org.apache.commons.math3.genetics.OrderedCrossover Maven / Gradle / Ivy
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math3.genetics;
import java.util.ArrayList;
import java.util.Collections;
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;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.util.FastMath;
/**
* Order 1 Crossover [OX1] builds offspring from ordered chromosomes by copying a
* consecutive slice from one parent, and filling up the remaining genes from the other
* parent as they appear.
*
* This policy works by applying the following rules:
*
* - select a random slice of consecutive genes from parent 1
* - copy the slice to child 1 and mark out the genes in parent 2
* - starting from the right side of the slice, copy genes from parent 2 as they
* appear to child 1 if they are not yet marked out.
*
*
* Example (random sublist from index 3 to 7, underlined):
*
* p1 = (8 4 7 3 6 2 5 1 9 0) X c1 = (0 4 7 3 6 2 5 1 8 9)
* --------- ---------
* p2 = (0 1 2 3 4 5 6 7 8 9) X c2 = (8 1 2 3 4 5 6 7 9 0)
*
*
* This policy works only on {@link AbstractListChromosome}, and therefore it
* is parameterized by T. Moreover, the chromosomes must have same lengths.
*
* @see
* Order 1 Crossover Operator
*
* @param generic type of the {@link AbstractListChromosome}s for crossover
* @since 3.1
*/
public class OrderedCrossover implements CrossoverPolicy {
/**
* {@inheritDoc}
*
* @throws MathIllegalArgumentException iff one of the chromosomes is
* 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) first, (AbstractListChromosome) second);
}
/**
* 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 first, final AbstractListChromosome 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 parent1Rep = first.getRepresentation();
final List parent2Rep = second.getRepresentation();
// and of the children
final List child1 = new ArrayList(length);
final List child2 = new ArrayList(length);
// sets of already inserted items for quick access
final Set child1Set = new HashSet(length);
final Set child2Set = new HashSet(length);
final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();
// choose random points, making sure that lb < ub.
int a = random.nextInt(length);
int b;
do {
b = random.nextInt(length);
} while (a == b);
// determine the lower and upper bounds
final int lb = FastMath.min(a, b);
final int ub = FastMath.max(a, b);
// add the subLists that are between lb and ub
child1.addAll(parent1Rep.subList(lb, ub + 1));
child1Set.addAll(child1);
child2.addAll(parent2Rep.subList(lb, ub + 1));
child2Set.addAll(child2);
// iterate over every item in the parents
for (int i = 1; i <= length; i++) {
final int idx = (ub + i) % length;
// retrieve the current item in each parent
final T item1 = parent1Rep.get(idx);
final T item2 = parent2Rep.get(idx);
// if the first child already contains the item in the second parent add it
if (!child1Set.contains(item2)) {
child1.add(item2);
child1Set.add(item2);
}
// if the second child already contains the item in the first parent add it
if (!child2Set.contains(item1)) {
child2.add(item1);
child2Set.add(item1);
}
}
// rotate so that the original slice is in the same place as in the parents.
Collections.rotate(child1, lb);
Collections.rotate(child2, lb);
return new ChromosomePair(first.newFixedLengthChromosome(child1),
second.newFixedLengthChromosome(child2));
}
}