#region License Information
/* HeuristicLab
* Copyright (C) 2002-2010 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 .
*/
#endregion
using System;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Encodings.PermutationEncoding {
///
/// Performs the crossover described in the COSA optimization method.
///
///
/// It is implemented as described in Wendt, O. 1994. COSA: COoperative Simulated Annealing - Integration von Genetischen Algorithmen und Simulated Annealing am Beispiel der Tourenplanung. Dissertation Thesis. IWI Frankfurt.
/// The operator actually performs a 2-opt mutation on the first parent, but it uses the second parent to determine which new edge should be inserted.
/// Thus the mutation is not random as the second breakpoint depends on the information that is encoded in other members of the population.
/// The idea is that the child should not sit right inbetween the two parents, but rather go a little bit from one parent in direction to the other.
///
[Item("CosaCrossover", "An operator which performs the crossover described in the COSA optimization method. It is implemented as described in Wendt, O. 1994. COSA: COoperative Simulated Annealing - Integration von Genetischen Algorithmen und Simulated Annealing am Beispiel der Tourenplanung. Dissertation Thesis. IWI Frankfurt.")]
[StorableClass]
public class CosaCrossover : PermutationCrossover {
[StorableConstructor]
protected CosaCrossover(bool deserializing) : base(deserializing) { }
protected CosaCrossover(CosaCrossover original, Cloner cloner) : base(original, cloner) { }
public CosaCrossover() : base() { }
public override IDeepCloneable Clone(Cloner cloner) {
return new CosaCrossover(this, cloner);
}
///
/// The operator actually performs a 2-opt mutation on the first parent, but it uses the second parent to determine which new edge should be inserted.
/// Thus the mutation is not random as the second breakpoint depends on the information that is encoded in other members of the population.
/// The idea is that the child should not sit right inbetween the two parents, but rather go a little bit from one parent in direction to the other.
///
/// Thrown when and are not of equal length.
/// The random number generator.
/// The parent scope 1 to cross over.
/// The parent scope 2 to cross over.
/// The created cross over permutation as int array.
public static Permutation Apply(IRandom random, Permutation parent1, Permutation parent2) {
if (parent1.Length != parent2.Length) throw new ArgumentException("CosaCrossover: The parent permutations are of unequal length.");
int length = parent1.Length;
int[] result = new int[length];
int crossPoint, startIndex, endIndex;
crossPoint = random.Next(length);
startIndex = (crossPoint + 1) % length;
int i = 0;
while ((i < parent2.Length) && (parent2[i] != parent1[crossPoint])) { // find index of cross point in second permutation
i++;
}
int newEdge = parent2[(i + 1) % length]; // the number that follows the cross point number in parent2 is the new edge that we want to insert
endIndex = 0;
while ((endIndex < parent1.Length) && (parent1[endIndex] != newEdge)) { // find index of the new edge in the first permutation
endIndex++;
}
if (startIndex <= endIndex) {
// copy parent1 to child and reverse the order in between startIndex and endIndex
for (i = 0; i < parent1.Length; i++) {
if (i >= startIndex && i <= endIndex) {
result[i] = parent1[endIndex - i + startIndex];
} else {
result[i] = parent1[i];
}
}
} else { // startIndex > endIndex
for (i = 0; i < parent1.Length; i++) {
if (i >= startIndex || i <= endIndex) {
result[i] = parent1[(endIndex - i + startIndex + length) % length]; // add length to wrap around when dropping below index 0
} else {
result[i] = parent1[i];
}
}
}
return new Permutation(parent1.PermutationType, result);
}
///
/// Checks number of parents and calls .
///
/// Thrown if there are not exactly two parents.
/// A random number generator.
/// An array containing the two permutations that should be crossed.
/// The newly created permutation, resulting from the crossover operation.
protected override Permutation Cross(IRandom random, ItemArray parents) {
if (parents.Length != 2) throw new InvalidOperationException("CosaCrossover: The number of parents is not equal to 2");
return Apply(random, parents[0], parents[1]);
}
}
}