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[MATH-777] Added UniformCrossover policy. Thanks for Reid Hochstedler.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1350391 13f79535-47bb-0310-9956-ffa450edef68
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Thomas Neidhart
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math3.genetics;
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import java.util.ArrayList;
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import java.util.List;
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import org.apache.commons.math3.exception.DimensionMismatchException;
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import org.apache.commons.math3.exception.MathIllegalArgumentException;
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import org.apache.commons.math3.exception.OutOfRangeException;
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import org.apache.commons.math3.exception.util.LocalizedFormats;
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import org.apache.commons.math3.random.RandomGenerator;
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/**
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* Perform Uniform Crossover [UX] on the specified chromosomes. A fixed mixing
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* ratio is used to combine genes from the first and second parents, e.g. using a
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* ratio of 0.5 would result in approximately 50% of genes coming from each
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* parent. This is typically a poor method of crossover, but empirical evidence
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* suggests that it is more exploratory and results in a larger part of the
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* problem space being searched.
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*
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* <p>This crossover policy evaluates each gene of the parent chromosomes by chosing a
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* uniform random number {@code p} in the range [0, 1]. If {@code p} < {@code ratio},
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* the parent genes are swapped. This means with a ratio of 0.7, 30% of the genes from the
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* first parent and 70% from the second parent will be selected for the first offspring (and
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* vice versa for the second offspring).</p>
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*
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* <p>This policy works only on {@link AbstractListChromosome}, and therefore it
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* is parameterized by T. Moreover, the chromosomes must have same lengths.
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* </p>
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*
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* @see <a href="http://en.wikipedia.org/wiki/Crossover_%28genetic_algorithm%29">Crossover techniques (Wikipedia)</a>
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* @see <a href="http://www.obitko.com/tutorials/genetic-algorithms/crossover-mutation.php">Crossover (Obitko.com)</a>
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* @see <a href="http://www.tomaszgwiazda.com/uniformX.htm">Uniform crossover</a>
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* @param <T> generic type of the {@link AbstractListChromosome}s for crossover
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* @since 3.1
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* @version $Id$
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*/
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public class UniformCrossover<T> implements CrossoverPolicy {
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/** The mixing ratio. */
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private final double ratio;
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/**
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* Creates a new {@link UniformCrossover} policy using the given mixing ratio.
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*
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* @param ratio the mixing ratio
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* @throws OutOfRangeException if the mixing ratio is outside the [0, 1] range
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*/
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public UniformCrossover(final double ratio) {
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if (ratio < 0.0d || ratio > 1.0d) {
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throw new OutOfRangeException(LocalizedFormats.CROSSOVER_RATE, ratio, 0.0d, 1.0d);
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}
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this.ratio = ratio;
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}
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/**
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* Returns the mixing ratio used by this {@link CrossoverPolicy}.
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*
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* @return the mixing ratio
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*/
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public double getRatio() {
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return ratio;
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}
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/**
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* {@inheritDoc}
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*/
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@SuppressWarnings("unchecked")
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public ChromosomePair crossover(final Chromosome first, final Chromosome second) {
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if (!(first instanceof AbstractListChromosome<?> && second instanceof AbstractListChromosome<?>)) {
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throw new MathIllegalArgumentException(LocalizedFormats.INVALID_FIXED_LENGTH_CHROMOSOME);
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}
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return mate((AbstractListChromosome<T>) first, (AbstractListChromosome<T>) second);
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}
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/**
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* Helper for {@link #crossover(Chromosome, Chromosome)}. Performs the actual crossover.
