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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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.
*/
package org.apache.commons.math3.genetics;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.random.RandomGenerator;
import org.apache.commons.math3.random.JDKRandomGenerator;
/**
* Implementation of a genetic algorithm. All factors that govern the operation
* of the algorithm can be configured for a specific problem.
*
* @since 2.0
*/
public class GeneticAlgorithm {
/**
* Static random number generator shared by GA implementation classes. Set the randomGenerator seed to get
* reproducible results. Use {@link #setRandomGenerator(RandomGenerator)} to supply an alternative to the default
* JDK-provided PRNG.
*/
//@GuardedBy("this")
private static RandomGenerator randomGenerator = new JDKRandomGenerator();
/** the crossover policy used by the algorithm. */
private final CrossoverPolicy crossoverPolicy;
/** the rate of crossover for the algorithm. */
private final double crossoverRate;
/** the mutation policy used by the algorithm. */
private final MutationPolicy mutationPolicy;
/** the rate of mutation for the algorithm. */
private final double mutationRate;
/** the selection policy used by the algorithm. */
private final SelectionPolicy selectionPolicy;
/** the number of generations evolved to reach {@link StoppingCondition} in the last run. */
private int generationsEvolved = 0;
/**
* Create a new genetic algorithm.
* @param crossoverPolicy The {@link CrossoverPolicy}
* @param crossoverRate The crossover rate as a percentage (0-1 inclusive)
* @param mutationPolicy The {@link MutationPolicy}
* @param mutationRate The mutation rate as a percentage (0-1 inclusive)
* @param selectionPolicy The {@link SelectionPolicy}
* @throws OutOfRangeException if the crossover or mutation rate is outside the [0, 1] range
*/
public GeneticAlgorithm(final CrossoverPolicy crossoverPolicy,
final double crossoverRate,
final MutationPolicy mutationPolicy,
final double mutationRate,
final SelectionPolicy selectionPolicy) throws OutOfRangeException {
if (crossoverRate < 0 || crossoverRate > 1) {
throw new OutOfRangeException(LocalizedFormats.CROSSOVER_RATE,
crossoverRate, 0, 1);
}
if (mutationRate < 0 || mutationRate > 1) {
throw new OutOfRangeException(LocalizedFormats.MUTATION_RATE,
mutationRate, 0, 1);
}
this.crossoverPolicy = crossoverPolicy;
this.crossoverRate = crossoverRate;
this.mutationPolicy = mutationPolicy;
this.mutationRate = mutationRate;
this.selectionPolicy = selectionPolicy;
}
/**
* Set the (static) random generator.
*
* @param random random generator
*/
public static synchronized void setRandomGenerator(final RandomGenerator random) {
randomGenerator = random;
}
/**
* Returns the (static) random generator.
*
* @return the static random generator shared by GA implementation classes
*/
public static synchronized RandomGenerator getRandomGenerator() {
return randomGenerator;
}
/**
* Evolve the given population. Evolution stops when the stopping condition
* is satisfied. Updates the {@link #getGenerationsEvolved() generationsEvolved}
* property with the number of generations evolved before the StoppingCondition
* is satisfied.
*
* @param initial the initial, seed population.
* @param condition the stopping condition used to stop evolution.
* @return the population that satisfies the stopping condition.
*/
public Population evolve(final Population initial, final StoppingCondition condition) {
Population current = initial;
generationsEvolved = 0;
while (!condition.isSatisfied(current)) {
current = nextGeneration(current);
generationsEvolved++;
}
return current;
}
/**
* Evolve the given population into the next generation.
*
*
* - Get nextGeneration population to fill from
current
* generation, using its nextGeneration method
* - Loop until new generation is filled:
* - Apply configured SelectionPolicy to select a pair of parents
* from
current
* - With probability = {@link #getCrossoverRate()}, apply
* configured {@link CrossoverPolicy} to parents
* - With probability = {@link #getMutationRate()}, apply
* configured {@link MutationPolicy} to each of the offspring
* - Add offspring individually to nextGeneration,
* space permitting
*
* - Return nextGeneration
*
*
* @param current the current population.
* @return the population for the next generation.
*/
public Population nextGeneration(final Population current) {
Population nextGeneration = current.nextGeneration();
RandomGenerator randGen = getRandomGenerator();
while (nextGeneration.getPopulationSize() < nextGeneration.getPopulationLimit()) {
// select parent chromosomes
ChromosomePair pair = getSelectionPolicy().select(current);
// crossover?
if (randGen.nextDouble() < getCrossoverRate()) {
// apply crossover policy to create two offspring
pair = getCrossoverPolicy().crossover(pair.getFirst(), pair.getSecond());
}
// mutation?
if (randGen.nextDouble() < getMutationRate()) {
// apply mutation policy to the chromosomes
pair = new ChromosomePair(
getMutationPolicy().mutate(pair.getFirst()),
getMutationPolicy().mutate(pair.getSecond()));
}
// add the first chromosome to the population
nextGeneration.addChromosome(pair.getFirst());
// is there still a place for the second chromosome?
if (nextGeneration.getPopulationSize() < nextGeneration.getPopulationLimit()) {
// add the second chromosome to the population
nextGeneration.addChromosome(pair.getSecond());
}
}
return nextGeneration;
}
/**
* Returns the crossover policy.
* @return crossover policy
*/
public CrossoverPolicy getCrossoverPolicy() {
return crossoverPolicy;
}
/**
* Returns the crossover rate.
* @return crossover rate
*/
public double getCrossoverRate() {
return crossoverRate;
}
/**
* Returns the mutation policy.
* @return mutation policy
*/
public MutationPolicy getMutationPolicy() {
return mutationPolicy;
}
/**
* Returns the mutation rate.
* @return mutation rate
*/
public double getMutationRate() {
return mutationRate;
}
/**
* Returns the selection policy.
* @return selection policy
*/
public SelectionPolicy getSelectionPolicy() {
return selectionPolicy;
}
/**
* Returns the number of generations evolved to reach {@link StoppingCondition} in the last run.
*
* @return number of generations evolved
* @since 2.1
*/
public int getGenerationsEvolved() {
return generationsEvolved;
}
}