source: stable/HeuristicLab.Algorithms.ALPS/3.3/AlpsGeneticAlgorithm.cs @ 13459

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2015 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.Collections;
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 within an age-layered population structure as described in Gregory S. Hornby. 2006. ALPS: the age-layered population structure for reducing the problem of premature convergence. In Proceedings of the 8th annual conference on Genetic and evolutionary computation (GECCO '06). 815-822.")]
  [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 160)]
  [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 IFixedValueParameter<MultiAnalyzer> AnalyzerParameter {
      get { return (IFixedValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
    }
    private IFixedValueParameter<MultiAnalyzer> LayerAnalyzerParameter {
      get { return (IFixedValueParameter<MultiAnalyzer>)Parameters["LayerAnalyzer"]; }
    }

    private IValueParameter<IntValue> NumberOfLayersParameter {
      get { return (IValueParameter<IntValue>)Parameters["NumberOfLayers"]; }
    }
    private IValueParameter<IntValue> PopulationSizeParameter {
      get { return (IValueParameter<IntValue>)Parameters["PopulationSize"]; }
    }

    public IConstrainedValueParameter<ISelector> SelectorParameter {
      get { return (IConstrainedValueParameter<ISelector>)Parameters["Selector"]; }
    }
    public IConstrainedValueParameter<ICrossover> CrossoverParameter {
      get { return (IConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
    }
    public IConstrainedValueParameter<IManipulator> MutatorParameter {
      get { return (IConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
    }
    private IValueParameter<PercentValue> MutationProbabilityParameter {
      get { return (IValueParameter<PercentValue>)Parameters["MutationProbability"]; }
    }
    private IValueParameter<IntValue> ElitesParameter {
      get { return (IValueParameter<IntValue>)Parameters["Elites"]; }
    }
    private IFixedValueParameter<BoolValue> ReevaluateElitesParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }
    }
    private IValueParameter<BoolValue> PlusSelectionParameter {
      get { return (IValueParameter<BoolValue>)Parameters["PlusSelection"]; }
    }

    private IValueParameter<EnumValue<AgingScheme>> AgingSchemeParameter {
      get { return (IValueParameter<EnumValue<AgingScheme>>)Parameters["AgingScheme"]; }
    }
    private IValueParameter<IntValue> AgeGapParameter {
      get { return (IValueParameter<IntValue>)Parameters["AgeGap"]; }
    }
    private IValueParameter<DoubleValue> AgeInheritanceParameter {
      get { return (IValueParameter<DoubleValue>)Parameters["AgeInheritance"]; }
    }
    private IValueParameter<IntArray> AgeLimitsParameter {
      get { return (IValueParameter<IntArray>)Parameters["AgeLimits"]; }
    }

    private IValueParameter<IntValue> MatingPoolRangeParameter {
      get { return (IValueParameter<IntValue>)Parameters["MatingPoolRange"]; }
    }
    private IValueParameter<BoolValue> ReduceToPopulationSizeParameter {
      get { return (IValueParameter<BoolValue>)Parameters["ReduceToPopulationSize"]; }
    }

    private IValueParameter<MultiTerminator> TerminatorParameter {
      get { return (IValueParameter<MultiTerminator>)Parameters["Terminator"]; }
    }
    #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; }
    }
    public MultiAnalyzer LayerAnalyzer {
      get { return LayerAnalyzerParameter.Value; }
    }

    public IntValue NumberOfLayers {
      get { return NumberOfLayersParameter.Value; }
      set { NumberOfLayersParameter.Value = value; }
    }
    public IntValue PopulationSize {
      get { return PopulationSizeParameter.Value; }
      set { PopulationSizeParameter.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 IManipulator Mutator {
      get { return MutatorParameter.Value; }
      set { MutatorParameter.Value = value; }
    }
    public PercentValue MutationProbability {
      get { return MutationProbabilityParameter.Value; }
      set { MutationProbabilityParameter.Value = value; }
    }
    public IntValue Elites {
      get { return ElitesParameter.Value; }
      set { ElitesParameter.Value = value; }
    }
    public bool ReevaluteElites {
      get { return ReevaluateElitesParameter.Value.Value; }
      set { ReevaluateElitesParameter.Value.Value = value; }
    }
    public bool PlusSelection {
      get { return PlusSelectionParameter.Value.Value; }
      set { PlusSelectionParameter.Value.Value = value; }
    }

