source: branches/ALPS/HeuristicLab.Algorithms.ALPS/3.3/AlpsGeneticAlgorithm.cs @ 11813

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Optimization.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Random;
using HeuristicLab.Selection;

namespace HeuristicLab.Algorithms.ALPS {
  [Item("ALPS Genetic Algorithm", "A genetic algorithm with an age-layered population structure.")]
  [Creatable("Algorithms")]
  [StorableClass]
  public sealed class AlpsGeneticAlgorithm : 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 IValueParameter<IntValue> SeedParameter {
      get { return (IValueParameter<IntValue>)Parameters["Seed"]; }
    }
    private IValueParameter<BoolValue> SetSeedRandomlyParameter {
      get { return (IValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }
    }
    private IValueParameter<MultiAnalyzer> AnalyzerParameter {
      get { return (IValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
    }
    private IValueParameter<MultiAnalyzer> LayerAnalyzerParameter {
      get { return (IValueParameter<MultiAnalyzer>)Parameters["LayerAnalyzer"]; }
    }
    private IValueParameter<IntValue> NumberOfLayersParameter {
      get { return (IValueParameter<IntValue>)Parameters["NumberOfLayers"]; }
    }
    private IValueParameter<IntValue> PopulationSizeParameter {
      get { return (IValueParameter<IntValue>)Parameters["PopulationSize"]; }
    }
    private IValueParameter<IntValue> MaximumGenerationsParameter {
      get { return (IValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
    }
    private IValueParameter<AgingScheme> AgingSchemeParameter {
      get { return (IValueParameter<AgingScheme>)Parameters["AgingScheme"]; }
    }
    private IValueParameter<IntValue> AgeGapParameter {
      get { return (IValueParameter<IntValue>)Parameters["AgeGap"]; }
    }
    private IValueParameter<IntArray> AgeLimitsParameter {
      get { return (IValueParameter<IntArray>)Parameters["AgeLimits"]; }
    }
    private IValueParameter<ReductionOperation> AgeInheritanceParameter {
      get { return (IValueParameter<ReductionOperation>)Parameters["AgeInheritance"]; }
    }
    public IConstrainedValueParameter<ISelector> SelectorParameter {
      get { return (IConstrainedValueParameter<ISelector>)Parameters["Selector"]; }
    }
    public IConstrainedValueParameter<ICrossover> CrossoverParameter {
      get { return (IConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
    }
    private IValueParameter<PercentValue> MutationProbabilityParameter {
      get { return (IValueParameter<PercentValue>)Parameters["MutationProbability"]; }
    }
    public IConstrainedValueParameter<IManipulator> MutatorParameter {
      get { return (IConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
    }
    private IValueParameter<IntValue> ElitesParameter {
      get { return (IValueParameter<IntValue>)Parameters["Elites"]; }
    }
    private IFixedValueParameter<BoolValue> ReevaluateElitesParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }
    }
    #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 MultiAnalyzer Analyzer {
      get { return AnalyzerParameter.Value; }
      set { AnalyzerParameter.Value = value; }
    }
    public MultiAnalyzer LayerAnalyzer {
      get { return LayerAnalyzerParameter.Value; }
      set { LayerAnalyzerParameter.Value = value; }
    }
    public IntValue NumberOfLayers {
      get { return NumberOfLayersParameter.Value; }
      set { NumberOfLayersParameter.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 AgingScheme AgingScheme {
      get { return AgingSchemeParameter.Value; }
      set { AgingSchemeParameter.Value = value; }
    }
    public IntValue AgeGap {
      get { return AgeGapParameter.Value; }
      set { AgeGapParameter.Value = value; }
    }
    public IntArray AgeLimits {
      get { return AgeLimitsParameter.Value; }
      set { AgeLimitsParameter.Value = value; }
    }
    public ReductionOperation AgeInheritance {
      get { return AgeInheritanceParameter.Value; }
      set { AgeInheritanceParameter.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; }
    }

    private RandomCreator GlobalRandomCreator {
      get { return (RandomCreator)OperatorGraph.InitialOperator; }
    }
    private SolutionsCreator SolutionsCreator {
      get { return OperatorGraph.Iterate().OfType<SolutionsCreator>().First(); }
    }
    private AlpsGeneticAlgorithmMainLoop MainLoop {
      get { return OperatorGraph.Iterate().OfType<AlpsGeneticAlgorithmMainLoop>().First(); }
    }

