source: trunk/sources/HeuristicLab.Algorithms.EvolutionStrategy/3.3/EvolutionStrategy.cs @ 14815

#region License Information

/* HeuristicLab

 * Copyright (C) 2002-2016 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 <http://www.gnu.org/licenses/>.

 */

#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.Random;



namespace HeuristicLab.Algorithms.EvolutionStrategy {

  [Item("Evolution Strategy (ES)", "An evolution strategy.")]

  [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 200)]

  [StorableClass]

  public sealed class EvolutionStrategy : 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<IntValue> SeedParameter {

      get { return (ValueParameter<IntValue>)Parameters["Seed"]; }

    }

    private ValueParameter<BoolValue> SetSeedRandomlyParameter {

      get { return (ValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }

    }

    private ValueParameter<IntValue> PopulationSizeParameter {

      get { return (ValueParameter<IntValue>)Parameters["PopulationSize"]; }

    }

    private ValueParameter<IntValue> ParentsPerChildParameter {

      get { return (ValueParameter<IntValue>)Parameters["ParentsPerChild"]; }

    }

    private ValueParameter<IntValue> ChildrenParameter {

      get { return (ValueParameter<IntValue>)Parameters["Children"]; }

    }

    private ValueParameter<IntValue> MaximumGenerationsParameter {

      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }

    }

    private ValueParameter<BoolValue> PlusSelectionParameter {

      get { return (ValueParameter<BoolValue>)Parameters["PlusSelection"]; }

    }

    private IFixedValueParameter<BoolValue> ReevaluateElitesParameter {

      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }

    }

    public IConstrainedValueParameter<IManipulator> MutatorParameter {

      get { return (IConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }

    }

    public IConstrainedValueParameter<ICrossover> RecombinatorParameter {

      get { return (IConstrainedValueParameter<ICrossover>)Parameters["Recombinator"]; }

    }

    private ValueParameter<MultiAnalyzer> AnalyzerParameter {

      get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }

    }

    public IConstrainedValueParameter<IStrategyParameterCreator> StrategyParameterCreatorParameter {

      get { return (IConstrainedValueParameter<IStrategyParameterCreator>)Parameters["StrategyParameterCreator"]; }

    }

    public IConstrainedValueParameter<IStrategyParameterCrossover> StrategyParameterCrossoverParameter {

      get { return (IConstrainedValueParameter<IStrategyParameterCrossover>)Parameters["StrategyParameterCrossover"]; }

    }

    public IConstrainedValueParameter<IStrategyParameterManipulator> StrategyParameterManipulatorParameter {

      get { return (IConstrainedValueParameter<IStrategyParameterManipulator>)Parameters["StrategyParameterManipulator"]; }

    }

    #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 IntValue ParentsPerChild {

      get { return ParentsPerChildParameter.Value; }

      set { ParentsPerChildParameter.Value = value; }

    }

    public IntValue Children {

      get { return ChildrenParameter.Value; }

      set { ChildrenParameter.Value = value; }

    }

    public IntValue MaximumGenerations {

      get { return MaximumGenerationsParameter.Value; }

      set { MaximumGenerationsParameter.Value = value; }

    }

    public BoolValue PlusSelection {

      get { return PlusSelectionParameter.Value; }

      set { PlusSelectionParameter.Value = value; }

    }

    public bool ReevaluteElites {

      get { return ReevaluateElitesParameter.Value.Value; }

      set { ReevaluateElitesParameter.Value.Value = value; }

    }

    public IManipulator Mutator {

      get { return MutatorParameter.Value; }

      set { MutatorParameter.Value = value; }

    }

    public ICrossover Recombinator {

      get { return RecombinatorParameter.Value; }

      set { RecombinatorParameter.Value = value; }

    }

    public MultiAnalyzer Analyzer {

      get { return AnalyzerParameter.Value; }

      set { AnalyzerParameter.Value = value; }

    }

    public IStrategyParameterCreator StrategyParameterCreator {

      get { return StrategyParameterCreatorParameter.Value; }

      set { StrategyParameterCreatorParameter.Value = value; }

    }

    public IStrategyParameterCrossover StrategyParameterCrossover {

      get { return StrategyParameterCrossoverParameter.Value; }

      set { StrategyParameterCrossoverParameter.Value = value; }

    }

    public IStrategyParameterManipulator StrategyParameterManipulator {

      get { return StrategyParameterManipulatorParameter.Value; }

      set { StrategyParameterManipulatorParameter.Value = value; }

    }



    private RandomCreator RandomCreator {

      get { return (RandomCreator)OperatorGraph.InitialOperator; }

    }

    private SolutionsCreator SolutionsCreator {

      get { return (SolutionsCreator)RandomCreator.Successor; }

    }

    private EvolutionStrategyMainLoop MainLoop {

      get { return FindMainLoop(SolutionsCreator.Successor); }

    }

    [Storable]