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*
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* @param first the first chromosome
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* @param second the second chromosome
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* @return the pair of new chromosomes that resulted from the crossover
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* @throws DimensionMismatchException if the length of the two chromosomes is different
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*/
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private ChromosomePair mate(final AbstractListChromosome<T> first,
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final AbstractListChromosome<T> second) {
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final int length = first.getLength();
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if (length != second.getLength()) {
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throw new DimensionMismatchException(second.getLength(), length);
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}
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// array representations of the parents
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final List<T> parent1Rep = first.getRepresentation();
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final List<T> parent2Rep = second.getRepresentation();
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// and of the children
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final List<T> child1Rep = new ArrayList<T>(first.getLength());
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final List<T> child2Rep = new ArrayList<T>(second.getLength());
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final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();
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for (int index = 0; index < length; index++) {
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if (random.nextDouble() < ratio) {
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// swap the bits -> take other parent
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child1Rep.add(parent2Rep.get(index));
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child2Rep.add(parent1Rep.get(index));
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} else {
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child1Rep.add(parent1Rep.get(index));
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child2Rep.add(parent2Rep.get(index));
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}
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}
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return new ChromosomePair(first.newFixedLengthChromosome(child1Rep),
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second.newFixedLengthChromosome(child2Rep));
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}
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}
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@ -0,0 +1,131 @@
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package org.apache.commons.math3.genetics;
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import java.util.ArrayList;
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import java.util.List;
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import junit.framework.Assert;
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import org.apache.commons.math3.exception.DimensionMismatchException;
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import org.apache.commons.math3.exception.MathIllegalArgumentException;
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import org.apache.commons.math3.exception.OutOfRangeException;
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import org.junit.BeforeClass;
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import org.junit.Test;
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public class UniformCrossoverTest {
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private static final int LEN = 10000;
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private static final List<Integer> p1 = new ArrayList<Integer>(LEN);
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private static final List<Integer> p2 = new ArrayList<Integer>(LEN);
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@BeforeClass
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public static void setUpBeforeClass() {
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for (int i = 0; i < LEN; i++) {
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p1.add(0);
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p2.add(1);
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}
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}
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@Test(expected = OutOfRangeException.class)
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public void testRatioTooLow() {
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new UniformCrossover<Integer>(-0.5d);
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}
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@Test(expected = OutOfRangeException.class)
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public void testRatioTooHigh() {
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new UniformCrossover<Integer>(1.5d);
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}
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@Test
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public void testCrossover() {
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// test crossover with different ratios
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performCrossover(0.5);
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performCrossover(0.7);
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performCrossover(0.2);
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}
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private void performCrossover(double ratio) {
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final DummyBinaryChromosome p1c = new DummyBinaryChromosome(p1);
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final DummyBinaryChromosome p2c = new DummyBinaryChromosome(p2);
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final CrossoverPolicy cp = new UniformCrossover<Integer>(ratio);
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// make a number of rounds
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for (int i = 0; i < 20; i++) {
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final ChromosomePair pair = cp.crossover(p1c, p2c);
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final List<Integer> c1 = ((DummyBinaryChromosome) pair.getFirst()).getRepresentation();
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final List<Integer> c2 = ((DummyBinaryChromosome) pair.getSecond()).getRepresentation();
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int from1 = 0;
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int from2 = 0;
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// check first child
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for (int val : c1) {
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if (val == 0) {
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from1++;
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} else {
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from2++;
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}
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}
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Assert.assertEquals(1.0 - ratio, Double.valueOf((double) from1 / LEN), 0.1);
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Assert.assertEquals(ratio, Double.valueOf((double) from2 / LEN), 0.1);
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from1 = 0;
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from2 = 0;
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// check second child
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for (int val : c2) {
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if (val == 0) {
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from1++;
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} else {
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from2++;
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}
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}
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Assert.assertEquals(ratio, Double.valueOf((double) from1 / LEN), 0.1);
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Assert.assertEquals(1.0 - ratio, Double.valueOf((double) from2 / LEN), 0.1);
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}
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}
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@Test(expected = DimensionMismatchException.class)
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public void testCrossoverDimensionMismatchException(){
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final Integer[] p1 = new Integer[] {1,0,1,0,0,1,0,1,1};
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final Integer[] p2 = new Integer[] {0,1,1,0,1};
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final BinaryChromosome p1c = new DummyBinaryChromosome(p1);
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final BinaryChromosome p2c = new DummyBinaryChromosome(p2);
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final CrossoverPolicy cp = new UniformCrossover<Integer>(0.5d);
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cp.crossover(p1c, p2c);
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}
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@Test(expected = MathIllegalArgumentException.class)
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public void testCrossoverInvalidFixedLengthChromosomeFirst() {
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final Integer[] p1 = new Integer[] {1,0,1,0,0,1,0,1,1};
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final BinaryChromosome p1c = new DummyBinaryChromosome(p1);
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final Chromosome p2c = new Chromosome() {
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public double fitness() {
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// Not important
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return 0;
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}
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};
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final CrossoverPolicy cp = new UniformCrossover<Integer>(0.5d);
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cp.crossover(p1c, p2c);
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}
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@Test(expected = MathIllegalArgumentException.class)
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public void testCrossoverInvalidFixedLengthChromosomeSecond() {
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final Integer[] p1 = new Integer[] {1,0,1,0,0,1,0,1,1};
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final BinaryChromosome p2c = new DummyBinaryChromosome(p1);
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final Chromosome p1c = new Chromosome() {
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public double fitness() {
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// Not important
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return 0;
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}
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};
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final CrossoverPolicy cp = new UniformCrossover<Integer>(0.5d);
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cp.crossover(p1c, p2c);
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}
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}
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