#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 <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 HEAL.Attic;
using HeuristicLab.Random;

namespace HeuristicLab.Algorithms.OffspringSelectionEvolutionStrategy {
  [Item("Offspring Selection Evolution Strategy (OSES)", "An evolution strategy with offspring selection.")]
  [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 205)]
  [StorableType("4276A72B-3157-49E0-AD50-6FC2EC8524FD")]
  public sealed class OffspringSelectionEvolutionStrategy : 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> 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"]; }
    }

    private ValueLookupParameter<DoubleValue> SuccessRatioParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
    }
    private ValueLookupParameter<DoubleValue> MaximumSelectionPressureParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["MaximumSelectionPressure"]; }
    }
    private ValueLookupParameter<IntValue> SelectedParentsParameter {
      get { return (ValueLookupParameter<IntValue>)Parameters["SelectedParents"]; }
    }
    private ValueLookupParameter<DoubleValue> ComparisonFactorParameter {
      get { return (ValueLookupParameter<DoubleValue>)Parameters["ComparisonFactor"]; }
    }
    private ValueParameter<IntValue> MaximumEvaluatedSolutionsParameter {
      get { return (ValueParameter<IntValue>)Parameters["MaximumEvaluatedSolutions"]; }
    }
    #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 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; }
    }

    public DoubleValue SuccessRatio {
      get { return SuccessRatioParameter.Value; }
      set { SuccessRatioParameter.Value = value; }
    }
    public DoubleValue MaximumSelectionPressure {
      get { return MaximumSelectionPressureParameter.Value; }
      set { MaximumSelectionPressureParameter.Value = value; }
    }
    public IntValue SelectedParents {
      get { return SelectedParentsParameter.Value; }
      set { SelectedParentsParameter.Value = value; }
    }
    public DoubleValue ComparisonFactor {
      get { return ComparisonFactorParameter.Value; }
      set { ComparisonFactorParameter.Value = value; }
    }
    public IntValue MaximumEvaluatedSolutions {
      get { return MaximumEvaluatedSolutionsParameter.Value; }
      set { MaximumEvaluatedSolutionsParameter.Value = value; }
    }

    private RandomCreator RandomCreator {
      get { return (RandomCreator)OperatorGraph.InitialOperator; }
    }
    private SolutionsCreator SolutionsCreator {
      get { return (SolutionsCreator)RandomCreator.Successor; }
    }
    private OffspringSelectionEvolutionStrategyMainLoop MainLoop {
      get { return FindMainLoop(SolutionsCreator.Successor); }
    }
    [Storable]
    private BestAverageWorstQualityAnalyzer qualityAnalyzer;
    [Storable]
    private ValueAnalyzer selectionPressureAnalyzer;
    #endregion

    public OffspringSelectionEvolutionStrategy()
      : 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>("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()));

      Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
      Parameters.Add(new ValueLookupParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(40)));
      Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
      Parameters.Add(new ValueParameter<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions.", new IntValue(int.MaxValue)));
      Parameters.Add(new ValueLookupParameter<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].", new DoubleValue(0.5)));



      RandomCreator randomCreator = new RandomCreator();
      SolutionsCreator solutionsCreator = new SolutionsCreator();
      SubScopesCounter subScopesCounter = new SubScopesCounter();
      UniformSubScopesProcessor strategyVectorProcessor = new UniformSubScopesProcessor();
      Placeholder strategyVectorCreator = new Placeholder();
      ResultsCollector resultsCollector = new ResultsCollector();
      OffspringSelectionEvolutionStrategyMainLoop mainLoop = new OffspringSelectionEvolutionStrategyMainLoop();
      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.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";

      mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
      mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
      mainLoop.MaximumEvaluatedSolutionsParameter.ActualName = MaximumEvaluatedSolutionsParameter.Name;
      mainLoop.SelectedParentsParameter.ActualName = SelectedParentsParameter.Name;
      mainLoop.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
      mainLoop.CurrentSuccessRatioParameter.ActualName = "CurrentSuccessRatio";
      mainLoop.SelectionPressureParameter.ActualName = "SelectionPressure";

      qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
      selectionPressureAnalyzer = new ValueAnalyzer();
      ParameterizeAnalyzers();
      UpdateAnalyzers();

      Initialize();
    }
    [StorableConstructor]
    private OffspringSelectionEvolutionStrategy(StorableConstructorFlag _) : base(_) { }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      Initialize();
    }

    private OffspringSelectionEvolutionStrategy(OffspringSelectionEvolutionStrategy original, Cloner cloner)
      : base(original, cloner) {
      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
      selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
      Initialize();
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new OffspringSelectionEvolutionStrategy(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 (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 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);
      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;
      }

      selectionPressureAnalyzer.Name = "SelectionPressure Analyzer";
      selectionPressureAnalyzer.ResultsParameter.ActualName = "Results";
      selectionPressureAnalyzer.ValueParameter.ActualName = "SelectionPressure";
      selectionPressureAnalyzer.ValueParameter.Depth = 0;
      selectionPressureAnalyzer.ValuesParameter.ActualName = "Selection Pressure History";
    }
    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>().Any()) {
        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);
      Analyzer.Operators.Add(selectionPressureAnalyzer, selectionPressureAnalyzer.EnabledByDefault);
    }
    private OffspringSelectionEvolutionStrategyMainLoop FindMainLoop(IOperator start) {
      IOperator mainLoop = start;
      while (mainLoop != null && !(mainLoop is OffspringSelectionEvolutionStrategyMainLoop))
        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
      return (OffspringSelectionEvolutionStrategyMainLoop) mainLoop;
    }
    #endregion
  }
}