source: branches/2520_PersistenceReintegration/HeuristicLab.Encodings.BinaryVectorEncoding/3.3/Crossovers/UniformCrossover.cs @ 16453

Last change on this file since 16453 was 16453, checked in by jkarder, 12 months ago

#2520: updated year of copyrights

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26
27namespace HeuristicLab.Encodings.BinaryVectorEncoding {
28  /// <summary>
29  /// Uniform crossover for binary vectors.
30  /// </summary>
31  /// <remarks>
32  /// It is implemented as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg.
33  /// </remarks>
34  [Item("UniformCrossover", "Uniform crossover for binary vectors. It is implemented as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg.")]
35  [StorableClass]
36  public sealed class UniformCrossover : BinaryVectorCrossover {
37
38    [StorableConstructor]
39    private UniformCrossover(bool deserializing) : base(deserializing) { }
40    private UniformCrossover(UniformCrossover original, Cloner cloner) : base(original, cloner) { }
41    public UniformCrossover() : base() { }
42
43    public override IDeepCloneable Clone(Cloner cloner) {
44      return new UniformCrossover(this, cloner);
45    }
46
47    /// <summary>
48    /// Performs a uniform crossover between two binary vectors.
49    /// </summary>
50    /// <param name="random">A random number generator.</param>
51    /// <param name="parent1">The first parent for crossover.</param>
52    /// <param name="parent2">The second parent for crossover.</param>
53    /// <returns>The newly created binary vector, resulting from the uniform crossover.</returns>
54    public static BinaryVector Apply(IRandom random, BinaryVector parent1, BinaryVector parent2) {
55      if (parent1.Length != parent2.Length)
56        throw new ArgumentException("UniformCrossover: The parents are of different length.");
57
58      int length = parent1.Length;
59      bool[] result = new bool[length];
60
61      for (int i = 0; i < length; i++) {
62        if (random.NextDouble() < 0.5)
63          result[i] = parent1[i];
64        else
65          result[i] = parent2[i];
66      }
67
68      return new BinaryVector(result);
69    }
70
71    /// <summary>
72    /// Performs a uniform crossover at a randomly chosen position of two
73    /// given parent binary vectors.
74    /// </summary>
75    /// <exception cref="ArgumentException">Thrown if there are not exactly two parents.</exception>
76    /// <param name="random">A random number generator.</param>
77    /// <param name="parents">An array containing the two binary vectors that should be crossed.</param>
78    /// <returns>The newly created binary vector, resulting from the uniform crossover.</returns>
79    protected override BinaryVector Cross(IRandom random, ItemArray<BinaryVector> parents) {
80      if (parents.Length != 2) throw new ArgumentException("ERROR in UniformCrossover: The number of parents is not equal to 2");
81
82      return Apply(random, parents[0], parents[1]);
83    }
84  }
85}
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