source: branches/2987_MOEAD_Algorithm/HeuristicLab.Algorithms.MOEAD/3.4/MOEADAlgorithmBase.cs @ 16583

using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.ExpressionGenerator;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;
using System;
using System.Collections.Generic;
using System.Linq;

using CancellationToken = System.Threading.CancellationToken;

namespace HeuristicLab.Algorithms.MOEAD {
  [Item("MOEADAlgorithmBase", "Base class for all MOEA/D algorithm variants.")]
  [StorableClass]
  public abstract class MOEADAlgorithmBase : BasicAlgorithm {
    #region data members
    protected enum NeighborType { NEIGHBOR, POPULATION }
    // TCHE = Chebyshev (Tchebyshev)
    // PBI  = Penalty-based boundary intersection
    // AGG  = Weighted sum
    public enum FunctionType { TCHE, PBI, AGG }

    [Storable]
    protected double[] IdealPoint { get; set; }
    [Storable]
    protected double[] NadirPoint { get; set; } // potentially useful for objective normalization

    [Storable]
    protected double[][] lambda;

    [Storable]
    protected int[][] neighbourhood;

    [Storable]
    protected IList<IMOEADSolution> solutions;

    [Storable]
    protected FunctionType functionType;

    [Storable]
    protected List<IMOEADSolution> population;

    [Storable]
    protected List<IMOEADSolution> offspringPopulation;

    [Storable]
    protected List<IMOEADSolution> jointPopulation;

    [Storable]
    protected int evaluatedSolutions;

    [Storable]
    protected ExecutionContext executionContext;

    [Storable]
    protected IScope globalScope;

    [Storable]
    protected ExecutionState previousExecutionState;
    #endregion

    #region parameters
    private const string SeedParameterName = "Seed";
    private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
    private const string PopulationSizeParameterName = "PopulationSize";
    private const string ResultPopulationSizeParameterName = "ResultPopulationSize";
    private const string CrossoverProbabilityParameterName = "CrossoverProbability";
    private const string CrossoverParameterName = "Crossover";
    private const string MutationProbabilityParameterName = "MutationProbability";
    private const string MutatorParameterName = "Mutator";
    private const string MaximumEvaluatedSolutionsParameterName = "MaximumEvaluatedSolutions";
    private const string RandomParameterName = "Random";
    private const string AnalyzerParameterName = "Analyzer";
    // MOEA-D parameters
    private const string NeighbourSizeParameterName = "NeighbourSize";
    private const string NeighbourhoodSelectionProbabilityParameterName = "NeighbourhoodSelectionProbability";
    private const string MaximumNumberOfReplacedSolutionsParameterName = "MaximumNumberOfReplacedSolutions";
    private const string FunctionTypeParameterName = "FunctionType";

    public IValueParameter<MultiAnalyzer> AnalyzerParameter {
      get { return (ValueParameter<MultiAnalyzer>)Parameters[AnalyzerParameterName]; }
    }

