GaussianMutator.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.String.format;
import java.util.Random;
import javolution.lang.Immutable;
import org.jenetics.internal.util.HashBuilder;
import org.jenetics.util.IndexStream;
import org.jenetics.util.MSeq;
import org.jenetics.util.RandomRegistry;
import org.jenetics.util.math;
/**
* The GaussianMutator class performs the mutation of a {@link NumericGene}.
* This mutator picks a new value based on a Gaussian distribution around the
* current value of the gene. The variance of the new value (before clipping to
* the allowed gene range) will be
* <p>
* <img
* src="doc-files/gaussian-mutator-var.gif"
* alt="\hat{\sigma }^2 = \left ( \frac{ g_{max} - g_{min} }{4}\right )^2"
* >
* </p>
* The new value will be cropped to the gene's boundaries.
*
*
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @since 1.0
* @version 1.6 — <em>$Date: 2014-03-05 $</em>
*/
public final class GaussianMutator<G extends NumericGene<?, G>>
extends Mutator<G>
implements Immutable
{
public GaussianMutator() {
}
public GaussianMutator(final double probability) {
super(probability);
}
@Override
protected int mutate(final MSeq<G> genes, final double p) {
final Random random = RandomRegistry.getRandom();
final IndexStream stream = IndexStream.Random(genes.length(), p);
int alterations = 0;
for (int i = stream.next(); i != -1; i = stream.next()) {
genes.set(i, mutate(genes.get(i), random));
++alterations;
}
return alterations;
}
G mutate(final G gene, final Random random) {
final double std = (
gene.getMax().doubleValue() - gene.getMin().doubleValue()
)*0.25;
return gene.newInstance(math.clamp(
random.nextGaussian()*std + gene.doubleValue(),
gene.getMin().doubleValue(),
gene.getMax().doubleValue()
));
}
@Override
public int hashCode() {
return HashBuilder.of(getClass()).and(super.hashCode()).value();
}
@Override
public boolean equals(final Object obj) {
if (obj == this) {
return true;
}
if (obj == null || obj.getClass() != getClass()) {
return false;
}
return super.equals(obj);
}
@Override
public String toString() {
return format(
"%s[p=%f]",
getClass().getSimpleName(),
_probability
);
}
}