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Java example source code file (FitnessCachingTest.java)

This example Java source code file (FitnessCachingTest.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.

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Java - Java tags/keywords

binarymutation, crossover_rate, dummybinarychromosome, dummycountingbinarychromosome, elitism_rate, elitisticlistpopulation, geneticalgorithm, mutation_rate, num_generations, onepointcrossover, override, population_size, stoppingcondition, tournamentselection, util

The FitnessCachingTest.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,
 * See the License for the specific language governing permissions and
 * limitations under the License.
package org.apache.commons.math3.genetics;

import java.util.LinkedList;
import java.util.List;

import org.junit.Assert;
import org.junit.Test;

public class FitnessCachingTest {

    // parameters for the GA
    private static final int DIMENSION = 50;
    private static final double CROSSOVER_RATE = 1;
    private static final double MUTATION_RATE = 0.1;
    private static final int TOURNAMENT_ARITY = 5;

    private static final int POPULATION_SIZE = 10;
    private static final int NUM_GENERATIONS = 50;
    private static final double ELITISM_RATE = 0.2;

    // how many times was the fitness computed
    private static int fitnessCalls = 0;

    public void testFitnessCaching() {
        // initialize a new genetic algorithm
        GeneticAlgorithm ga = new GeneticAlgorithm(
                new OnePointCrossover<Integer>(),
                CROSSOVER_RATE, // all selected chromosomes will be recombined (=crosssover)
                new BinaryMutation(),
                MUTATION_RATE, // no mutation
                new TournamentSelection(TOURNAMENT_ARITY)

        // initial population
        Population initial = randomPopulation();
        // stopping conditions
        StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);

        // run the algorithm
        ga.evolve(initial, stopCond);

        int neededCalls =
            POPULATION_SIZE /*initial population*/ +
            (NUM_GENERATIONS - 1) /*for each population*/ * (int)(POPULATION_SIZE * (1.0 - ELITISM_RATE)) /*some chromosomes are copied*/
        Assert.assertTrue(fitnessCalls <= neededCalls); // some chromosomes after crossover may be the same os old ones

     * Initializes a random population.
    private static ElitisticListPopulation randomPopulation() {
        List<Chromosome> popList = new LinkedList();

        for (int i=0; i<POPULATION_SIZE; i++) {
            BinaryChromosome randChrom = new DummyCountingBinaryChromosome(BinaryChromosome.randomBinaryRepresentation(DIMENSION));
        return new ElitisticListPopulation(popList, popList.size(), ELITISM_RATE);

    private static class DummyCountingBinaryChromosome extends DummyBinaryChromosome {

        public DummyCountingBinaryChromosome(List<Integer> representation) {

        public double fitness() {
            return 0;

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