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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,
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* 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.List;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomGenerator;
/**
* Perform Uniform Crossover [UX] on the specified chromosomes. A fixed mixing
* ratio is used to combine genes from the first and second parents, e.g. using a
* ratio of 0.5 would result in approximately 50% of genes coming from each
* parent. This is typically a poor method of crossover, but empirical evidence
* suggests that it is more exploratory and results in a larger part of the
* problem space being searched.
*
* This crossover policy evaluates each gene of the parent chromosomes by chosing a
* uniform random number {@code p} in the range [0, 1]. If {@code p} < {@code ratio},
* the parent genes are swapped. This means with a ratio of 0.7, 30% of the genes from the
* first parent and 70% from the second parent will be selected for the first offspring (and
* vice versa for the second offspring).
*
* This policy works only on {@link AbstractListChromosome}, and therefore it
* is parameterized by T. Moreover, the chromosomes must have same lengths.
*
* @see Crossover techniques (Wikipedia)
* @see Crossover (Obitko.com)
* @see Uniform crossover
* @param generic type of the {@link AbstractListChromosome}s for crossover
* @since 3.1
*/
public class UniformCrossover implements CrossoverPolicy {
/** The mixing ratio. */
private final double ratio;
/**
* Creates a new {@link UniformCrossover} policy using the given mixing ratio.
*
* @param ratio the mixing ratio
* @throws OutOfRangeException if the mixing ratio is outside the [0, 1] range
*/
public UniformCrossover(final double ratio) throws OutOfRangeException {
if (ratio < 0.0d || ratio > 1.0d) {
throw new OutOfRangeException(LocalizedFormats.CROSSOVER_RATE, ratio, 0.0d, 1.0d);
}
this.ratio = ratio;
}
/**
* Returns the mixing ratio used by this {@link CrossoverPolicy}.
*
* @return the mixing ratio
*/
public double getRatio() {
return ratio;
}
/**
* {@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
*/
private 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 child1Rep = new ArrayList(length);
final List child2Rep = new ArrayList(length);
final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();
for (int index = 0; index < length; index++) {
if (random.nextDouble() < ratio) {
// swap the bits -> take other parent
child1Rep.add(parent2Rep.get(index));
child2Rep.add(parent1Rep.get(index));
} else {
child1Rep.add(parent1Rep.get(index));
child2Rep.add(parent2Rep.get(index));
}
}
return new ChromosomePair(first.newFixedLengthChromosome(child1Rep),
second.newFixedLengthChromosome(child2Rep));
}
}