    public IConstrainedValueParameter<StringValue> FunctionTypeParameter {
      get { return (IConstrainedValueParameter<StringValue>)Parameters[FunctionTypeParameterName]; }
    }
    public IFixedValueParameter<IntValue> NeighbourSizeParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters[NeighbourSizeParameterName]; }
    }
    public IFixedValueParameter<IntValue> MaximumNumberOfReplacedSolutionsParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters[MaximumNumberOfReplacedSolutionsParameterName]; }
    }
    public IFixedValueParameter<DoubleValue> NeighbourhoodSelectionProbabilityParameter {
      get { return (IFixedValueParameter<DoubleValue>)Parameters[NeighbourhoodSelectionProbabilityParameterName]; }
    }
    public IFixedValueParameter<IntValue> SeedParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
    }
    public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
    }
    private IValueParameter<IntValue> PopulationSizeParameter {
      get { return (IValueParameter<IntValue>)Parameters[PopulationSizeParameterName]; }
    }
    private IValueParameter<IntValue> ResultPopulationSizeParameter {
      get { return (IValueParameter<IntValue>)Parameters[ResultPopulationSizeParameterName]; }
    }
    public IValueParameter<PercentValue> CrossoverProbabilityParameter {
      get { return (IValueParameter<PercentValue>)Parameters[CrossoverProbabilityParameterName]; }
    }
    public IConstrainedValueParameter<ICrossover> CrossoverParameter {
      get { return (IConstrainedValueParameter<ICrossover>)Parameters[CrossoverParameterName]; }
    }
    public IValueParameter<PercentValue> MutationProbabilityParameter {
      get { return (IValueParameter<PercentValue>)Parameters[MutationProbabilityParameterName]; }
    }
    public IConstrainedValueParameter<IManipulator> MutatorParameter {
      get { return (IConstrainedValueParameter<IManipulator>)Parameters[MutatorParameterName]; }
    }
    public IValueParameter<IntValue> MaximumEvaluatedSolutionsParameter {
      get { return (IValueParameter<IntValue>)Parameters[MaximumEvaluatedSolutionsParameterName]; }
    }
    public IValueParameter<IRandom> RandomParameter {
      get { return (IValueParameter<IRandom>)Parameters[RandomParameterName]; }
    }
    #endregion

    #region parameter properties
    public new IMultiObjectiveHeuristicOptimizationProblem Problem {
      get { return (IMultiObjectiveHeuristicOptimizationProblem)base.Problem; }
    }
    public int Seed {
      get { return SeedParameter.Value.Value; }
      set { SeedParameter.Value.Value = value; }
    }
    public bool SetSeedRandomly {
      get { return SetSeedRandomlyParameter.Value.Value; }
      set { SetSeedRandomlyParameter.Value.Value = value; }
    }
    public IntValue PopulationSize {
      get { return PopulationSizeParameter.Value; }
      set { PopulationSizeParameter.Value = value; }
    }
    public IntValue ResultPopulationSize {
      get { return ResultPopulationSizeParameter.Value; }
      set { ResultPopulationSizeParameter.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 MultiAnalyzer Analyzer {
      get { return AnalyzerParameter.Value; }
      set { AnalyzerParameter.Value = value; }
    }
    public IntValue MaximumEvaluatedSolutions {
      get { return MaximumEvaluatedSolutionsParameter.Value; }
      set { MaximumEvaluatedSolutionsParameter.Value = value; }
    }
    public int NeighbourSize {
      get { return NeighbourSizeParameter.Value.Value; }
      set { NeighbourSizeParameter.Value.Value = value; }
    }
    public int MaximumNumberOfReplacedSolutions {
      get { return MaximumNumberOfReplacedSolutionsParameter.Value.Value; }
      set { MaximumNumberOfReplacedSolutionsParameter.Value.Value = value; }
    }
    public double NeighbourhoodSelectionProbability {
      get { return NeighbourhoodSelectionProbabilityParameter.Value.Value; }
      set { NeighbourhoodSelectionProbabilityParameter.Value.Value = value; }
    }
    #endregion

    #region constructors
    public MOEADAlgorithmBase() {
      Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
      Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
      Parameters.Add(new ValueParameter<IntValue>(PopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
      Parameters.Add(new ValueParameter<IntValue>(ResultPopulationSizeParameterName, "The size of the population of solutions.", new IntValue(100)));
      Parameters.Add(new ValueParameter<PercentValue>(CrossoverProbabilityParameterName, "The probability that the crossover operator is applied.", new PercentValue(0.9)));
      Parameters.Add(new ConstrainedValueParameter<ICrossover>(CrossoverParameterName, "The operator used to cross solutions."));
      Parameters.Add(new ValueParameter<PercentValue>(MutationProbabilityParameterName, "The probability that the mutation operator is applied on a solution.", new PercentValue(0.25)));
      Parameters.Add(new ConstrainedValueParameter<IManipulator>(MutatorParameterName, "The operator used to mutate solutions."));
      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
      Parameters.Add(new ValueParameter<IntValue>(MaximumEvaluatedSolutionsParameterName, "The maximum number of evaluated solutions (approximately).", new IntValue(100_000)));
      Parameters.Add(new ValueParameter<IRandom>(RandomParameterName, new MersenneTwister()));
      Parameters.Add(new FixedValueParameter<IntValue>(NeighbourSizeParameterName, new IntValue(20)));
      Parameters.Add(new FixedValueParameter<IntValue>(MaximumNumberOfReplacedSolutionsParameterName, new IntValue(2)));
      Parameters.Add(new FixedValueParameter<DoubleValue>(NeighbourhoodSelectionProbabilityParameterName, new DoubleValue(0.1)));