    public EnumValue<AgingScheme> AgingScheme {
      get { return AgingSchemeParameter.Value; }
      set { AgingSchemeParameter.Value = value; }
    }
    public IntValue AgeGap {
      get { return AgeGapParameter.Value; }
      set { AgeGapParameter.Value = value; }
    }
    public DoubleValue AgeInheritance {
      get { return AgeInheritanceParameter.Value; }
      set { AgeInheritanceParameter.Value = value; }
    }
    public IntArray AgeLimits {
      get { return AgeLimitsParameter.Value; }
      set { AgeLimitsParameter.Value = value; }
    }

    public IntValue MatingPoolRange {
      get { return MatingPoolRangeParameter.Value; }
      set { MatingPoolRangeParameter.Value = value; }
    }

    public MultiTerminator Terminators {
      get { return TerminatorParameter.Value; }
    }

    public int MaximumGenerations {
      get { return generationsTerminator.Threshold.Value; }
      set { generationsTerminator.Threshold.Value = value; }
    }
    #endregion

    #region Helper Properties
    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;
    [Storable]
    private OldestAverageYoungestAgeAnalyzer ageAnalyzer;
    [Storable]
    private OldestAverageYoungestAgeAnalyzer layerAgeAnalyzer;
    [Storable]
    private AgeDistributionAnalyzer ageDistributionAnalyzer;
    [Storable]
    private AgeDistributionAnalyzer layerAgeDistributionAnalyzer;
    #endregion

    #region Preconfigured Terminators
    [Storable]
    private ComparisonTerminator<IntValue> generationsTerminator;
    [Storable]
    private ComparisonTerminator<IntValue> evaluationsTerminator;
    [Storable]
    private SingleObjectiveQualityTerminator qualityTerminator;
    [Storable]
    private ExecutionTimeTerminator executionTimeTerminator;
    #endregion

    #region Constructors
    [StorableConstructor]
    private AlpsGeneticAlgorithm(bool deserializing)
      : base(deserializing) { }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      Initialize();
    }
    private AlpsGeneticAlgorithm(AlpsGeneticAlgorithm original, Cloner cloner)
      : base(original, cloner) {
      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
      layerQualityAnalyzer = cloner.Clone(original.layerQualityAnalyzer);
      ageAnalyzer = cloner.Clone(original.ageAnalyzer);
      layerAgeAnalyzer = cloner.Clone(original.layerAgeAnalyzer);
      ageDistributionAnalyzer = cloner.Clone(original.ageDistributionAnalyzer);
      layerAgeDistributionAnalyzer = cloner.Clone(original.layerAgeDistributionAnalyzer);
      generationsTerminator = cloner.Clone(original.generationsTerminator);
      evaluationsTerminator = cloner.Clone(original.evaluationsTerminator);
      qualityTerminator = cloner.Clone(original.qualityTerminator);
      executionTimeTerminator = cloner.Clone(original.executionTimeTerminator);
      Initialize();
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new AlpsGeneticAlgorithm(this, cloner);
    }
    public AlpsGeneticAlgorithm()
      : base() {
      #region Add parameters
      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 FixedValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze all individuals from all layers combined.", new MultiAnalyzer()));
      Parameters.Add(new FixedValueParameter<MultiAnalyzer>("LayerAnalyzer", "The operator used to analyze each layer.", new MultiAnalyzer()));

      Parameters.Add(new ValueParameter<IntValue>("NumberOfLayers", "The number of layers.", new IntValue(10)));
      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions in each layer.", new IntValue(100)));

      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 OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate 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 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 });
      Parameters.Add(new ValueParameter<BoolValue>("PlusSelection", "Include the parents in the selection of the invividuals for the next generation.", new BoolValue(false)));

      Parameters.Add(new ValueParameter<EnumValue<AgingScheme>>("AgingScheme", "The aging scheme for setting the age-limits for the layers.", new EnumValue<AgingScheme>(ALPS.AgingScheme.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(20)));
      Parameters.Add(new ValueParameter<DoubleValue>("AgeInheritance", "A weight that determines the age of a child after crossover based on the older (1.0) and younger (0.0) parent.", new DoubleValue(1.0)) { Hidden = true });
      Parameters.Add(new ValueParameter<IntArray>("AgeLimits", "The maximum age an individual is allowed to reach in a certain layer.", new IntArray(new int[0])) { Hidden = true });

      Parameters.Add(new ValueParameter<IntValue>("MatingPoolRange", "The range of layers used for creating a mating pool. (1 = current + previous layer)", new IntValue(1)) { Hidden = true });
      Parameters.Add(new ValueParameter<BoolValue>("ReduceToPopulationSize", "Reduce the CurrentPopulationSize after elder migration to PopulationSize", new BoolValue(true)) { Hidden = true });

