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Jenetics - Java Genetic Algorithm Library
/*
* Java Genetic Algorithm Library (jenetics-7.1.2).
* Copyright (c) 2007-2023 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 ([email protected])
*/
package io.jenetics;
import static java.util.Objects.requireNonNull;
import io.jenetics.util.ISeq;
import io.jenetics.util.MSeq;
import io.jenetics.util.RandomRegistry;
import io.jenetics.util.Seq;
/**
* {@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.
*
*
*
*
*
* The figure above shows how the stochastic-universal selection works; n
* is the number of individuals to select.
*
* @see
* Wikipedia: Stochastic universal sampling
*
*
* @author Franz Wilhelmstötter
* @since 1.0
* @version 5.0
*/
public class StochasticUniversalSelector<
G extends Gene, G>,
N extends Number & Comparable super N>
>
extends RouletteWheelSelector
{
public StochasticUniversalSelector() {
super(true);
}
/**
* This method sorts the population in descending order while calculating the
* selection probabilities.
*/
@Override
public ISeq> select(
final Seq> 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
);
}
if (count == 0 || population.isEmpty()) {
return ISeq.empty();
}
final MSeq> selection = MSeq.ofLength(count);
final Seq> pop = _sorted
? population.asISeq().copy().sort(POPULATION_COMPARATOR)
: population;
final double[] probabilities = probabilities(pop, count, opt);
assert pop.size() == probabilities.length;
//Calculating the equally spaces random points.
final double delta = 1.0/count;
final double[] points = new double[count];
points[0] = RandomRegistry.random().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.set(i, pop.get(j%pop.size()));
}
return selection.toISeq();
}
@Override
public String toString() {
return getClass().getSimpleName();
}
}