StochasticUniversalSelector.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.util.Objects.requireNonNull;
import org.jenetics.internal.util.HashBuilder;
import org.jenetics.util.RandomRegistry;
/**
* {@code StochasticUniversalSelector} is a method for selecting a
* population according to some given probability in a way that minimize chance
* fluctuations. It can be viewed as a type of roulette game where now we have
* P equally spaced points which we spin.
*
* <p><div align="center">
* <img src="doc-files/StochasticUniversalSelection.svg" width="400" />
* </p></div>
*
* The figure above shows how the stochastic-universal selection works; <i>n</i>
* is the number of individuals to select.
*
* @see <a href="https://secure.wikimedia.org/wikipedia/en/wiki/Stochastic_universal_sampling">
* Wikipedia: Stochastic universal sampling
* </a>
*
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @since 1.0
* @version 1.0 — <em>$Date: 2014-03-06 $</em>
*/
public class StochasticUniversalSelector<
G extends Gene<?, G>,
N extends Number & Comparable<? super N>
>
extends RouletteWheelSelector<G, N>
{
public StochasticUniversalSelector() {
}
/**
* This method sorts the population in descending order while calculating the
* selection probabilities. (The method {@link Population#sort()} is called
* by this method.)
*/
@Override
public Population<G, N> select(
final Population<G, N> population,
final int count,
final Optimize opt
) {
requireNonNull(population, "Population");
if (count < 0) {
throw new IllegalArgumentException(
"Selection count must be greater or equal then zero, but was " +
count
);
}
final Population<G, N> selection = new Population<>(count);
if (count == 0) {
return selection;
}
final double[] probabilities = probabilities(population, count, opt);
assert (population.size() == probabilities.length);
//Calculating the equally spaces random points.
final double delta = 1.0/count;
final double[] points = new double[count];
points[0] = RandomRegistry.getRandom().nextDouble()*delta;
for (int i = 1; i < count; ++i) {
points[i] = delta*i;
}
int j = 0;
double prop = 0;
for (int i = 0; i < count; ++i) {
while (points[i] > prop) {
prop += probabilities[j];
++j;
}
selection.add(population.get(j));
}
return selection;
}
@Override
protected double[] probabilities(
final Population<G, N> population,
final int count
) {
population.sort();
return super.probabilities(population, count);
}
@Override
public int hashCode() {
return HashBuilder.of(getClass()).and(super.hashCode()).value();
}
@Override
public boolean equals(final Object obj) {
return obj == this ||
obj != null &&
obj.getClass() == getClass() &&
super.equals(obj);
}
@Override
public String toString() {
return getClass().getSimpleName();
}
}