1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022018 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 


22  using System;


23  using HeuristicLab.Common;


24  using HeuristicLab.Core;


25  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


26 


27  namespace 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, SpringerVerlag 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, SpringerVerlag 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  }

