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Java example source code file (UniformCrossover.java)

This example Java source code file (UniformCrossover.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

abstractlistchromosome, arraylist, chromosome, chromosomepair, crossoverpolicy, dimensionmismatchexception, list, mathillegalargumentexception, outofrangeexception, randomgenerator, suppresswarnings, uniformcrossover, util

The UniformCrossover.java Java example source code

 * 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,
 * 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.
 * <p>
 * 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).
 * <p>
 * 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://en.wikipedia.org/wiki/Crossover_%28genetic_algorithm%29">Crossover techniques (Wikipedia)
 * @see <a href="http://www.obitko.com/tutorials/genetic-algorithms/crossover-mutation.php">Crossover (Obitko.com)
 * @see <a href="http://www.tomaszgwiazda.com/uniformX.htm">Uniform crossover
 * @param <T> generic type of the {@link AbstractListChromosome}s for crossover
 * @since 3.1
public class UniformCrossover<T> 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
    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) 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<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
        final List<T> child1Rep = new ArrayList(length);
        final List<T> 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
            } else {

        return new ChromosomePair(first.newFixedLengthChromosome(child1Rep),

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