NumberOfIslands.ValueChanged += new EventHandler(PopulationSize_ValueChanged); ParameterizeSelectors(); } private void PopulationSize_ValueChanged(object sender, EventArgs e) { ParameterizeSelectors(); } private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) { ParameterizeMainLoop(); ParameterizeSelectors(); ParameterizeAnalyzers(); } private void MigrationRateParameter_ValueChanged(object sender, EventArgs e) { MigrationRate.ValueChanged += new EventHandler(MigrationRate_ValueChanged); ParameterizeSelectors(); } private void MigrationRate_ValueChanged(object sender, EventArgs e) { ParameterizeSelectors(); } #endregion #region Helpers private void Initialize() { PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged); PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged); MigrationRateParameter.ValueChanged += new EventHandler(MigrationRateParameter_ValueChanged); MigrationRate.ValueChanged += new EventHandler(MigrationRate_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 ParameterizeMainLoop() { MainLoop.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name; MainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name; MainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name; MainLoop.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 ParameterizeStochasticOperatorForIsland(IOperator op) { IStochasticOperator stochasticOp = op as IStochasticOperator; if (stochasticOp != null) { stochasticOp.RandomParameter.ActualName = "LocalRandom"; stochasticOp.RandomParameter.Hidden = true; } } private void ParameterizeSelectors() { foreach (ISelector selector in SelectorParameter.ValidValues) { selector.CopySelected = new BoolValue(true); selector.NumberOfSelectedSubScopesParameter.Value = new IntValue(2 * (PopulationSize.Value - Elites.Value)); selector.NumberOfSelectedSubScopesParameter.Hidden = true; ParameterizeStochasticOperatorForIsland(selector); } foreach (ISelector selector in EmigrantsSelectorParameter.ValidValues) { selector.CopySelected = new BoolValue(true); selector.NumberOfSelectedSubScopesParameter.Value = new IntValue((int)Math.Ceiling(PopulationSize.Value * MigrationRate.Value)); selector.NumberOfSelectedSubScopesParameter.Hidden = true; ParameterizeStochasticOperator(selector); } foreach (IReplacer replacer in ImmigrationReplacerParameter.ValidValues) { ParameterizeStochasticOperator(replacer); } 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; } foreach (ISingleObjectiveSelector selector in EmigrantsSelectorParameter.ValidValues.OfType()) { selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name; selector.MaximizationParameter.Hidden = true; selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName; selector.QualityParameter.Hidden = true; } foreach (ISingleObjectiveReplacer selector in ImmigrationReplacerParameter.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() { islandQualityAnalyzer.ResultsParameter.ActualName = "Results"; islandQualityAnalyzer.ResultsParameter.Hidden = true; islandQualityAnalyzer.QualityParameter.Depth = 1; qualityAnalyzer.ResultsParameter.ActualName = "Results"; qualityAnalyzer.ResultsParameter.Hidden = true; qualityAnalyzer.QualityParameter.Depth = 2; if (Problem != null) { islandQualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name; islandQualityAnalyzer.MaximizationParameter.Hidden = true; islandQualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName; islandQualityAnalyzer.QualityParameter.Hidden = true; islandQualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name; islandQualityAnalyzer.BestKnownQualityParameter.Hidden = true; qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name; qualityAnalyzer.MaximizationParameter.Hidden = true; qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName; 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; ICrossover defaultCrossover = Problem.Operators.OfType().FirstOrDefault(); CrossoverParameter.ValidValues.Clear(); foreach (ICrossover crossover in Problem.Operators.OfType().OrderBy(x => x.Name)) { ParameterizeStochasticOperatorForIsland(crossover); 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(); IManipulator defaultMutator = Problem.Operators.OfType().FirstOrDefault(); foreach (IManipulator mutator in Problem.Operators.OfType().OrderBy(x => x.Name)) { ParameterizeStochasticOperatorForIsland(mutator); MutatorParameter.ValidValues.Add(mutator); } if (oldMutator != null) { IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType()); if (mutator != null) MutatorParameter.Value = mutator; else oldMutator = null; } if (oldMutator == null && defaultMutator != null) MutatorParameter.Value = defaultMutator; } private void UpdateAnalyzers() { IslandAnalyzer.Operators.Clear(); Analyzer.Operators.Clear(); IslandAnalyzer.Operators.Add(islandQualityAnalyzer, islandQualityAnalyzer.EnabledByDefault); if (Problem != null) { foreach (IAnalyzer analyzer in Problem.Operators.OfType()) { foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType()) param.Depth = 2; Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault); } } Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault); } private IslandGeneticAlgorithmMainLoop FindMainLoop(IOperator start) { IOperator mainLoop = start; while (mainLoop != null && !(mainLoop is IslandGeneticAlgorithmMainLoop)) mainLoop = ((SingleSuccessorOperator)mainLoop).Successor; if (mainLoop == null) return null; else return (IslandGeneticAlgorithmMainLoop)mainLoop; } #endregion } }