#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 <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.Optimization.Operators.LCS;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Random;

namespace HeuristicLab.Algorithms.GAssist {
  /// <summary>
  /// A genetic algorithm.
  /// </summary>
  [Item("GAssist", "A learning classifier system.")]
  [Creatable("Algorithms")]
  [StorableClass]
  public sealed class GAssist : HeuristicOptimizationEngineAlgorithm, IStorableContent {
    public string Filename { get; set; }

    #region Problem Properties
    public override Type ProblemType {
      get { return typeof(IGAssistProblem); }
    }
    public new IGAssistProblem Problem {
      get { return (IGAssistProblem)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"]; }
    }
    public ValueParameter<MDLIterationOperator> MDLIterationOperatorParameter {
      get { return (ValueParameter<MDLIterationOperator>)Parameters["MDLIterationOperator"]; }
    }
    public IConstrainedValueParameter<IDefaultRuleOperator> DefaultRuleParameter {
      get { return (IConstrainedValueParameter<IDefaultRuleOperator>)Parameters["DefaultRule"]; }
    }
    public IConstrainedValueParameter<INichingSingleObjectiveSelector> SelectorParameter {
      get { return (IConstrainedValueParameter<INichingSingleObjectiveSelector>)Parameters["Selector"]; }
    }
    private ValueParameter<PercentValue> CrossoverProbabilityParameter {
      get { return (ValueParameter<PercentValue>)Parameters["CrossoverProbability"]; }
    }
    public IConstrainedValueParameter<ICrossover> CrossoverParameter {
      get { return (IConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
    }
    private ValueParameter<PercentValue> MutationProbabilityParameter {
      get { return (ValueParameter<PercentValue>)Parameters["MutationProbability"]; }
    }
    public IConstrainedValueParameter<IManipulator> MutatorParameter {
      get { return (IConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
    }
    private ValueParameter<IntValue> ElitesParameter {
      get { return (ValueParameter<IntValue>)Parameters["Elites"]; }
    }
    private ValueParameter<MultiAnalyzer> AnalyzerParameter {
      get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
    }
    private ValueParameter<GAssistSpecialStageMultiOperator> SpecialStagesParameter {
      get { return (ValueParameter<GAssistSpecialStageMultiOperator>)Parameters["SpecialStages"]; }
    }
    private ValueParameter<IntValue> MaximumGenerationsParameter {
      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
    }
    private ValueParameter<PercentValue> StartReinitializeProbabilityParameter {
      get { return (ValueParameter<PercentValue>)Parameters["StartReinitializeProbability"]; }
    }
    private ValueParameter<PercentValue> EndReinitializeProbabilityParameter {
      get { return (ValueParameter<PercentValue>)Parameters["EndReinitializeProbability"]; }
    }
    public IConstrainedValueParameter<IDiscreteDoubleValueModifier> ReinitializeCurveOperatorParameter {
      get { return (IConstrainedValueParameter<IDiscreteDoubleValueModifier>)Parameters["ReinitializeCurveOperator"]; }
    }
    private ValueParameter<PercentValue> SplitProbabilityParameter {
      get { return (ValueParameter<PercentValue>)Parameters["SplitProbability"]; }
    }
    private ValueParameter<PercentValue> MergeProbabilityParameter {
      get { return (ValueParameter<PercentValue>)Parameters["MergeProbability"]; }
    }
    private ValueParameter<PercentValue> OneProbabilityParameter {
      get { return (ValueParameter<PercentValue>)Parameters["OneProbability"]; }
    }
    private ValueParameter<IntValue> MaximumNumberOfIntervalsParameter {
      get { return (ValueParameter<IntValue>)Parameters["MaximumNumberOfIntervals"]; }
    }
    private ValueParameter<IntValue> InitialNumberOfRulesParameter {
      get { return (ValueParameter<IntValue>)Parameters["InitialNumberOfRules"]; }
    }
    private ValueParameter<ItemCollection<IDiscretizer>> DiscretizersParameter {
      get { return (ValueParameter<ItemCollection<IDiscretizer>>)Parameters["Discretizers"]; }
    }
    public ValueParameter<IntValue> MDLActivationIterationParameter {
      get { return (ValueParameter<IntValue>)Parameters["MDLActivationIteration"]; }
    }
    public ValueParameter<DoubleValue> InitialTheoryLengthRatioParameter {
      get { return (ValueParameter<DoubleValue>)Parameters["InitialTheoryLengthRatio"]; }
    }
    public ValueParameter<DoubleValue> WeightRelaxFactorParameter {
      get { return (ValueParameter<DoubleValue>)Parameters["WeightRelaxFactor"]; }
    }
    public ValueParameter<IntValue> WeightAdaptionIterationsParameter {
      get { return (ValueParameter<IntValue>)Parameters["WeightAdaptionIterations"]; }
    }
    public ValueParameter<IntValue> NumberOfStrataParameter {
      get { return (ValueParameter<IntValue>)Parameters["NumberOfStrata"]; }
    }
    #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 INichingSingleObjectiveSelector Selector {
      get { return SelectorParameter.Value; }
      set { SelectorParameter.Value = value; }
    }
    public PercentValue CrossoverProbability {
      get { return CrossoverProbabilityParameter.Value; }
      set { CrossoverProbabilityParameter.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 GAssistSpecialStageMultiOperator SpecialStages {
      get { return SpecialStagesParameter.Value; }
      set { SpecialStagesParameter.Value = value; }
    }
    public IDiscreteDoubleValueModifier ReinitializeCurveOperator {
      get { return ReinitializeCurveOperatorParameter.Value; }
      set { ReinitializeCurveOperatorParameter.Value = value; }
    }
    public IntValue MaximumGenerations {
      get { return MaximumGenerationsParameter.Value; }
      set { MaximumGenerationsParameter.Value = value; }
    }
    private RandomCreator RandomCreator {
      get { return (RandomCreator)OperatorGraph.InitialOperator; }
    }
    private VariableCreator VariableCreator {
      get { return (VariableCreator)RandomCreator.Successor; }
    }
    private Placeholder MDLIterationPlaceholder {
      get { return (Placeholder)VariableCreator.Successor; }
    }
    private ILASOperator ILASOperator {
      get { return (ILASOperator)MDLIterationPlaceholder.Successor; }
    }
    private InitializeDiscretizersOperator InitializeDiscretizers {
      get { return (InitializeDiscretizersOperator)ILASOperator.Successor; }
    }
    private SolutionsCreator SolutionsCreator {
      get { return (SolutionsCreator)InitializeDiscretizers.Successor; }
    }
    private GAssistMainLoop GeneticAlgorithmMainLoop {
      get { return FindMainLoop(SolutionsCreator.Successor); }
    }
    [Storable]
    private BestAverageWorstQualityAnalyzer qualityAnalyzer;
    #endregion

