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Java example source code file (HelloWorldExample.java)

This example Java source code file (HelloWorldExample.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

chromosome, crossover_rate, elitism_rate, geneticalgorithm, list, mutation_rate, override, population, population_size, randomcharactermutation, stoppingcondition, stringbuffer, stringchromosome, target_string, util

The HelloWorldExample.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.userguide.genetics;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.LinkedList;
import java.util.List;

import org.apache.commons.lang3.ArrayUtils;
import org.apache.commons.lang3.RandomStringUtils;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.genetics.AbstractListChromosome;
import org.apache.commons.math3.genetics.Chromosome;
import org.apache.commons.math3.genetics.ElitisticListPopulation;
import org.apache.commons.math3.genetics.GeneticAlgorithm;
import org.apache.commons.math3.genetics.InvalidRepresentationException;
import org.apache.commons.math3.genetics.MutationPolicy;
import org.apache.commons.math3.genetics.OnePointCrossover;
import org.apache.commons.math3.genetics.Population;
import org.apache.commons.math3.genetics.StoppingCondition;
import org.apache.commons.math3.genetics.TournamentSelection;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;

public class HelloWorldExample {
    public static final int    POPULATION_SIZE   = 1000;
    public static final double CROSSOVER_RATE    = 0.9;
    public static final double MUTATION_RATE     = 0.03;
    public static final double ELITISM_RATE      = 0.1;
    public static final int    TOURNAMENT_ARITY  = 2;

    public static final String TARGET_STRING = "Hello World!";
    public static final int DIMENSION = TARGET_STRING.length();

    public static void main(String[] args) {
        long startTime = System.currentTimeMillis();

        // initialize a new genetic algorithm
        GeneticAlgorithm ga = new GeneticAlgorithm(new OnePointCrossover<Character>(), CROSSOVER_RATE,
                                                   new RandomCharacterMutation(), MUTATION_RATE,
                                                   new TournamentSelection(TOURNAMENT_ARITY));

        // initial population
        Population initial = getInitialPopulation();

        // stopping condition
        StoppingCondition stoppingCondition = new StoppingCondition() {
            
            int generation = 0;
            
//            @Override
            public boolean isSatisfied(Population population) {
                Chromosome fittestChromosome = population.getFittestChromosome();
                
                if (generation == 1 || generation % 10 == 0) {
                    System.out.println("Generation " + generation + ": " + fittestChromosome.toString());
                }
                generation++;

                double fitness = fittestChromosome.fitness();
                if (Precision.equals(fitness, 0.0, 1e-6)) {
                    return true;
                } else {
                    return false;
                }
            }
        };

        System.out.println("Starting evolution ...");
        
        // run the algorithm
        Population finalPopulation = ga.evolve(initial, stoppingCondition);

        // Get the end time for the simulation.
        long endTime = System.currentTimeMillis();

        // best chromosome from the final population
        Chromosome best = finalPopulation.getFittestChromosome();
        System.out.println("Generation " + ga.getGenerationsEvolved() + ": " + best.toString());
        System.out.println("Total execution time: " + (endTime - startTime) + "ms");
    }
    
    private static List<Character> randomRepresentation(int length) {
        return asList(RandomStringUtils.randomAscii(length));
    }

    private static List<Character> asList(String str) {
        return Arrays.asList(ArrayUtils.toObject(str.toCharArray()));    
    }
    
    private static Population getInitialPopulation() {
        List<Chromosome> popList = new LinkedList();

        for (int i = 0; i < POPULATION_SIZE; i++) {
            popList.add(new StringChromosome(randomRepresentation(DIMENSION)));
        }
        return new ElitisticListPopulation(popList, 2 * popList.size(), ELITISM_RATE);
    }

    /**
     * String Chromosome represented by a list of characters.
     */
    public static class StringChromosome extends AbstractListChromosome<Character> {

        public StringChromosome(List<Character> repr) {
            super(repr);
        }

        public StringChromosome(String str) {
            this(asList(str));
        }

        public double fitness() {
            String target = TARGET_STRING;
            int f = 0; // start at 0; the best fitness
            List<Character> chromosome = getRepresentation();
            for (int i = 0, c = target.length(); i < c; i++) {
                // subtract the ascii difference between the target character and the chromosome character.
                // Thus 'c' is fitter than 'd' when compared to 'a'.
                f -= FastMath.abs(target.charAt(i) - chromosome.get(i).charValue());
            }
            return f;
        }

        @Override
        protected void checkValidity(List<Character> repr) throws InvalidRepresentationException {
            for (char c : repr) {
                if (c < 32 || c > 126) {
                    throw new InvalidRepresentationException(LocalizedFormats.INVALID_FIXED_LENGTH_CHROMOSOME);
                }
            }
        }

        public List<Character> getStringRepresentation() {
            return getRepresentation();
        }

        @Override
        public StringChromosome newFixedLengthChromosome(List<Character> repr) {
            return new StringChromosome(repr);
        }

        @Override
        public String toString() {
            StringBuffer sb = new StringBuffer();
            for (Character i : getRepresentation()) {
                sb.append(i.charValue());
            }
            return String.format("(f=%s '%s')", getFitness(), sb.toString());
        }

    }

    private static class RandomCharacterMutation implements MutationPolicy {
        public Chromosome mutate(Chromosome original) {
            if (!(original instanceof StringChromosome)) {
                throw new IllegalArgumentException();
            }

            StringChromosome strChromosome = (StringChromosome) original;
            List<Character> characters = strChromosome.getStringRepresentation();
            
            int mutationIndex = GeneticAlgorithm.getRandomGenerator().nextInt(characters.size());

            List<Character> mutatedChromosome = new ArrayList(characters);
            char newValue = (char) (32 + GeneticAlgorithm.getRandomGenerator().nextInt(127 - 32));
            mutatedChromosome.set(mutationIndex, newValue);

            return strChromosome.newFixedLengthChromosome(mutatedChromosome);
        }
    }
}

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