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package org.apache.commons.math3.genetics;

import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.util.LocalizedFormats;

Cycle Crossover [CX] builds offspring from ordered chromosomes by identifying cycles between two parent chromosomes. To form the children, the cycles are copied from the respective parents.

To form a cycle the following procedure is applied:

  1. start with the first gene of parent 1
  2. look at the gene at the same position of parent 2
  3. go to the position with the same gene in parent 1
  4. add this gene index to the cycle
  5. repeat the steps 2-5 until we arrive at the starting gene of this cycle
The indices that form a cycle are then used to form the children in alternating order, i.e. in cycle 1, the genes of parent 1 are copied to child 1, while in cycle 2 the genes of parent 1 are copied to child 2, and so forth ...

Example (zero-start cycle):
p1 = (8 4 7 3 6 2 5 1 9 0)    X   c1 = (8 1 2 3 4 5 6 7 9 0)
p2 = (0 1 2 3 4 5 6 7 8 9)    X   c2 = (0 4 7 3 6 2 5 1 8 9)
cycle 1: 8 0 9
cycle 2: 4 1 7 2 5 6
cycle 3: 3
This policy works only on AbstractListChromosome, and therefore it is parameterized by T. Moreover, the chromosomes must have same lengths.
Type parameters:
See Also:
Since:3.1
/** * Cycle Crossover [CX] builds offspring from <b>ordered</b> chromosomes by identifying cycles * between two parent chromosomes. To form the children, the cycles are copied from the * respective parents. * <p> * To form a cycle the following procedure is applied: * <ol> * <li>start with the first gene of parent 1</li> * <li>look at the gene at the same position of parent 2</li> * <li>go to the position with the same gene in parent 1</li> * <li>add this gene index to the cycle</li> * <li>repeat the steps 2-5 until we arrive at the starting gene of this cycle</li> * </ol> * The indices that form a cycle are then used to form the children in alternating order, i.e. * in cycle 1, the genes of parent 1 are copied to child 1, while in cycle 2 the genes of parent 1 * are copied to child 2, and so forth ... * </p> * * Example (zero-start cycle): * <pre> * p1 = (8 4 7 3 6 2 5 1 9 0) X c1 = (8 1 2 3 4 5 6 7 9 0) * p2 = (0 1 2 3 4 5 6 7 8 9) X c2 = (0 4 7 3 6 2 5 1 8 9) * * cycle 1: 8 0 9 * cycle 2: 4 1 7 2 5 6 * cycle 3: 3 * </pre> * * This policy works only on {@link AbstractListChromosome}, and therefore it * is parameterized by T. Moreover, the chromosomes must have same lengths. * * @see <a href="http://www.rubicite.com/Tutorials/GeneticAlgorithms/CrossoverOperators/CycleCrossoverOperator.aspx"> * Cycle Crossover Operator</a> * * @param <T> generic type of the {@link AbstractListChromosome}s for crossover * @since 3.1 */
public class CycleCrossover<T> implements CrossoverPolicy {
If the start index shall be chosen randomly.
/** If the start index shall be chosen randomly. */
private final boolean randomStart;
Creates a new CycleCrossover policy.
/** * Creates a new {@link CycleCrossover} policy. */
public CycleCrossover() { this(false); }
Creates a new CycleCrossover policy using the given randomStart behavior.
Params:
  • randomStart – whether the start index shall be chosen randomly or be set to 0
/** * Creates a new {@link CycleCrossover} policy using the given {@code randomStart} behavior. * * @param randomStart whether the start index shall be chosen randomly or be set to 0 */
public CycleCrossover(final boolean randomStart) { this.randomStart = randomStart; }
Returns whether the starting index is chosen randomly or set to zero.
Returns:true if the starting index is chosen randomly, false otherwise
/** * Returns whether the starting index is chosen randomly or set to zero. * * @return {@code true} if the starting index is chosen randomly, {@code false} otherwise */
public boolean isRandomStart() { return randomStart; }
{@inheritDoc}
Throws:
/** * {@inheritDoc} * * @throws MathIllegalArgumentException if the chromosomes are not an instance of {@link AbstractListChromosome} * @throws DimensionMismatchException if the length of the two chromosomes is different */
@SuppressWarnings("unchecked") public ChromosomePair crossover(final Chromosome first, final Chromosome second) throws DimensionMismatchException, MathIllegalArgumentException { if (!(first instanceof AbstractListChromosome<?> && second instanceof AbstractListChromosome<?>)) { throw new MathIllegalArgumentException(LocalizedFormats.INVALID_FIXED_LENGTH_CHROMOSOME); } return mate((AbstractListChromosome<T>) first, (AbstractListChromosome<T>) second); }
Helper for crossover(Chromosome, Chromosome). Performs the actual crossover.
Params:
  • first – the first chromosome
  • second – the second chromosome
Throws:
Returns:the pair of new chromosomes that resulted from the crossover
/** * Helper for {@link #crossover(Chromosome, Chromosome)}. Performs the actual crossover. * * @param first the first chromosome * @param second the second chromosome * @return the pair of new chromosomes that resulted from the crossover * @throws DimensionMismatchException if the length of the two chromosomes is different */
protected ChromosomePair mate(final AbstractListChromosome<T> first, final AbstractListChromosome<T> second) throws DimensionMismatchException { final int length = first.getLength(); if (length != second.getLength()) { throw new DimensionMismatchException(second.getLength(), length); } // array representations of the parents final List<T> parent1Rep = first.getRepresentation(); final List<T> parent2Rep = second.getRepresentation(); // and of the children: do a crossover copy to simplify the later processing final List<T> child1Rep = new ArrayList<T>(second.getRepresentation()); final List<T> child2Rep = new ArrayList<T>(first.getRepresentation()); // the set of all visited indices so far final Set<Integer> visitedIndices = new HashSet<Integer>(length); // the indices of the current cycle final List<Integer> indices = new ArrayList<Integer>(length); // determine the starting index int idx = randomStart ? GeneticAlgorithm.getRandomGenerator().nextInt(length) : 0; int cycle = 1; while (visitedIndices.size() < length) { indices.add(idx); T item = parent2Rep.get(idx); idx = parent1Rep.indexOf(item); while (idx != indices.get(0)) { // add that index to the cycle indices indices.add(idx); // get the item in the second parent at that index item = parent2Rep.get(idx); // get the index of that item in the first parent idx = parent1Rep.indexOf(item); } // for even cycles: swap the child elements on the indices found in this cycle if (cycle++ % 2 != 0) { for (int i : indices) { T tmp = child1Rep.get(i); child1Rep.set(i, child2Rep.get(i)); child2Rep.set(i, tmp); } } visitedIndices.addAll(indices); // find next starting index: last one + 1 until we find an unvisited index idx = (indices.get(0) + 1) % length; while (visitedIndices.contains(idx) && visitedIndices.size() < length) { idx++; if (idx >= length) { idx = 0; } } indices.clear(); } return new ChromosomePair(first.newFixedLengthChromosome(child1Rep), second.newFixedLengthChromosome(child2Rep)); } }