    public GAssist()
      : 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", "The size of the population of solutions.", new IntValue(100)));
      Parameters.Add(new ConstrainedValueParameter<INichingSingleObjectiveSelector>("Selector", "The operator used to select solutions for reproduction."));
      Parameters.Add(new ValueParameter<PercentValue>("CrossoverProbability", "The probability that the Crossover operator is applied on a solution.", new PercentValue(0.9)));
      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
      Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
      Parameters.Add(new ValueParameter<GAssistSpecialStageMultiOperator>("SpecialStages", "", new GAssistSpecialStageMultiOperator()));
      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
      Parameters.Add(new ValueParameter<PercentValue>("SplitProbability", "", new PercentValue(0.05)));
      Parameters.Add(new ValueParameter<PercentValue>("MergeProbability", "", new PercentValue(0.05)));
      Parameters.Add(new ValueParameter<PercentValue>("StartReinitializeProbability", "", new PercentValue(0.05)));
      Parameters.Add(new ValueParameter<PercentValue>("EndReinitializeProbability", "", new PercentValue(Double.Epsilon)));
      Parameters.Add(new ValueParameter<PercentValue>("OneProbability", "", new PercentValue(0.75)));
      Parameters.Add(new ValueParameter<IntValue>("MaximumNumberOfIntervals", "", new IntValue(5)));
      Parameters.Add(new ValueParameter<IntValue>("InitialNumberOfRules", "", new IntValue(20)));
      Parameters.Add(new ValueParameter<MDLIterationOperator>("MDLIterationOperator", "", new MDLIterationOperator()));
      Parameters.Add(new ConstrainedValueParameter<IDefaultRuleOperator>("DefaultRule", ""));
      Parameters.Add(new ConstrainedValueParameter<IDiscreteDoubleValueModifier>("ReinitializeCurveOperator", ""));
      Parameters.Add(new ValueParameter<ItemCollection<IDiscretizer>>("Discretizers", "", new ItemCollection<IDiscretizer>()));