      Parameters.Add(new ValueParameter<MultiTerminator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop.", new MultiTerminator()));
      #endregion

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

      #region Create and parameterize operator graph
      OperatorGraph.InitialOperator = globalRandomCreator;

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

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

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

      localRandomCreator.Successor = layerSolutionsCreator;

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

      initializeAgeProcessor.Operator = initializeAge;
      initializeAgeProcessor.Successor = initializeCurrentPopulationSize;

      initializeCurrentPopulationSize.ValueParameter.ActualName = "CurrentPopulationSize";
      initializeCurrentPopulationSize.Successor = initializeLocalEvaluatedSolutions;

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

      initializeLocalEvaluatedSolutions.LeftSideParameter.ActualName = "LayerEvaluatedSolutions";
      initializeLocalEvaluatedSolutions.RightSideParameter.ActualName = "CurrentPopulationSize";
      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;

      mainLoop.GlobalRandomParameter.ActualName = "GlobalRandom";
      mainLoop.LocalRandomParameter.ActualName = localRandomCreator.LocalRandomParameter.Name;
      mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
      mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
      mainLoop.LayerAnalyzerParameter.ActualName = LayerAnalyzerParameter.Name;
      mainLoop.NumberOfLayersParameter.ActualName = NumberOfLayersParameter.Name;
      mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
      mainLoop.CurrentPopulationSizeParameter.ActualName = "CurrentPopulationSize";
      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
      mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
      mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
      mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
      mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
      mainLoop.PlusSelectionParameter.ActualName = PlusSelectionParameter.Name;
      mainLoop.AgeParameter.ActualName = "Age";
      mainLoop.AgeGapParameter.ActualName = AgeGapParameter.Name;
      mainLoop.AgeInheritanceParameter.ActualName = AgeInheritanceParameter.Name;
      mainLoop.AgeLimitsParameter.ActualName = AgeLimitsParameter.Name;
      mainLoop.MatingPoolRangeParameter.ActualName = MatingPoolRangeParameter.Name;
      mainLoop.ReduceToPopulationSizeParameter.ActualName = ReduceToPopulationSizeParameter.Name;
      mainLoop.TerminatorParameter.ActualName = TerminatorParameter.Name;
      #endregion

      #region Set selectors
      foreach (var selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(s => !(s is IMultiObjectiveSelector)).OrderBy(s => Name))
        SelectorParameter.ValidValues.Add(selector);
      var defaultSelector = SelectorParameter.ValidValues.OfType<GeneralizedRankSelector>().FirstOrDefault();
      if (defaultSelector != null) {
        defaultSelector.PressureParameter.Value = new DoubleValue(4.0);
        SelectorParameter.Value = defaultSelector;
      }
      #endregion

      #region Create analyzers
      qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
      layerQualityAnalyzer = new BestAverageWorstQualityAnalyzer();
      ageAnalyzer = new OldestAverageYoungestAgeAnalyzer();
      layerAgeAnalyzer = new OldestAverageYoungestAgeAnalyzer();
      ageDistributionAnalyzer = new AgeDistributionAnalyzer();
      layerAgeDistributionAnalyzer = new AgeDistributionAnalyzer();
      #endregion

      #region Create terminators
      generationsTerminator = new ComparisonTerminator<IntValue>("Generations", ComparisonType.Less, new IntValue(1000)) { Name = "Generations" };
      evaluationsTerminator = new ComparisonTerminator<IntValue>("EvaluatedSolutions", ComparisonType.Less, new IntValue(int.MaxValue)) { Name = "Evaluations" };
      qualityTerminator = new SingleObjectiveQualityTerminator() { Name = "Quality" };
      executionTimeTerminator = new ExecutionTimeTerminator(this, new TimeSpanValue(TimeSpan.FromMinutes(5)));
      #endregion

      #region Parameterize
      UpdateAnalyzers();
      ParameterizeAnalyzers();

      ParameterizeSelectors();

      UpdateTerminators();

      ParameterizeAgeLimits();
      #endregion

      Initialize();
    }
    #endregion

    #region Events
    public override void Prepare() {
      if (Problem != null)
        base.Prepare();
    }
    protected override void OnProblemChanged() {
      base.OnProblemChanged();
      ParameterizeStochasticOperator(Problem.SolutionCreator);
      ParameterizeStochasticOperatorForLayer(Problem.Evaluator);
      foreach (var @operator in Problem.Operators.OfType<IOperator>())
        ParameterizeStochasticOperator(@operator);