    private BestAverageWorstQualityAnalyzer qualityAnalyzer;

    #endregion



    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();

    }

    [StorableConstructor]

    private EvolutionStrategy(bool deserializing) : base(deserializing) { }

    [StorableHook(HookType.AfterDeserialization)]

    private void AfterDeserialization() {

      // BackwardsCompatibility3.3

      #region Backwards compatible code, remove with 3.4

      if (!Parameters.ContainsKey("ReevaluateElites")) {

        Parameters.Add(new FixedValueParameter<BoolValue>("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 });

      }

      #endregion



      Initialize();

    }



    private EvolutionStrategy(EvolutionStrategy original, Cloner cloner)

      : base(original, cloner) {

      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);

      Initialize();

    }

    public override IDeepCloneable Clone(Cloner cloner) {

      return new EvolutionStrategy(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<IOperator>()) ParameterizeStochasticOperator(op);

      ParameterizeSolutionsCreator();

      ParameterizeMainLoop();

      ParameterizeAnalyzers();

      ParameterizeIterationBasedOperators();

      UpdateRecombinators();

      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();

      ParameterizeMainLoop();

      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<IOperator>()) ParameterizeStochasticOperator(op);

      ParameterizeIterationBasedOperators();

      UpdateRecombinators();

      UpdateMutators();

      UpdateAnalyzers();

      base.Problem_OperatorsChanged(sender, e);

    }

    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {

      ParameterizeMainLoop();

      ParameterizeAnalyzers();

    }

    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {

      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);

      PopulationSize_ValueChanged(null, EventArgs.Empty);

    }

    private void PopulationSize_ValueChanged(object sender, EventArgs e) {

      if (PopulationSize.Value <= 0) PopulationSize.Value = 1;

      if (!PlusSelection.Value && Children.Value < PopulationSize.Value)

        Children.Value = PopulationSize.Value;

      if (PopulationSize.Value < ParentsPerChild.Value)

        ParentsPerChild.Value = PopulationSize.Value;

    }

    private void ParentsPerChildParameter_ValueChanged(object sender, EventArgs e) {

      ParentsPerChild.ValueChanged += new EventHandler(ParentsPerChild_ValueChanged);

      ParentsPerChild_ValueChanged(null, EventArgs.Empty);

    }

    private void ParentsPerChild_ValueChanged(object sender, EventArgs e) {

      if (ParentsPerChild.Value < 1 || ParentsPerChild.Value > 1 && RecombinatorParameter.ValidValues.Count == 0)

        ParentsPerChild.Value = 1;

      if (ParentsPerChild.Value > 1 && Recombinator == null) Recombinator = RecombinatorParameter.ValidValues.First();

      if (ParentsPerChild.Value > 1 && ParentsPerChild.Value > PopulationSize.Value)

        PopulationSize.Value = ParentsPerChild.Value;

    }

    private void ChildrenParameter_ValueChanged(object sender, EventArgs e) {

      Children.ValueChanged += new EventHandler(Children_ValueChanged);

      Children_ValueChanged(null, EventArgs.Empty);

    }

    private void Children_ValueChanged(object sender, EventArgs e) {

      if (Children.Value <= 0) Children.Value = 1;

      if (!PlusSelection.Value && Children.Value < PopulationSize.Value)

        PopulationSize.Value = Children.Value;

    }

    private void PlusSelectionParameter_ValueChanged(object sender, EventArgs e) {

      PlusSelection.ValueChanged += new EventHandler(PlusSelection_ValueChanged);

      PlusSelection_ValueChanged(null, EventArgs.Empty);

    }

    private void PlusSelection_ValueChanged(object sender, EventArgs e) {

      if (!PlusSelection.Value && Children.Value < PopulationSize.Value)