      Parameters.Add(new ValueParameter<IntValue>("MDLActivationIteration", "", new IntValue(25)));
      Parameters.Add(new ValueParameter<DoubleValue>("InitialTheoryLengthRatio", "", new DoubleValue(0.075)));
      Parameters.Add(new ValueParameter<DoubleValue>("WeightRelaxFactor", "", new DoubleValue(0.9)));
      Parameters.Add(new ValueParameter<IntValue>("WeightAdaptionIterations", "", new IntValue(10)));

      Parameters.Add(new ValueParameter<IntValue>("NumberOfStrata", "", new IntValue(2)));

      RandomCreator randomCreator = new RandomCreator();
      VariableCreator variableCreator = new VariableCreator();
      Placeholder mdlIterationPlaceholder = new Placeholder();
      ILASOperator ilasOperator = new ILASOperator();
      InitializeDiscretizersOperator initializeDiscretizers = new InitializeDiscretizersOperator();
      SolutionsCreator solutionsCreator = new SolutionsCreator();
      SubScopesCounter subScopesCounter = new SubScopesCounter();
      ResultsCollector resultsCollector = new ResultsCollector();
      GAssistMainLoop mainLoop = new GAssistMainLoop();
      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 = variableCreator;

      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class GAssistMainLoop expects this to be called Generations
      variableCreator.Successor = mdlIterationPlaceholder;

      mdlIterationPlaceholder.Name = "MDL Iteration Operator";
      mdlIterationPlaceholder.OperatorParameter.ActualName = MDLIterationOperatorParameter.Name;
      mdlIterationPlaceholder.Successor = ilasOperator;

      ilasOperator.RandomParameter.ActualName = randomCreator.RandomParameter.ActualName;
      ilasOperator.NumberOfStrataParameter.ActualName = NumberOfStrataParameter.Name;
      ilasOperator.Successor = initializeDiscretizers;

      initializeDiscretizers.DiscretizersParameter.ActualName = DiscretizersParameter.Name;
      initializeDiscretizers.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<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
      resultsCollector.ResultsParameter.ActualName = "Results";
      resultsCollector.Successor = mainLoop;

      mainLoop.MDLIterationParameter.ActualName = MDLIterationOperatorParameter.Name;
      mainLoop.DefaultRuleParameter.ActualName = DefaultRuleParameter.Name;
      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
      mainLoop.CrossoverProbabilityParameter.ActualName = CrossoverProbabilityParameter.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.SpecialStagesParameter.ActualName = SpecialStagesParameter.Name;
      mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
      mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
      mainLoop.ResultsParameter.ActualName = "Results";
      mainLoop.ReinitializationProbabilityOperatorParameter.ActualName = ReinitializeCurveOperatorParameter.Name;

      foreach (INichingSingleObjectiveSelector selector in ApplicationManager.Manager.GetInstances<INichingSingleObjectiveSelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
        SelectorParameter.ValidValues.Add(selector);
      //INichingSingleObjectiveSelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
      //if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
      ParameterizeSelectors();

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

      foreach (IDiscreteDoubleValueModifier op in ApplicationManager.Manager.GetInstances<IDiscreteDoubleValueModifier>().OrderBy(x => x.Name)) {
        ReinitializeCurveOperatorParameter.ValidValues.Add(op);
      }
      ReinitializeCurveOperatorParameter.Value = ReinitializeCurveOperatorParameter.ValidValues.First(x => x.GetType().Equals(typeof(LinearDiscreteDoubleValueModifier)));
      ParameterizeReinitializeCurveOperators();