      var functionTypeParameter = new ConstrainedValueParameter<StringValue>(FunctionTypeParameterName);
      foreach (var s in new[] { "Chebyshev", "PBI", "Weighted Sum" }) {
        functionTypeParameter.ValidValues.Add(new StringValue(s));
      }
      Parameters.Add(functionTypeParameter);
    }

    protected MOEADAlgorithmBase(MOEADAlgorithmBase original, Cloner cloner) : base(original, cloner) {
      functionType = original.functionType;
      evaluatedSolutions = original.evaluatedSolutions;
      previousExecutionState = original.previousExecutionState;

      if (original.IdealPoint != null) {
        IdealPoint = (double[])original.IdealPoint.Clone();
      }

      if (original.NadirPoint != null) {
        NadirPoint = (double[])original.NadirPoint.Clone();
      }

      if (original.lambda != null) {
        lambda = (double[][])original.lambda.Clone();
      }

      if (original.neighbourhood != null) {
        neighbourhood = (int[][])original.neighbourhood.Clone();
      }

      if (original.solutions != null) {
        solutions = original.solutions.Select(cloner.Clone).ToArray();
      }

      if (original.population != null) {
        population = original.population.Select(cloner.Clone).ToList();
      }

      if (original.offspringPopulation != null) {
        offspringPopulation = original.offspringPopulation.Select(cloner.Clone).ToList();
      }

      if (original.jointPopulation != null) {
        jointPopulation = original.jointPopulation.Select(x => cloner.Clone(x)).ToList();
      }

      if (original.executionContext != null) {
        executionContext = cloner.Clone(original.executionContext);
      }

      if (original.globalScope != null) {
        globalScope = cloner.Clone(original.globalScope);
      }
    }

    [StorableConstructor]
    protected MOEADAlgorithmBase(bool deserializing) : base(deserializing) { }
    #endregion

    private void InitializePopulation(ExecutionContext executionContext, CancellationToken cancellationToken, IRandom random, bool[] maximization) {
      var creator = Problem.SolutionCreator;
      var evaluator = Problem.Evaluator;

      var dimensions = maximization.Length;
      var populationSize = PopulationSize.Value;
      population = new List<IMOEADSolution>(populationSize);

      var parentScope = executionContext.Scope;
      // first, create all individuals
      for (int i = 0; i < populationSize; ++i) {
        var childScope = new Scope(i.ToString()) { Parent = parentScope };
        ExecuteOperation(executionContext, cancellationToken, executionContext.CreateChildOperation(creator, childScope));
        parentScope.SubScopes.Add(childScope);
      }

      // then, evaluate them and update qualities
      for (int i = 0; i < populationSize; ++i) {
        var childScope = parentScope.SubScopes[i];
        ExecuteOperation(executionContext, cancellationToken, executionContext.CreateChildOperation(evaluator, childScope));

        var qualities = (DoubleArray)childScope.Variables["Qualities"].Value;
        var solution = new MOEADSolution(childScope, dimensions, 0);
        for (int j = 0; j < dimensions; ++j) {
          solution.Qualities[j] = maximization[j] ? 1 - qualities[j] : qualities[j];
        }
        population.Add(solution);
      }
    }

    public override void Prepare() {
      base.Prepare();
    }

    protected void InitializeAlgorithm(CancellationToken cancellationToken) {
      globalScope = new Scope("Global Scope");
      executionContext = new ExecutionContext(null, this, globalScope);

