Mutator.java
/*
* Java Genetic Algorithm Library (@__identifier__@).
* Copyright (c) @__year__@ Franz Wilhelmstötter
*
* Licensed 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.
*
* Author:
* Franz Wilhelmstötter (franz.wilhelmstoetter@gmx.at)
*/
package org.jenetics;
import static java.lang.Math.pow;
import static java.lang.String.format;
import java.util.concurrent.atomic.AtomicInteger;
import org.jenetics.internal.util.HashBuilder;
import org.jenetics.util.IndexStream;
import org.jenetics.util.MSeq;
/**
* This class is for mutating a chromosomes of an given population. There are
* two distinct roles mutation plays
* <ul>
* <li>Exploring the search space. By making small moves mutation allows a
* population to explore the search space. This exploration is often slow
* compared to crossover, but in problems where crossover is disruptive this
* can be an important way to explore the landscape.
* </li>
* <li>Maintaining diversity. Mutation prevents a population from
* correlating. Even if most of the search is being performed by crossover,
* mutation can be vital to provide the diversity which crossover needs.
* </li>
* </ul>
*
* <p>
* The mutation probability is the parameter that must be optimized. The optimal
* value of the mutation rate depends on the role mutation plays. If mutation is
* the only source of exploration (if there is no crossover) then the mutation
* rate should be set so that a reasonable neighborhood of solutions is explored.
* </p>
* The mutation probability <i>P(m)</i> is the probability that a specific gene
* over the whole population is mutated. The number of available genes of an
* population is
* <p>
* <img src="doc-files/mutator-N_G.gif" alt="N_P N_{g}=N_P \sum_{i=0}^{N_{G}-1}N_{C[i]}" />
* </p>
* where <i>N<sub>P</sub></i> is the population size, <i>N<sub>g</sub></i> the
* number of genes of a genotype. So the (average) number of genes
* mutated by the mutation is
* <p>
* <img src="doc-files/mutator-mean_m.gif" alt="\hat{\mu}=N_{P}N_{g}\cdot P(m)" />
* </p>
*
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @since 1.0
* @version 1.0 — <em>$Date: 2014-02-27 $</em>
*/
public class Mutator<G extends Gene<?, G>> extends AbstractAlterer<G> {
/**
* Construct a Mutation object which a given mutation probability.
*
* @param probability Mutation probability. The given probability is
* divided by the number of chromosomes of the genotype to form
* the concrete mutation probability.
* @throws IllegalArgumentException if the {@code probability} is not in the
* valid range of {@code [0, 1]}..
*/
public Mutator(final double probability) {
super(probability);
}
/**
* Default constructor, with probability = 0.01.
*/
public Mutator() {
this(0.01);
}
/**
* Concrete implementation of the alter method.
*/
@Override
public <C extends Comparable<? super C>> int alter(
final Population<G, C> population,
final int generation
) {
assert(population != null) : "Not null is guaranteed from base class.";
final double p = pow(_probability, 1.0/3.0);
final AtomicInteger alterations = new AtomicInteger(0);
final IndexStream stream = IndexStream.Random(population.size(), p);
for (int i = stream.next(); i != -1; i = stream.next()) {
final Phenotype<G, C> pt = population.get(i);
final Genotype<G> gt = pt.getGenotype();
final Genotype<G> mgt = mutate(gt, p, alterations);
final Phenotype<G, C> mpt = pt.newInstance(mgt, generation);
population.set(i, mpt);
}
return alterations.get();
}
private Genotype<G> mutate(
final Genotype<G> genotype,
final double p,
final AtomicInteger alterations
) {
Genotype<G> gt = genotype;
final IndexStream stream = IndexStream.Random(genotype.length(), p);
final int start = stream.next();
if (start != -1) {
final MSeq<Chromosome<G>> chromosomes = genotype.toSeq().copy();
for (int i = start; i != -1; i = stream.next()) {
final Chromosome<G> chromosome = chromosomes.get(i);
final MSeq<G> genes = chromosome.toSeq().copy();
final int mutations = mutate(genes, p);
if (mutations > 0) {
alterations.addAndGet(mutations);
chromosomes.set(i, chromosome.newInstance(genes.toISeq()));
}
}
gt = genotype.newInstance(chromosomes.toISeq());
}
return gt;
}
/**
* <p>
* Template method which gives an (re)implementation of the mutation class
* the possibility to perform its own mutation operation, based on a
* writable gene array and the gene mutation probability <i>p</i>.
* </p>
* This implementation, for example, does it in this way:
* [code]
* protected int mutate(final MSeq〈G〉 genes, final double p) {
* final IndexStream stream = IndexStream.Random(genes.length(), p);
*
* int alterations = 0;
* for (int i = stream.next(); i != -1; i = stream.next()) {
* genes.set(i, genes.get(i).newInstance());
* ++alterations;
* }
* return alterations;
* }
* [/code]
*
* @param genes the genes to mutate.
* @param p the gene mutation probability.
*/
protected int mutate(final MSeq<G> genes, final double p) {
final IndexStream stream = IndexStream.Random(genes.length(), p);
int alterations = 0;
for (int i = stream.next(); i != -1; i = stream.next()) {
genes.set(i, genes.get(i).newInstance());
++alterations;
}
return alterations;
}
@Override
public int hashCode() {
return HashBuilder.of(getClass()).and(super.hashCode()).value();
}
@Override
public boolean equals(final Object obj) {
return obj == this || obj instanceof Mutator<?>;
}
@Override
public String toString() {
return format("%s[p=%f]", getClass().getSimpleName(), _probability);
}
}