      ParameterizeIterationBasedOperators();

      ParameterizeSolutionsCreator();
      ParameterizeMainLoop();
      ParameterizeAnalyzers();
      ParameterizeSelectors();
      ParameterizeTerminators();

      Problem.Evaluator.QualityParameter.ActualNameChanged += Evaluator_QualityParameter_ActualNameChanged;

      UpdateAnalyzers();
      UpdateCrossovers();
      UpdateMutators();
      UpdateTerminators();
    }

    protected override void RegisterProblemEvents() {
      base.RegisterProblemEvents();
      var maximizationParameter = (IValueParameter<BoolValue>)Problem.MaximizationParameter;
      if (maximizationParameter != null) maximizationParameter.ValueChanged += new EventHandler(MaximizationParameter_ValueChanged);
    }
    protected override void DeregisterProblemEvents() {
      var maximizationParameter = (IValueParameter<BoolValue>)Problem.MaximizationParameter;
      if (maximizationParameter != null) maximizationParameter.ValueChanged -= new EventHandler(MaximizationParameter_ValueChanged);
      base.DeregisterProblemEvents();
    }

    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
      base.Problem_SolutionCreatorChanged(sender, e);
      ParameterizeStochasticOperator(Problem.SolutionCreator);
      ParameterizeStochasticOperatorForLayer(Problem.Evaluator);

      Problem.Evaluator.QualityParameter.ActualNameChanged += Evaluator_QualityParameter_ActualNameChanged;

      ParameterizeSolutionsCreator();
      ParameterizeAnalyzers();
    }
    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
      base.Problem_EvaluatorChanged(sender, e);
      foreach (var @operator in Problem.Operators.OfType<IOperator>())
        ParameterizeStochasticOperator(@operator);

      UpdateAnalyzers();

      ParameterizeSolutionsCreator();
      ParameterizeMainLoop();
      ParameterizeSelectors();
    }
    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
      base.Problem_OperatorsChanged(sender, e);
      ParameterizeIterationBasedOperators();
      UpdateCrossovers();
      UpdateMutators();
      UpdateTerminators();
    }
    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
      ParameterizeMainLoop();
      ParameterizeAnalyzers();
      ParameterizeSelectors();
    }
    private void MaximizationParameter_ValueChanged(object sender, EventArgs e) {
      ParameterizeTerminators();
    }
    private void QualityAnalyzer_CurrentBestQualityParameter_NameChanged(object sender, EventArgs e) {
      ParameterizeTerminators();
    }

    private void AgeGapParameter_ValueChanged(object sender, EventArgs e) {
      AgeGap.ValueChanged += AgeGap_ValueChanged;
      ParameterizeAgeLimits();
    }
    private void AgeGap_ValueChanged(object sender, EventArgs e) {
      ParameterizeAgeLimits();
    }
    private void AgingSchemeParameter_ValueChanged(object sender, EventArgs e) {
      AgingScheme.ValueChanged += AgingScheme_ValueChanged;
      ParameterizeAgeLimits();
    }
    private void AgingScheme_ValueChanged(object sender, EventArgs e) {
      ParameterizeAgeLimits();
    }
    private void NumberOfLayersParameter_ValueChanged(object sender, EventArgs e) {
      NumberOfLayers.ValueChanged += NumberOfLayers_ValueChanged;
      ParameterizeAgeLimits();
    }
    private void NumberOfLayers_ValueChanged(object sender, EventArgs e) {
      ParameterizeAgeLimits();
    }

    private void AnalyzerOperators_ItemsAdded(object sender, CollectionItemsChangedEventArgs<IndexedItem<IAnalyzer>> e) {
      foreach (var analyzer in e.Items) {
        foreach (var parameter in analyzer.Value.Parameters.OfType<IScopeTreeLookupParameter>()) {
          parameter.Depth = 2;
        }
      }
    }
    private void LayerAnalyzerOperators_ItemsAdded(object sender, CollectionItemsChangedEventArgs<IndexedItem<IAnalyzer>> e) {
      foreach (var analyzer in e.Items) {
        IParameter resultParameter;
        if (analyzer.Value.Parameters.TryGetValue("Results", out resultParameter)) {
          var lookupParameter = resultParameter as ILookupParameter;
          if (lookupParameter != null)
            lookupParameter.ActualName = "LayerResults";
        }
        foreach (var parameter in analyzer.Value.Parameters.OfType<IScopeTreeLookupParameter>()) {
          parameter.Depth = 1;
        }
      }
    }
    #endregion