      InitializeSpecialStages();

      UpdateDiscretizers();

      Initialize();
    }

    private void UpdateDiscretizers() {
      // change to add more
      DiscretizersParameter.Value.AddRange(ApplicationManager.Manager.GetInstances<IDiscretizer>());
    }

    private void ParameterizeReinitializeCurveOperators() {
      foreach (IDiscreteDoubleValueModifier op in ReinitializeCurveOperatorParameter.ValidValues) {
        op.IndexParameter.ActualName = "Generations";
        op.IndexParameter.Hidden = true;
        op.StartIndexParameter.Value = new IntValue(0);
        op.EndIndexParameter.ActualName = MaximumGenerationsParameter.Name;
        op.ValueParameter.ActualName = "ReinitializeProbability";
        op.ValueParameter.Hidden = true;
        op.StartValueParameter.ActualName = StartReinitializeProbabilityParameter.Name;
        op.StartValueParameter.Hidden = true;
        op.EndValueParameter.ActualName = EndReinitializeProbabilityParameter.Name;
        op.EndValueParameter.Hidden = true;
        ParameterizeStochasticOperator(op);
      }
    }

    private void InitializeSpecialStages() {
      SpecialStages.Operators.Clear();
      var splitOperator = new SplitOperator();
      splitOperator.ProbabilityParameter.ActualName = "SplitProbability";
      //change
      splitOperator.IndividualParameter.ActualName = "DecisionList";
      SpecialStages.Operators.Add(splitOperator);
      var mergeOperator = new MergeOperator();
      mergeOperator.ProbabilityParameter.ActualName = "MergeProbability";
      //change
      mergeOperator.IndividualParameter.ActualName = "DecisionList";
      SpecialStages.Operators.Add(mergeOperator);
      var reinitializeOperator = new ReinitializeOperator();
      reinitializeOperator.ProbabilityParameter.ActualName = "ReinitializeProbability";
      reinitializeOperator.DiscretizersParameter.ActualName = "Discretizers";
      reinitializeOperator.OneProbabilityParameter.ActualName = "OneProbability";
      //change
      reinitializeOperator.IndividualParameter.ActualName = "DecisionList";
      SpecialStages.Operators.Add(reinitializeOperator);
      foreach (var op in SpecialStages.Operators) {
        op.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
      }
    }

    [StorableConstructor]
    private GAssist(bool deserializing) : base(deserializing) { }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      Initialize();
    }

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

    public override void Prepare() {
      if (Problem != null) base.Prepare();
    }

    #region Events
    protected override void OnProblemChanged() {
      ParameterizeStochasticOperator(Problem.SolutionCreator);
      ParameterizeStochasticOperator(Problem.Evaluator);
      ParameterizeMDLOperator(Problem.Evaluator);
      ParameterizeIterationBasedOperators(Problem.Evaluator);
      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) {
        ParameterizeStochasticOperator(op);
      }
      ParameterizeSolutionsCreator();
      ParameterizeGeneticAlgorithmMainLoop();
      ParameterizeMDL();
      ParameterizeSelectors();
      ParameterizeAnalyzers();
      ParameterizeIterationBasedOperators();
      UpdateDefaultRuleOperators();
      UpdateCrossovers();
      UpdateMutators();
      UpdateAnalyzers();
      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
      ILASOperator.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
      InitializeDiscretizers.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
      base.OnProblemChanged();
    }

    private void ParameterizeMDL() {
      MDLIterationOperatorParameter.Value.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
      //change
      MDLIterationOperatorParameter.Value.IndividualParameter.ActualName = "DecisionList";
      MDLIterationOperatorParameter.Value.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
      MDLIterationOperatorParameter.Value.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;

      MDLIterationOperatorParameter.Value.InitialTheoryLengthRatioParameter.ActualName = InitialTheoryLengthRatioParameter.Name;
      MDLIterationOperatorParameter.Value.MDLActivationIterationParameter.ActualName = MDLActivationIterationParameter.Name;
      MDLIterationOperatorParameter.Value.WeightAdaptionIterationsParameter.ActualName = WeightAdaptionIterationsParameter.Name;
      MDLIterationOperatorParameter.Value.WeightRelaxFactorParameter.ActualName = WeightRelaxFactorParameter.Name;
      MDLIterationOperatorParameter.Value.IterationsParameter.ActualName = "Generations";
    }