    #endregion

    #region Preconfigured Analyzers
    [Storable]
    private BestAverageWorstQualityAnalyzer qualityAnalyzer;
    [Storable]
    private BestAverageWorstQualityAnalyzer layerQualityAnalyzer;
    #endregion

    [StorableConstructor]
    private AlpsGeneticAlgorithm(bool deserializing)
      : base(deserializing) { }
    private AlpsGeneticAlgorithm(AlpsGeneticAlgorithm original, Cloner cloner)
      : base(original, cloner) {
      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
      layerQualityAnalyzer = cloner.Clone(original.layerQualityAnalyzer);
      Initialize();
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new AlpsGeneticAlgorithm(this, cloner);
    }
    public AlpsGeneticAlgorithm()
      : 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<MultiAnalyzer>("Analyzer", "The operator used to analyze the islands.", new MultiAnalyzer()));
      Parameters.Add(new ValueParameter<MultiAnalyzer>("LayerAnalyzer", "The operator used to analyze each layer.", new MultiAnalyzer()));
      Parameters.Add(new ValueParameter<IntValue>("NumberOfLayers", "The number of layers.", new IntValue(5)));
      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions each layer.", new IntValue(20)));
      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations that should be processed.", new IntValue(1000)));
      Parameters.Add(new ValueParameter<AgingScheme>("AgingScheme", "The aging scheme for setting the age-limits for the layers.", new AgingScheme(AgingSchemes.Polynomial)));
      Parameters.Add(new ValueParameter<IntValue>("AgeGap", "The frequency of reseeding the lowest layer and scaling factor for the age-limits for the layers", new IntValue(5)));
      Parameters.Add(new ValueParameter<IntArray>("AgeLimits", new IntArray(new[] { 5, 20, 45, 80, 125 })) { Hidden = true });
      Parameters.Add(new ValueParameter<ReductionOperation>("AgeInheritance", "The operator for determining the age of an offspring based the parents' age.", new ReductionOperation(ReductionOperations.Max)) { Hidden = true });
      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
      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 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 });

      var globalRandomCreator = new RandomCreator();
      var layer0Creator = new SubScopesCreator() { Name = "Create Layer Zero" };
      var layer0Processor = new SubScopesProcessor();
      var localRandomCreator = new LocalRandomCreator();
      var layerVariableCreator = new VariableCreator();
      var layerSolutionsCreator = new SolutionsCreator();
      var initializeAgeProcessor = new UniformSubScopesProcessor();
      var initializeAge = new VariableCreator() { Name = "Initialize Age" };
      var initializeLayerPopulationSize = new SubScopesCounter() { Name = "Init LayerPopulationCounter" };
      var initializeLocalEvaluatedSolutions = new Assigner() { Name = "Initialize LayerEvaluatedSolutions" };
      var initializeGlobalEvaluatedSolutions = new DataReducer() { Name = "Initialize EvaluatedSolutions" };
      var resultsCollector = new ResultsCollector();
      var mainLoop = new AlpsGeneticAlgorithmMainLoop();

      OperatorGraph.InitialOperator = globalRandomCreator;

      globalRandomCreator.RandomParameter.ActualName = "GlobalRandom";
      globalRandomCreator.SeedParameter.Value = null;
      globalRandomCreator.SetSeedRandomlyParameter.Value = null;
      globalRandomCreator.Successor = layer0Creator;

      layer0Creator.NumberOfSubScopesParameter.Value = new IntValue(1);
      layer0Creator.Successor = layer0Processor;

      layer0Processor.Operators.Add(localRandomCreator);
      layer0Processor.Successor = initializeGlobalEvaluatedSolutions;

      localRandomCreator.Successor = layerVariableCreator;

      layerVariableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Layer", new IntValue(0)));
      //layerVariableCreator.CollectedValues.Add(new ValueParameter<ResultCollection>("Results", new ResultCollection()));
      layerVariableCreator.Successor = layerSolutionsCreator;

      layerSolutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
      layerSolutionsCreator.Successor = initializeAgeProcessor;

      initializeAgeProcessor.Operator = initializeAge;
      initializeAgeProcessor.Successor = initializeLayerPopulationSize;

      initializeLayerPopulationSize.ValueParameter.ActualName = "LayerPopulationSize";
      initializeLayerPopulationSize.Successor = initializeLocalEvaluatedSolutions;

      initializeAge.CollectedValues.Add(new ValueParameter<IntValue>("Age", new IntValue(0)));
      initializeAge.Successor = null;

      initializeLocalEvaluatedSolutions.LeftSideParameter.ActualName = "LayerEvaluatedSolutions";
      initializeLocalEvaluatedSolutions.RightSideParameter.ActualName = "LayerPopulationSize";
      initializeLocalEvaluatedSolutions.Successor = null;

      initializeGlobalEvaluatedSolutions.ReductionOperation.Value.Value = ReductionOperations.Sum;
      initializeGlobalEvaluatedSolutions.TargetOperation.Value.Value = ReductionOperations.Assign;
      initializeGlobalEvaluatedSolutions.ParameterToReduce.ActualName = "LayerEvaluatedSolutions";
      initializeGlobalEvaluatedSolutions.TargetParameter.ActualName = "EvaluatedSolutions";
      initializeGlobalEvaluatedSolutions.Successor = resultsCollector;

      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
      resultsCollector.Successor = mainLoop;

      foreach (var selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(s => !(s is IMultiObjectiveSelector)).OrderBy(s => Name))
        SelectorParameter.ValidValues.Add(selector);
      var porportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(s => s is ProportionalSelector);
      if (porportionalSelector != null) SelectorParameter.Value = porportionalSelector;

      ParameterizeSelectors();

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

      Initialize();
    }

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

    #region Events
    protected override void OnProblemChanged() {
      ParameterizeStochasticOperator(Problem.SolutionCreator);
      ParameterizeStochasticOperatorForLayer(Problem.Evaluator);
      foreach (var @operator in Problem.Operators.OfType<IOperator>())
        ParameterizeStochasticOperator(@operator);
      ParameterizeSolutionsCreator();
      ParameterizeMainLoop();
      ParameterizeSelectors();
      ParameterizeAnalyzers();
      ParameterizeIterationBasedOperators();
      UpdateCrossovers();
      UpdateMutators();
      UpdateAnalyzers();
      Problem.Evaluator.QualityParameter.ActualNameChanged += 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) {
      ParameterizeStochasticOperatorForLayer(Problem.Evaluator);
      ParameterizeSolutionsCreator();
      ParameterizeMainLoop();
      ParameterizeSelectors();
      ParameterizeAnalyzers();
      Problem.Evaluator.QualityParameter.ActualNameChanged += Evaluator_QualityParameter_ActualNameChanged;
      base.Problem_EvaluatorChanged(sender, e);
    }
    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
      foreach (var @operator in Problem.Operators.OfType<IOperator>())
        ParameterizeStochasticOperator(@operator);
      ParameterizeIterationBasedOperators();
      UpdateCrossovers();
      UpdateMutators();
      UpdateAnalyzers();
      base.Problem_OperatorsChanged(sender, e);
    }
    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
      ParameterizeMainLoop();
      ParameterizeSelectors();
      ParameterizeAnalyzers();
    }

    void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
      PopulationSizeParameter.ValueChanged += PopulationSize_ValueChanged;
      ParameterizeSelectors();
    }
    void PopulationSize_ValueChanged(object sender, EventArgs e) {
      ParameterizeSelectors();
    }
    void ElitesParameter_ValueChanged(object sender, EventArgs e) {
      Elites.ValueChanged += ElitesParameter_ValueChanged;
      ParameterizeSelectors();
    }
    void Elites_ValueChanged(object sender, EventArgs e) {
      ParameterizeSelectors();
    }
    private void AgeGapParameter_ValueChanged(object sender, EventArgs e) {
      AgeGap.ValueChanged += AgeGap_ValueChanged;
      RecalculateAgeLimits();
    }
    private void AgeGap_ValueChanged(object sender, EventArgs e) {
      RecalculateAgeLimits();
    }
    private void AgingSchemeParameter_ValueChanged(object sender, EventArgs e) {
      AgingScheme.ValueChanged += AgingScheme_ValueChanged;
      RecalculateAgeLimits();
    }
    private void AgingScheme_ValueChanged(object sender, EventArgs e) {
      RecalculateAgeLimits();
    }
    private void NumberOfLayersParameter_ValueChanged(object sender, EventArgs e) {
      NumberOfLayers.ValueChanged += NumberOfLayers_ValueChanged;
      RecalculateAgeLimits();
    }
    private void NumberOfLayers_ValueChanged(object sender, EventArgs e) {
      RecalculateAgeLimits();
    }
    #endregion