        Children.Value = PopulationSize.Value;

    }

    private void RecombinatorParameter_ValueChanged(object sender, EventArgs e) {

      if (Recombinator == null && ParentsPerChild.Value > 1) ParentsPerChild.Value = 1;

      else if (Recombinator != null && ParentsPerChild.Value == 1) ParentsPerChild.Value = 2;

      if (Recombinator != null && Mutator is ISelfAdaptiveManipulator && StrategyParameterCrossover == null) {

        if (StrategyParameterCrossoverParameter.ValidValues.Count > 0)

          StrategyParameterCrossover = StrategyParameterCrossoverParameter.ValidValues.First();

      }

    }

    private void MutatorParameter_ValueChanged(object sender, EventArgs e) {

      if (Mutator is ISelfAdaptiveManipulator) {

        UpdateStrategyParameterOperators();

      } else {

        StrategyParameterCreatorParameter.ValidValues.Clear();

        StrategyParameterCrossoverParameter.ValidValues.Clear();

        StrategyParameterManipulatorParameter.ValidValues.Clear();

        UpdateRecombinators();

      }

    }

    private void StrategyParameterCreatorParameter_ValueChanged(object sender, EventArgs e) {

      if (Mutator is ISelfAdaptiveManipulator && StrategyParameterCreator == null && StrategyParameterCreatorParameter.ValidValues.Count > 0)

        StrategyParameterCreator = StrategyParameterCreatorParameter.ValidValues.First();

    }

    private void StrategyParameterCrossoverParameter_ValueChanged(object sender, EventArgs e) {

      if (Mutator is ISelfAdaptiveManipulator && Recombinator != null && StrategyParameterCrossover == null && StrategyParameterCrossoverParameter.ValidValues.Count > 0)

        StrategyParameterCrossover = StrategyParameterCrossoverParameter.ValidValues.First();

    }

    #endregion



    #region Helpers

    private void Initialize() {

      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);

      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);

      ParentsPerChildParameter.ValueChanged += new EventHandler(ParentsPerChildParameter_ValueChanged);

      ParentsPerChild.ValueChanged += new EventHandler(ParentsPerChild_ValueChanged);

      ChildrenParameter.ValueChanged += new EventHandler(ChildrenParameter_ValueChanged);

      Children.ValueChanged += new EventHandler(Children_ValueChanged);

      PlusSelectionParameter.ValueChanged += new EventHandler(PlusSelectionParameter_ValueChanged);

      PlusSelection.ValueChanged += new EventHandler(PlusSelection_ValueChanged);

      RecombinatorParameter.ValueChanged += new EventHandler(RecombinatorParameter_ValueChanged);

      MutatorParameter.ValueChanged += new EventHandler(MutatorParameter_ValueChanged);

      StrategyParameterCrossoverParameter.ValueChanged += new EventHandler(StrategyParameterCrossoverParameter_ValueChanged);

      StrategyParameterCreatorParameter.ValueChanged += new EventHandler(StrategyParameterCreatorParameter_ValueChanged);

      if (Problem != null)

        Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);

    }

    private void ParameterizeSolutionsCreator() {

      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;

      SolutionsCreator.EvaluatorParameter.Hidden = true;

      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;

      SolutionsCreator.SolutionCreatorParameter.Hidden = true;

    }

    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) {

      if (op is IStochasticOperator) {

        IStochasticOperator stOp = (IStochasticOperator)op;

        stOp.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;

        stOp.RandomParameter.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;

      } else {

        qualityAnalyzer.MaximizationParameter.Hidden = false;

        qualityAnalyzer.QualityParameter.Hidden = false;

        qualityAnalyzer.BestKnownQualityParameter.Hidden = false;

      }

    }

    private void ParameterizeIterationBasedOperators() {

      if (Problem != null) {

        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {

          op.IterationsParameter.ActualName = "Generations";

          op.IterationsParameter.Hidden = true;

          op.MaximumIterationsParameter.ActualName = "MaximumGenerations";

          op.MaximumIterationsParameter.Hidden = true;

        }

      }

    }

    private void UpdateStrategyParameterOperators() {

      IStrategyParameterCreator oldStrategyCreator = StrategyParameterCreator;

      IStrategyParameterCrossover oldStrategyCrossover = StrategyParameterCrossover;