    #region Parameterization
    private void Initialize() {
      if (Problem != null)
        Problem.Evaluator.QualityParameter.ActualNameChanged += Evaluator_QualityParameter_ActualNameChanged;

      NumberOfLayersParameter.ValueChanged += NumberOfLayersParameter_ValueChanged;
      NumberOfLayers.ValueChanged += NumberOfLayers_ValueChanged;

      Analyzer.Operators.ItemsAdded += AnalyzerOperators_ItemsAdded;
      LayerAnalyzer.Operators.ItemsAdded += LayerAnalyzerOperators_ItemsAdded;

      AgeGapParameter.ValueChanged += AgeGapParameter_ValueChanged;
      AgeGap.ValueChanged += AgeGap_ValueChanged;
      AgingSchemeParameter.ValueChanged += AgingSchemeParameter_ValueChanged;
      AgingScheme.ValueChanged += AgingScheme_ValueChanged;

      qualityAnalyzer.CurrentBestQualityParameter.NameChanged += new EventHandler(QualityAnalyzer_CurrentBestQualityParameter_NameChanged);
    }
    private void ParameterizeSolutionsCreator() {
      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
    }
    private void ParameterizeMainLoop() {
      MainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
      MainLoop.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
      MainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
    }
    private void ParameterizeAnalyzers() {
      qualityAnalyzer.ResultsParameter.ActualName = "Results";
      qualityAnalyzer.ResultsParameter.Hidden = true;
      qualityAnalyzer.QualityParameter.Depth = 2;
      layerQualityAnalyzer.ResultsParameter.ActualName = "LayerResults";
      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 ParameterizeSelectors() {
      foreach (var selector in SelectorParameter.ValidValues) {
        selector.CopySelected = new BoolValue(true);
        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 ParameterizeTerminators() {
      qualityTerminator.Parameterize(qualityAnalyzer.CurrentBestQualityParameter, Problem);
    }
    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 = generationsTerminator.ThresholdParameter.Name;
          @operator.MaximumIterationsParameter.Hidden = true;
        }
      }
    }
    private void ParameterizeAgeLimits() {
      var scheme = AgingScheme.Value;
      int ageGap = AgeGap.Value;
      int numberOfLayers = NumberOfLayers.Value;
      AgeLimits = scheme.CalculateAgeLimits(ageGap, numberOfLayers);
    }

    private void ParameterizeStochasticOperator(IOperator @operator) {
      var stochasticOperator = @operator as IStochasticOperator;
      if (stochasticOperator != null) {
        stochasticOperator.RandomParameter.ActualName = "GlobalRandom";
        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 UpdateAnalyzers() {
      Analyzer.Operators.Clear();
      LayerAnalyzer.Operators.Clear();

      Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);
      Analyzer.Operators.Add(ageAnalyzer, ageAnalyzer.EnabledByDefault);
      Analyzer.Operators.Add(ageDistributionAnalyzer, ageDistributionAnalyzer.EnabledByDefault);
      LayerAnalyzer.Operators.Add(layerQualityAnalyzer, false);
      LayerAnalyzer.Operators.Add(layerAgeAnalyzer, false);
      LayerAnalyzer.Operators.Add(layerAgeDistributionAnalyzer, false);

      if (Problem != null) {
        foreach (var analyzer in Problem.Operators.OfType<IAnalyzer>()) {
          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
          LayerAnalyzer.Operators.Add((IAnalyzer)analyzer.Clone(), false);
        }
      }
    }
    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 UpdateTerminators() {
      var newTerminators = new Dictionary<ITerminator, bool> {
        {generationsTerminator, !Terminators.Operators.Contains(generationsTerminator) || Terminators.Operators.ItemChecked(generationsTerminator)},
        {evaluationsTerminator, Terminators.Operators.Contains(evaluationsTerminator) && Terminators.Operators.ItemChecked(evaluationsTerminator)},
        {qualityTerminator, Terminators.Operators.Contains(qualityTerminator) && Terminators.Operators.ItemChecked(qualityTerminator) },
        {executionTimeTerminator, Terminators.Operators.Contains(executionTimeTerminator) && Terminators.Operators.ItemChecked(executionTimeTerminator)}
      };
      if (Problem != null) {
        foreach (var terminator in Problem.Operators.OfType<ITerminator>())
          newTerminators.Add(terminator, !Terminators.Operators.Contains(terminator) || Terminators.Operators.ItemChecked(terminator));
      }

      Terminators.Operators.Clear();

      foreach (var newTerminator in newTerminators)
        Terminators.Operators.Add(newTerminator.Key, newTerminator.Value);
    }
    #endregion
  }
}