    #region Parameterization
    private void Initialize() {
      PopulationSizeParameter.ValueChanged += PopulationSizeParameter_ValueChanged;
      PopulationSize.ValueChanged += PopulationSize_ValueChanged;
      ElitesParameter.ValueChanged += ElitesParameter_ValueChanged;
      Elites.ValueChanged += Elites_ValueChanged;
      if (Problem != null)
        Problem.Evaluator.QualityParameter.ActualNameChanged += Evaluator_QualityParameter_ActualNameChanged;
      AgeGapParameter.ValueChanged += AgeGapParameter_ValueChanged;
      AgeGap.ValueChanged += AgeGap_ValueChanged;
      AgingSchemeParameter.ValueChanged += AgingSchemeParameter_ValueChanged;
      AgingScheme.ValueChanged += AgingScheme_ValueChanged;
      NumberOfLayersParameter.ValueChanged += NumberOfLayersParameter_ValueChanged;
      NumberOfLayers.ValueChanged += NumberOfLayers_ValueChanged;
    }
    private void ParameterizeSolutionsCreator() {
      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
      MainLoop.LayerUpdator.SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
      MainLoop.LayerUpdator.SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
    }
    private void ParameterizeMainLoop() {
      MainLoop.MainOperator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
      MainLoop.MainOperator.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
      MainLoop.MainOperator.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
      MainLoop.EldersEmigrator.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
      MainLoop.EldersEmigrator.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
      MainLoop.LayerUpdator.SolutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
    }
    private void ParameterizeSelectors() {
      foreach (var selector in SelectorParameter.ValidValues) {
        selector.CopySelected = new BoolValue(true);
        selector.NumberOfSelectedSubScopesParameter.Value = new IntValue(2 * (PopulationSize.Value - Elites.Value));
        selector.NumberOfSelectedSubScopesParameter.Hidden = true;
        ParameterizeStochasticOperatorForLayer(selector);
      }
      if (Problem != null) {
        foreach (var selector in SelectorParameter.ValidValues.OfType<ISingleObjectiveSelector>()) {
          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
          selector.MaximizationParameter.Hidden = true;
          selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
          selector.QualityParameter.Hidden = true;
        }
      }
    }
    private void ParameterizeAnalyzers() {
      qualityAnalyzer.ResultsParameter.ActualName = "Results";
      qualityAnalyzer.ResultsParameter.Hidden = true;
      qualityAnalyzer.QualityParameter.Depth = 2;
      layerQualityAnalyzer.ResultsParameter.ActualName = "Results";
      layerQualityAnalyzer.ResultsParameter.Hidden = true;
      layerQualityAnalyzer.QualityParameter.Depth = 1;
      if (Problem != null) {
        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;
        layerQualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
        layerQualityAnalyzer.MaximizationParameter.Hidden = true;
        layerQualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
        layerQualityAnalyzer.QualityParameter.Hidden = true;
        layerQualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
        layerQualityAnalyzer.BestKnownQualityParameter.Hidden = true;
      }
    }
    private void ParameterizeIterationBasedOperators() {
      if (Problem != null) {
        foreach (var @operator in Problem.Operators.OfType<IIterationBasedOperator>()) {
          @operator.IterationsParameter.ActualName = "Generations";
          @operator.IterationsParameter.Hidden = true;
          @operator.MaximumIterationsParameter.ActualName = "MaximumGenerations";
          @operator.MaximumIterationsParameter.Hidden = true;
        }
      }
    }
    private void ParameterizeStochasticOperator(IOperator @operator) {
      var stochasticOperator = @operator as IStochasticOperator;
      if (stochasticOperator != null) {
        stochasticOperator.RandomParameter.ActualName = GlobalRandomCreator.RandomParameter.ActualName;
        stochasticOperator.RandomParameter.Hidden = true;
      }
    }
    private void ParameterizeStochasticOperatorForLayer(IOperator @operator) {
      var stochasticOperator = @operator as IStochasticOperator;
      if (stochasticOperator != null) {
        stochasticOperator.RandomParameter.ActualName = "LocalRandom";
        stochasticOperator.RandomParameter.Hidden = true;
      }
    }
    #endregion