      IStrategyParameterManipulator oldStrategyManipulator = StrategyParameterManipulator;

      ClearStrategyParameterOperators();

      ISelfAdaptiveManipulator manipulator = (Mutator as ISelfAdaptiveManipulator);

      if (manipulator != null) {

        var operators = Problem.Operators.OfType<IOperator>().Where(x => manipulator.StrategyParameterType.IsAssignableFrom(x.GetType())).OrderBy(x => x.Name);

        foreach (IStrategyParameterCreator strategyCreator in operators.OfType<IStrategyParameterCreator>())

          StrategyParameterCreatorParameter.ValidValues.Add(strategyCreator);

        foreach (IStrategyParameterCrossover strategyRecombinator in operators.OfType<IStrategyParameterCrossover>())

          StrategyParameterCrossoverParameter.ValidValues.Add(strategyRecombinator);

        foreach (IStrategyParameterManipulator strategyManipulator in operators.OfType<IStrategyParameterManipulator>())

          StrategyParameterManipulatorParameter.ValidValues.Add(strategyManipulator);



        if (StrategyParameterCrossoverParameter.ValidValues.Count == 0)

          RecombinatorParameter.ValidValues.Clear(); // if there is no strategy parameter crossover, there can be no crossover when the mutation operator needs strategy parameters



        if (oldStrategyCreator != null) {

          IStrategyParameterCreator tmp1 = StrategyParameterCreatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldStrategyCreator.GetType());

          if (tmp1 != null) StrategyParameterCreator = tmp1;

        } else if (StrategyParameterCreatorParameter.ValidValues.Count > 0) StrategyParameterCreator = StrategyParameterCreatorParameter.ValidValues.First();

        if (oldStrategyCrossover != null) {

          IStrategyParameterCrossover tmp2 = StrategyParameterCrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldStrategyCrossover.GetType());

          if (tmp2 != null) StrategyParameterCrossover = tmp2;

        } else if (StrategyParameterCrossoverParameter.ValidValues.Count > 0) StrategyParameterCrossover = StrategyParameterCrossoverParameter.ValidValues.First();

        if (oldStrategyManipulator != null) {

          IStrategyParameterManipulator tmp3 = StrategyParameterManipulatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldStrategyManipulator.GetType());

          if (tmp3 != null) StrategyParameterManipulator = tmp3;

        } else if (StrategyParameterManipulatorParameter.ValidValues.Count > 0) StrategyParameterManipulator = StrategyParameterManipulatorParameter.ValidValues.First();

      }

    }

    private void ClearStrategyParameterOperators() {

      StrategyParameterCreatorParameter.ValidValues.Clear();

      StrategyParameterCrossoverParameter.ValidValues.Clear();

      StrategyParameterManipulatorParameter.ValidValues.Clear();

    }

    private void UpdateRecombinators() {

      ICrossover oldRecombinator = Recombinator;

      RecombinatorParameter.ValidValues.Clear();

      foreach (ICrossover recombinator in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name)) {

        RecombinatorParameter.ValidValues.Add(recombinator);

      }

      if (oldRecombinator != null) {

        ICrossover recombinator = RecombinatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldRecombinator.GetType());

        if (recombinator != null) RecombinatorParameter.Value = recombinator;

      }

    }

    private void UpdateMutators() {

      IManipulator oldMutator = MutatorParameter.Value;

      MutatorParameter.ValidValues.Clear();

      foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().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;

      } else if (MutatorParameter.ValidValues.Count > 0 && Problem.Operators.OfType<ISelfAdaptiveManipulator>().Count() > 0) {

        ISelfAdaptiveManipulator mutator = Problem.Operators.OfType<ISelfAdaptiveManipulator>().First();

        if (mutator != null) MutatorParameter.Value = mutator;

      }

    }

    private void UpdateAnalyzers() {

      Analyzer.Operators.Clear();

      if (Problem != null) {

        foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {

          foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())

            param.Depth = 1;

          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);

        }

      }

      Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);

    }

    private EvolutionStrategyMainLoop FindMainLoop(IOperator start) {

      IOperator mainLoop = start;

      while (mainLoop != null && !(mainLoop is EvolutionStrategyMainLoop))

        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;

      if (mainLoop == null) return null;

      else return (EvolutionStrategyMainLoop)mainLoop;

    }

    #endregion

  }

}