      // set the execution context for parameters to allow lookup
      foreach (var parameter in Problem.Parameters.OfType<IValueParameter>()) {
        // we need all of these in order for the wiring of the operators to work
        globalScope.Variables.Add(new Variable(parameter.Name, parameter.Value));
      }
      globalScope.Variables.Add(new Variable("Results", Results)); // make results available as a parameter for analyzers etc.

      var rand = RandomParameter.Value;
      if (SetSeedRandomly) Seed = RandomSeedGenerator.GetSeed();
      rand.Reset(Seed);

      bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();
      var dimensions = maximization.Length;

      var populationSize = PopulationSize.Value;

      InitializePopulation(executionContext, cancellationToken, rand, maximization);
      InitializeUniformWeights(rand, populationSize, dimensions);
      InitializeNeighbourHood(lambda, populationSize, NeighbourSize);

      IdealPoint = new double[dimensions];
      IdealPoint.UpdateIdeal(population);

      NadirPoint = new double[dimensions];
      NadirPoint.UpdateNadir(population);

      var functionTypeString = FunctionTypeParameter.Value.Value;
      switch (functionTypeString) {
        case "Chebyshev":
          functionType = FunctionType.TCHE;
          break;
        case "PBI":
          functionType = FunctionType.PBI;
          break;
        case "Weighted Sum":
          functionType = FunctionType.AGG;
          break;
      }

      evaluatedSolutions = populationSize;
    }

    protected override void Initialize(CancellationToken cancellationToken) {
      globalScope = new Scope("Global Scope");
      executionContext = new ExecutionContext(null, this, globalScope);

      // set the execution context for parameters to allow lookup
      foreach (var parameter in Problem.Parameters.OfType<IValueParameter>()) {
        // we need all of these in order for the wiring of the operators to work
        globalScope.Variables.Add(new Variable(parameter.Name, parameter.Value));
      }
      globalScope.Variables.Add(new Variable("Results", Results)); // make results available as a parameter for analyzers etc.

      var rand = RandomParameter.Value;
      if (SetSeedRandomly) Seed = RandomSeedGenerator.GetSeed();
      rand.Reset(Seed);

      bool[] maximization = ((BoolArray)Problem.MaximizationParameter.ActualValue).CloneAsArray();
      var dimensions = maximization.Length;

      var populationSize = PopulationSize.Value;

      InitializePopulation(executionContext, cancellationToken, rand, maximization);
      InitializeUniformWeights(rand, populationSize, dimensions);
      InitializeNeighbourHood(lambda, populationSize, NeighbourSize);

      IdealPoint = new double[dimensions];
      IdealPoint.UpdateIdeal(population);

      NadirPoint = new double[dimensions];
      NadirPoint.UpdateNadir(population);

      var functionTypeString = FunctionTypeParameter.Value.Value;
      switch (functionTypeString) {
        case "Chebyshev":
          functionType = FunctionType.TCHE;
          break;
        case "PBI":
          functionType = FunctionType.PBI;
          break;
        case "Weighted Sum":
          functionType = FunctionType.AGG;
          break;
      }

      evaluatedSolutions = populationSize;

      base.Initialize(cancellationToken);
    }

    public override bool SupportsPause => true;

    protected void InitializeUniformWeights(IRandom random, int populationSize, int dimensions) {
      lambda = Enumerable.Range(0, populationSize).Select(_ => GenerateSample(random, dimensions)).ToArray();
    }

    // implements random number generation from https://en.wikipedia.org/wiki/Dirichlet_distribution#Random_number_generation
    private double[] GenerateSample(IRandom random, int dim) {
      var sum = 0d;
      var sample = new double[dim];
      for (int i = 0; i < dim; ++i) {
        sample[i] = GammaDistributedRandom.NextDouble(random, 1, 1);
        sum += sample[i];
      }
      for (int i = 0; i < dim; ++i) {
        sample[i] /= sum;
      }
      return sample;
    }

    protected void InitializeNeighbourHood(double[][] lambda, int populationSize, int neighbourSize) {
      neighbourhood = new int[populationSize][];

      var x = new double[populationSize];
      var idx = new int[populationSize];

      for (int i = 0; i < populationSize; ++i) {
        for (int j = 0; j < populationSize; ++j) {
          x[j] = MOEADUtil.EuclideanDistance(lambda[i], lambda[j]);
          idx[j] = j;
        }