    #region Updates
    private void UpdateCrossovers() {
      var oldCrossover = CrossoverParameter.Value;
      var defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
      CrossoverParameter.ValidValues.Clear();
      foreach (var crossover in Problem.Operators.OfType<ICrossover>().OrderBy(c => c.Name)) {
        ParameterizeStochasticOperatorForLayer(crossover);
        CrossoverParameter.ValidValues.Add(crossover);
      }
      if (oldCrossover != null) {
        var crossover = CrossoverParameter.ValidValues.FirstOrDefault(c => c.GetType() == oldCrossover.GetType());
        if (crossover != null)
          CrossoverParameter.Value = crossover;
        else
          oldCrossover = null;
      }
      if (oldCrossover == null && defaultCrossover != null)
        CrossoverParameter.Value = defaultCrossover;
    }
    private void UpdateMutators() {
      var oldMutator = MutatorParameter.Value;
      MutatorParameter.ValidValues.Clear();
      foreach (var mutator in Problem.Operators.OfType<IManipulator>().OrderBy(m => m.Name)) {
        ParameterizeStochasticOperatorForLayer(mutator);
        MutatorParameter.ValidValues.Add(mutator);
      }
      if (oldMutator != null) {
        var mutator = MutatorParameter.ValidValues.FirstOrDefault(m => m.GetType() == oldMutator.GetType());
        if (mutator != null)
          MutatorParameter.Value = mutator;
      }
    }
    private void UpdateAnalyzers() {
      Analyzer.Operators.Clear();
      LayerAnalyzer.Operators.Clear();

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

      if (Problem != null) {
        foreach (var analyzer in Problem.Operators.OfType<IAnalyzer>()) {
          foreach (var parameter in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
            parameter.Depth = 2;
          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
        }
      }
    }
    #endregion

    #region AgeLimits calculation
    private void RecalculateAgeLimits() {
      IEnumerable<int> scheme;
      switch (AgingScheme.Value) {
        case AgingSchemes.Linear: scheme = LinearAgingScheme(); break;
        case AgingSchemes.Fibonacci: scheme = FibonacciAgingScheme(); break;
        case AgingSchemes.Polynomial: scheme = PolynomialAgingScheme(2); break;
        case AgingSchemes.Exponential: scheme = ExponentialAgingScheme(2); break;
        default: throw new NotSupportedException("Aging Scheme " + AgingScheme.Value + " is not supported.");
      }

      int ageGap = AgeGap.Value;
      AgeLimits = new IntArray(scheme.Select(a => a * ageGap).Take(NumberOfLayers.Value).ToArray());
    }

    // 1 2 3 4 5 6 7
    private static IEnumerable<int> LinearAgingScheme() {
      for (int i = 0; ; i++)
        yield return i + 1;
    }
    // 1 2 3 5 8 13 21
    private static IEnumerable<int> FibonacciAgingScheme() {
      for (int i = 1, next = 2, temp; ; temp = next, next = i + next, i = temp)
        yield return i;
    }
    // (n^2): 1 2 4 9 16 25 36
    private static IEnumerable<int> PolynomialAgingScheme(double exp) {
      yield return 1;
      yield return 2;
      for (int i = 2; ; i++)
        yield return (int)Math.Pow(i, exp);

    }
    // 1 2 4 8 16 32 64
    private static IEnumerable<int> ExponentialAgingScheme(double @base) {
      for (int i = 0; ; i++)
        yield return (int)Math.Pow(@base, i);
    }
    #endregion
  }
}