source: branches/ScopedAlgorithms/HeuristicLab.Selection/3.3/TournamentSelector.cs @ 16099

#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.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Optimization.Selection;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Selection {
  /// <summary>
  /// A tournament selection operator which considers a single double quality value for selection.
  /// </summary>
  [Item("TournamentSelector", "A tournament selection operator which considers a single double quality value for selection.")]
  [StorableClass]
  public sealed class TournamentSelector : StochasticSingleObjectiveSelector, ISingleObjectiveSelector {
    public ValueLookupParameter<IntValue> GroupSizeParameter {
      get { return (ValueLookupParameter<IntValue>)Parameters["GroupSize"]; }
    }

    [StorableConstructor]
    private TournamentSelector(bool deserializing) : base(deserializing) { }
    private TournamentSelector(TournamentSelector original, Cloner cloner) : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new TournamentSelector(this, cloner);
    }

    public TournamentSelector()
      : base() {
      Parameters.Add(new ValueLookupParameter<IntValue>("GroupSize", "The size of the tournament group.", new IntValue(2)));
    }

    protected override IScope[] Select(List<IScope> scopes) {
      int count = NumberOfSelectedSubScopesParameter.ActualValue.Value;
      bool copy = CopySelectedParameter.Value.Value;
      IRandom random = RandomParameter.ActualValue;
      bool maximization = MaximizationParameter.ActualValue.Value;
      List<double> qualities = QualityParameter.ActualValue.Where(x => IsValidQuality(x.Value)).Select(x => x.Value).ToList();
      int groupSize = GroupSizeParameter.ActualValue.Value;
      IScope[] selected = new IScope[count];

      //check if list with indexes is as long as the original scope list
      //otherwise invalid quality values were filtered
      if (qualities.Count != scopes.Count) {
        throw new ArgumentException("The scopes contain invalid quality values (either infinity or double.NaN) on which the selector cannot operate.");
      }

      for (int i = 0; i < count; i++) {
        int best = random.Next(scopes.Count);
        int index;
        for (int j = 1; j < groupSize; j++) {
          index = random.Next(scopes.Count);
          if (((maximization) && (qualities[index] > qualities[best])) ||
              ((!maximization) && (qualities[index] < qualities[best]))) {
            best = index;
          }
        }

        if (copy)
          selected[i] = (IScope)scopes[best].Clone();
        else {
          selected[i] = scopes[best];
          scopes.RemoveAt(best);
          qualities.RemoveAt(best);
        }
      }
      return selected;
    }
  }

  [Item("Tournament Selector", "", ExcludeGenericTypeInfo = true)]
  [StorableClass]
  public sealed class TournamentSelector<TContext, TProblem, TEncoding, TSolution> : ParameterizedNamedItem, ISelector<TContext>
      where TContext : ISingleObjectivePopulationContext<TSolution>, IMatingpoolContext<TSolution>, IStochasticContext,
                       IProblemContext<TProblem, TEncoding, TSolution>
      where TProblem : class, ISingleObjectiveProblem<TEncoding, TSolution>, ISingleObjectiveProblemDefinition<TEncoding, TSolution>
      where TEncoding : class, IEncoding<TSolution>
      where TSolution : class, ISolution {

    [Storable]
    private IValueParameter<IntValue> groupSizeParameter;
    public int GroupSize {
      get { return groupSizeParameter.Value.Value; }
      set {
        if (value < 1) throw new ArgumentException("Cannot use a group size less than 1 in tournament selection.");
        groupSizeParameter.Value.Value = value;
      }
    }
    
    [StorableConstructor]
    private TournamentSelector(bool deserializing) : base(deserializing) { }
    private TournamentSelector(TournamentSelector<TContext, TProblem, TEncoding, TSolution> original, Cloner cloner)
      : base(original, cloner) {
      groupSizeParameter = cloner.Clone(groupSizeParameter);
    }
    public TournamentSelector() {
      Parameters.Add(groupSizeParameter = new ValueParameter<IntValue>("GroupSize", "The group size that competes in the tournament.", new IntValue(2)));
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new TournamentSelector<TContext, TProblem, TEncoding, TSolution>(this, cloner);
    }

    public void Select(TContext context, int n, bool withRepetition) {
      context.MatingPool = Select(context.Random, context.Problem.IsBetter, context.Population, GroupSize, n, withRepetition);
    }

    public static IEnumerable<ISingleObjectiveSolutionScope<TSolution>> Select(IRandom random, Func<double, double, bool> isBetterFunc, IEnumerable<ISingleObjectiveSolutionScope<TSolution>> population, int groupSize, int n, bool withRepetition) {
      var pop = population.Where(x => !double.IsNaN(x.Fitness)).ToList();

      var i = n;
      while (i > 0 && pop.Count > 0) {
        var best = random.Next(pop.Count);
        for (var j = 1; j < groupSize; j++) {
          var index = random.Next(pop.Count);
          if (isBetterFunc(pop[index].Fitness, pop[best].Fitness)) {
            best = index;
          }
        }

        yield return pop[best];
        i--;
        if (!withRepetition) {
          pop.RemoveAt(i);
        }
      }
    }
  }
}