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Java example source code file (GeneticAlgorithm.java)

This example Java source code file (GeneticAlgorithm.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

chromosomepair, crossoverpolicy, geneticalgorithm, jdkrandomgenerator, mutationpolicy, outofrangeexception, population, randomgenerator, selectionpolicy, stoppingcondition

The GeneticAlgorithm.java Java example source code

/*
 * 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.
     * <p>
     * <ol>
     *  <li>Get nextGeneration population to fill from current
     *      generation, using its nextGeneration method</li>
     *  <li>Loop until new generation is filled:
     *  <ul>
  • Apply configured SelectionPolicy to select a pair of parents * from <code>current
  • * <li>With probability = {@link #getCrossoverRate()}, apply * configured {@link CrossoverPolicy} to parents</li> * <li>With probability = {@link #getMutationRate()}, apply * configured {@link MutationPolicy} to each of the offspring</li> * <li>Add offspring individually to nextGeneration, * space permitting</li> * </ul> * <li>Return nextGeneration * </ol> * * @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; } }

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