/*
* 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 static org.jenetics.internal.math.random.indexes;
import org.jenetics.internal.util.Equality;
import org.jenetics.internal.util.Hash;
import org.jenetics.internal.util.IntRef;
import org.jenetics.util.MSeq;
import org.jenetics.util.RandomRegistry;
/**
* 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 3.0
*/
public class Mutator<
G extends Gene<?, G>,
C extends Comparable<? super C>
>
extends AbstractAlterer<G, C>
{
/**
* 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 int alter(
final Population<G, C> population,
final long generation
) {
assert population != null : "Not null is guaranteed from base class.";
final double p = pow(_probability, 1.0/3.0);
final IntRef alterations = new IntRef(0);
indexes(RandomRegistry.getRandom(), population.size(), p).forEach(i -> {
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.value;
}
private Genotype<G> mutate(
final Genotype<G> genotype,
final double p,
final IntRef alterations
) {
final MSeq<Chromosome<G>> chromosomes = genotype.toSeq().copy();
alterations.value +=
indexes(RandomRegistry.getRandom(), genotype.length(), p)
.map(i -> mutate(chromosomes, i, p))
.sum();
return genotype.newInstance(chromosomes.toISeq());
}
private int mutate(final MSeq<Chromosome<G>> c, final int i, final double p) {
final Chromosome<G> chromosome = c.get(i);
final MSeq<G> genes = chromosome.toSeq().copy();
final int mutations = mutate(genes, p);
if (mutations > 0) {
c.set(i, chromosome.newInstance(genes.toISeq()));
}
return mutations;
}
/**
* <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>.
*
* @param genes the genes to mutate.
* @param p the gene mutation probability.
* @return the number of performed mutations
*/
protected int mutate(final MSeq<G> genes, final double p) {
return (int)indexes(RandomRegistry.getRandom(), genes.length(), p)
.peek(i -> genes.set(i, genes.get(i).newInstance()))
.count();
}
@Override
public int hashCode() {
return Hash.of(getClass()).and(super.hashCode()).value();
}
@Override
public boolean equals(final Object obj) {
return Equality.of(this, obj).test(super::equals);
}
@Override
public String toString() {
return format("%s[p=%f]", getClass().getSimpleName(), _probability);
}
}