    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);
      Problem.Evaluator.StrataParameter.ActualName = ILASOperator.StrataParameter.ActualName;
      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();
      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;
      // change!
      ((IGAssistIndividualCreator)Problem.SolutionCreatorParameter.ActualValue).DiscretizersParameter.ActualName = "Discretizers";
    }
    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 ParameterizeMDLOperator(IOperator op) {
      IMDLCalculatorBasedOperator stochasticOp = op as IMDLCalculatorBasedOperator;
      if (stochasticOp != null) {
        stochasticOp.MDLCalculatorParameter.ActualName = MDLIterationOperatorParameter.Value.MDLCalculatorParameter.ActualName;
      }
    }
    private void ParameterizeSelectors() {
      foreach (INichingSingleObjectiveSelector 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;
        selector.ParentsPerChildParameter.Value = new IntValue(2);
        ParameterizeStochasticOperator(selector);
      }
      if (Problem != null) {
        foreach (INichingSingleObjectiveSelector selector in SelectorParameter.ValidValues.OfType<INichingSingleObjectiveSelector>()) {
          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
          selector.MaximizationParameter.Hidden = true;
          selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
          selector.QualityParameter.Hidden = true;
          selector.NichingParameter.ActualName = Problem.NichingParameterName;
          selector.GAssistNichesProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
          //change
          selector.IndividualParameter.ActualName = "DecisionList";
        }
      }
    }
    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(IOperator op) {
      IIterationBasedOperator iterationOp = op as IIterationBasedOperator;
      if (iterationOp != null) {
        ParameterizeIterationBasedOperators(iterationOp);
      }
    }
    private void ParameterizeIterationBasedOperators(IIterationBasedOperator op) {
      op.IterationsParameter.ActualName = "Generations";
      op.IterationsParameter.Hidden = true;
      op.MaximumIterationsParameter.ActualName = "MaximumGenerations";
      op.MaximumIterationsParameter.Hidden = true;
    }
    private void ParameterizeIterationBasedOperators() {
      if (Problem != null) {
        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {
          ParameterizeIterationBasedOperators(op);
        }
      }
    }
    private void UpdateDefaultRuleOperators() {
      IDefaultRuleOperator oldDefaultRule = DefaultRuleParameter.Value;
      DefaultRuleParameter.ValidValues.Clear();
      IDefaultRuleOperator defaultdefaultRule = Problem.Operators.OfType<IDefaultRuleOperator>().FirstOrDefault();

      foreach (IDefaultRuleOperator defaultRule in Problem.Operators.OfType<IDefaultRuleOperator>().OrderBy(x => x.Name))
        DefaultRuleParameter.ValidValues.Add(defaultRule);

      if (oldDefaultRule != null) {
        IDefaultRuleOperator defaultRule = DefaultRuleParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldDefaultRule.GetType());
        if (defaultRule != null) DefaultRuleParameter.Value = defaultRule;
        else oldDefaultRule = null;
      }
      if (oldDefaultRule == null && defaultdefaultRule != null)
        DefaultRuleParameter.Value = defaultdefaultRule;
    }
    private void UpdateCrossovers() {
      ICrossover oldCrossover = CrossoverParameter.Value;
      CrossoverParameter.ValidValues.Clear();
      ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();

      foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().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();
      IManipulator defaultMutator = Problem.Operators.OfType<IManipulator>().FirstOrDefault();

      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;
      }
      if (oldMutator == null && defaultMutator != null)
        MutatorParameter.Value = defaultMutator;
    }
    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 GAssistMainLoop FindMainLoop(IOperator start) {
      IOperator mainLoop = start;
      while (mainLoop != null && !(mainLoop is GAssistMainLoop))
        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
      if (mainLoop == null) return null;
      else return (GAssistMainLoop)mainLoop;
    }
    #endregion
  }
}