source: branches/ScatterSearch/HeuristicLab.Algorithms.ScatterSearch/3.3/Knapsack/NChildCrossover.cs @ 7756

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Algorithms.ScatterSearch.Knapsack {
  /// <summary>
  /// N child crossover for binary vectors.
  /// </summary>
  [Item("NChildCrossover", "N child crossover for binary vectors.")]
  [StorableClass]
  public sealed class NChildCrossover : NBinaryVectorCrossover {
    #region Parameter properties
    public IValueLookupParameter<IntValue> NParameter {
      get { return (IValueLookupParameter<IntValue>)Parameters["N"]; }
    }
    #endregion

    [StorableConstructor]
    private NChildCrossover(bool deserializing) : base(deserializing) { }
    private NChildCrossover(NChildCrossover original, Cloner cloner) : base(original, cloner) { }
    public NChildCrossover()
      : base() {
      #region Create parameters
      Parameters.Add(new ValueLookupParameter<IntValue>("N", "Number of children.", new IntValue(2)));
      #endregion
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new NChildCrossover(this, cloner);
    }

    public static ItemArray<BinaryVector> Apply(IRandom random, BinaryVector parent1, BinaryVector parent2, IntValue n) {
      if (parent1.Length != parent2.Length)
        throw new ArgumentException("NChildCrossover: The parents are of different length.");

      if (n.Value > Math.Pow(2, parent1.Length) - 2)
        throw new ArgumentException("NChildCrossover: There cannot be more children than 2^size of parents - 2.");

      if (n.Value < 1)
        throw new ArgumentException("NChildCrossover: N cannot be < 1.");

      var solutions = new BinaryVector[n.Value];
      for (int i = 0; i < n.Value; i++) {
        var solution = new BinaryVector(parent1.Length);
        for (int j = 0; j < solution.Length; j++) {
          solution[j] = random.Next(2) % 2 == 0 ? parent1[j] : parent2[j];
        }
        solutions[i] = solution;
      }

      return new ItemArray<BinaryVector>(solutions);
    }

    protected override ItemArray<BinaryVector> Cross(IRandom random, ItemArray<BinaryVector> parents) {
      if (parents.Length != 2) throw new ArgumentException("NChildCrossover: The number of parents is not equal to 2.");

      if (NParameter.ActualValue == null) throw new InvalidOperationException("NChildCrossover: Parameter " + NParameter.ActualName + " could not be found.");

      return Apply(random, parents[0], parents[1], NParameter.Value);
    }
  }
}