        MOEADUtil.MinFastSort(x, idx, populationSize, neighbourSize);
        neighbourhood[i] = (int[])idx.Clone();
      }
    }

    protected NeighborType ChooseNeighborType(IRandom random, double neighbourhoodSelectionProbability) {
      return random.NextDouble() < neighbourhoodSelectionProbability
        ? NeighborType.NEIGHBOR
        : NeighborType.POPULATION;
    }

    protected IList<IMOEADSolution> ParentSelection(IRandom random, int subProblemId, NeighborType neighbourType) {
      List<int> matingPool = MatingSelection(random, subProblemId, 2, neighbourType);

      var parents = new IMOEADSolution[3];

      parents[0] = population[matingPool[0]];
      parents[1] = population[matingPool[1]];
      parents[2] = population[subProblemId];

      return parents;
    }

    protected List<int> MatingSelection(IRandom random, int subproblemId, int numberOfSolutionsToSelect, NeighborType neighbourType) {
      int populationSize = PopulationSize.Value;

      var listOfSolutions = new List<int>(numberOfSolutionsToSelect);

      int neighbourSize = neighbourhood[subproblemId].Length;
      while (listOfSolutions.Count < numberOfSolutionsToSelect) {
        var selectedSolution = neighbourType == NeighborType.NEIGHBOR
          ? neighbourhood[subproblemId][random.Next(neighbourSize)]
          : random.Next(populationSize);

        bool flag = true;
        foreach (int individualId in listOfSolutions) {
          if (individualId == selectedSolution) {
            flag = false;
            break;
          }
        }

        if (flag) {
          listOfSolutions.Add(selectedSolution);
        }
      }

      return listOfSolutions;
    }

    protected void UpdateNeighbourHood(IRandom random, IMOEADSolution individual, int subProblemId, NeighborType neighbourType) {
      int replacedSolutions = 0;
      int size = neighbourType == NeighborType.NEIGHBOR ? NeighbourSize : population.Count;

      foreach (var i in Enumerable.Range(0, size).Shuffle(random)) {
        int k = neighbourType == NeighborType.NEIGHBOR
          ? neighbourhood[subProblemId][i]
          : i;

        double f1 = CalculateFitness(population[k].Qualities, lambda[k]);
        double f2 = CalculateFitness(individual.Qualities, lambda[k]);

        if (f2 < f1) {
          population[k] = (IMOEADSolution)individual.Clone();
          replacedSolutions++;
        }

        if (replacedSolutions >= MaximumNumberOfReplacedSolutions) {
          return;
        }
      }
    }

    private double CalculateFitness(double[] qualities, double[] lambda) {
      double fitness;
      int dim = qualities.Length;
      switch (functionType) {
        case FunctionType.TCHE: {
            double maxFun = double.MinValue;

            for (int n = 0; n < dim; n++) {
              double diff = Math.Abs(qualities[n] - IdealPoint[n]);

              var l = lambda[n].IsAlmost(0) ? 0.0001 : lambda[n];
              //var feval = l * diff;
              // introduce objective scaling
              var feval = l * (qualities[n] - IdealPoint[n]) / (NadirPoint[n] - IdealPoint[n]);
              if (feval > maxFun) {
                maxFun = feval;
              }
            }

            fitness = maxFun;
            return fitness;
          }
        case FunctionType.AGG: {
            double sum = 0.0;
            for (int n = 0; n < dim; n++) {
              sum += lambda[n] * qualities[n];
            }

            fitness = sum;
            return fitness;
          }
        case FunctionType.PBI: {
            double d1, d2, nl;
            double theta = 5.0;
            int dimensions = dim;

            d1 = d2 = nl = 0.0;

            for (int i = 0; i < dimensions; i++) {
              d1 += (qualities[i] - IdealPoint[i]) * lambda[i];
              nl += Math.Pow(lambda[i], 2.0);
            }
            nl = Math.Sqrt(nl);
            d1 = Math.Abs(d1) / nl;

            for (int i = 0; i < dimensions; i++) {
              d2 += Math.Pow((qualities[i] - IdealPoint[i]) - d1 * (lambda[i] / nl), 2.0);
            }
            d2 = Math.Sqrt(d2);

            fitness = (d1 + theta * d2);
            return fitness;
          }
        default: {
            throw new ArgumentException($"Unknown function type: {functionType}");
          }
      }
    }

    public IList<IMOEADSolution> GetResult(IRandom random) {
      var populationSize = PopulationSize.Value;
      var resultPopulationSize = ResultPopulationSize.Value;

      if (populationSize > resultPopulationSize) {
        return MOEADUtil.GetSubsetOfEvenlyDistributedSolutions(random, population, resultPopulationSize);
      } else {
        return population;
      }
    }

    protected void UpdateParetoFronts() {
      bool dominates(Point2D<double> x, Point2D<double> y) => x.X <= y.X && x.Y <= y.Y;
      // get all non-dominated points
      var points = population.Select(x => new Point2D<double>(Math.Round(x.Qualities[0], 6), Math.Round(x.Qualities[1], 6))).OrderBy(_ => _.X).ThenBy(_ => _.Y).ToArray();
      var dominated = new bool[points.Length];

      for (int i = 0; i < points.Length; ++i) {
        if (dominated[i]) { continue; }
        for (int j = 0; j < points.Length; ++j) {
          if (i == j) { continue; }
          if (dominated[j]) { continue; }
          dominated[j] = dominates(points[i], points[j]);
        }
      }

      var pf = Enumerable.Range(0, dominated.Length).Where(x => !dominated[x]).Select(x => points[x]);

      ScatterPlot sp;
      if (!Results.ContainsKey("Pareto Front")) {
        sp = new ScatterPlot();
        sp.Rows.Add(new ScatterPlotDataRow("Pareto Front", "", pf) { VisualProperties = { PointSize = 5 } });
        Results.AddOrUpdateResult("Pareto Front", sp);
      } else {
        sp = (ScatterPlot)Results["Pareto Front"].Value;
        sp.Rows["Pareto Front"].Points.Replace(pf);
      }
    }

    #region operator wiring and events
    protected void ExecuteOperation(ExecutionContext executionContext, CancellationToken cancellationToken, IOperation operation) {
      Stack<IOperation> executionStack = new Stack<IOperation>();
      executionStack.Push(operation);
      while (executionStack.Count > 0) {
        cancellationToken.ThrowIfCancellationRequested();
        IOperation next = executionStack.Pop();
        if (next is OperationCollection) {
          OperationCollection coll = (OperationCollection)next;
          for (int i = coll.Count - 1; i >= 0; i--)
            if (coll[i] != null) executionStack.Push(coll[i]);
        } else if (next is IAtomicOperation) {
          IAtomicOperation op = (IAtomicOperation)next;
          next = op.Operator.Execute((IExecutionContext)op, cancellationToken);
          if (next != null) executionStack.Push(next);
        }
      }
    }

    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);
        }
      }
    }

    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;
        else oldMutator = null;
      }

      if (oldMutator == null && defaultMutator != null)
        MutatorParameter.Value = defaultMutator;
    }

    protected override void OnProblemChanged() {
      UpdateCrossovers();
      UpdateMutators();
      UpdateAnalyzers();
      base.OnProblemChanged();
    }

    protected override void OnExecutionStateChanged() {
      previousExecutionState = ExecutionState;
      base.OnExecutionStateChanged();
    }

    protected override void OnStopped() {
      if (solutions != null) {
        solutions.Clear();
      }
      if (population != null) {
        population.Clear();
      }
      if (offspringPopulation != null) {
        offspringPopulation.Clear();
      }
      if (jointPopulation != null) {
        jointPopulation.Clear();
      }
      executionContext.Scope.SubScopes.Clear();
      base.OnStopped();
    }
    #endregion
  }
}