001 /*
002 * CISELab, Computational and Intelligent Systems Engineering Laboratory
003 * Department of Engineering
004 * University of Sannio, 82100 Benevento (ITALY)
005 * web-site: www.ciselab.org
006 *
007 * JENES, A Java Library for Genetic Algorithms
008 * Copyright (C) 2009, Luigi Troiano
009 *
010 * This library is free software; you can redistribute it and/or
011 * modify it under the terms of the GNU Lesser General Public
012 * License as published by the Free Software Foundation; either
013 * version 2.1 of the License, or (at your option) any later version.
014 *
015 * This library is distributed in the hope that it will be useful,
016 * but WITHOUT ANY WARRANTY; without even the implied warranty of
017 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
018 * Lesser General Public License for more details.
019 *
020 * You should have received a copy of the GNU Lesser General Public
021 * License along with this library; if not, write to the Free Software
022 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
023 02110-1301 USA
024 *
025 * COPYRIGHT DISCLAIMER
026 * CISELab, hereby disclaims all copyright interest in the
027 * library `JENES' (A Java Library for Genetic Algorithms)
028 * written by Luigi Troiano et al.
029 *
030 * Luigi Troiano, 1 January 2009
031 * CISELab Coordinator
032 */
033 package jenes.tutorials.problem2;
034
035 import jenes.GenerationEventListener;
036 import jenes.GeneticAlgorithm;
037 import jenes.Random;
038 import jenes.chromosome.IntegerChromosome;
039 import jenes.population.Individual;
040 import jenes.population.Population;
041 import jenes.population.Population.Statistics;
042 import jenes.stage.AbstractStage;
043 import jenes.stage.Parallel;
044 import jenes.stage.operator.common.OnePointCrossover;
045 import jenes.stage.operator.common.SimpleMutator;
046 import jenes.stage.operator.common.TournamentSelector;
047 import jenes.stage.operator.common.TwoPointsCrossover;
048 import jenes.tutorials.utils.Utils;
049
050
051 public class PatternProblem implements GenerationEventListener {
052
053 private static int POPULATION_SIZE = 100;
054 private static int CHROMOSOME_LENGTH = 10;
055 private static int GENERATION_LIMIT = 1000;
056 private static int MAX_INT = 49;
057
058 private PatternGA algorithm = null;
059
060 public PatternProblem() {
061 IntegerChromosome chrom = new IntegerChromosome(CHROMOSOME_LENGTH,0,MAX_INT);
062 Individual<IntegerChromosome> ind = new Individual<IntegerChromosome>(chrom);
063 Population<IntegerChromosome> pop = new Population<IntegerChromosome>(ind, POPULATION_SIZE);
064
065 algorithm = new PatternGA(pop, GENERATION_LIMIT);
066 algorithm.setElitism(5);
067
068 AbstractStage<IntegerChromosome> selection = new TournamentSelector<IntegerChromosome>(2);
069
070 Parallel<IntegerChromosome> parallel = new Parallel<IntegerChromosome>(new SimpleDispenser<IntegerChromosome>(2));
071
072 AbstractStage<IntegerChromosome> crossover1p = new OnePointCrossover<IntegerChromosome>(0.8);
073 parallel.add(crossover1p);
074
075 AbstractStage<IntegerChromosome> crossover2p = new TwoPointsCrossover<IntegerChromosome>(0.5);
076 parallel.add(crossover2p);
077
078 AbstractStage<IntegerChromosome> mutation = new SimpleMutator<IntegerChromosome>(0.02);
079
080 algorithm.addStage(selection);
081 algorithm.addStage(parallel);
082 algorithm.addStage(mutation);
083 algorithm.setBiggerIsBetter(false);
084 algorithm.addGenerationEventListener(this);
085 }
086
087 public void run(int[] target, int precision) {
088 algorithm.setTarget(target);
089 algorithm.setPrecision(precision);
090 algorithm.evolve();
091
092 Population.Statistics stats = algorithm.getCurrentPopulation().getStatistics();
093 GeneticAlgorithm.Statistics algostats = algorithm.getStatistics();
094
095 System.out.println();
096 System.out.print("Target:[");
097 for( int i = 0; i < target.length; ++i ) {
098 System.out.print(target[i]+ ( i < target.length - 1 ? " " : ""));
099 }
100 System.out.println("]");
101 System.out.println();
102
103 System.out.println("Solution: ");
104 System.out.println(stats.getLegalLowestIndividual().getChromosome() );
105 System.out.println(stats.getLegalLowestIndividual());
106 System.out.format("found in %d ms and %d generations.\n", algostats.getExecutionTime(), algostats.getGenerations() );
107 System.out.println();
108 }
109
110 public void onGeneration(GeneticAlgorithm ga, long time) {
111 Statistics stat = ga.getCurrentPopulation().getStatistics();
112 System.out.println("Current generation: " + ga.getGeneration());
113 System.out.println("\tBest score: " + stat.getLegalLowestScore());
114 System.out.println("\tAvg score: " + stat.getLegalScoreAvg());
115 }
116
117 private static void randomize(int[] sample) {
118 for(int i=0;i<sample.length;i++)
119 sample[i] = Random.getInstance().nextInt(0, MAX_INT+1);
120 }
121
122 public static void main(String[] args) {
123
124 Utils.printHeader();
125 System.out.println();
126
127 System.out.println("TUTORIAL 2:");
128 System.out.println("This algorithm aims to autonomously find a vector of integers that best matches with a target vector.");
129 System.out.println();
130
131 Random.getInstance().setStandardSeed();
132
133 PatternProblem problem = new PatternProblem();
134 int[] target = new int[CHROMOSOME_LENGTH];
135
136 randomize(target);
137 problem.run(target, 2);
138
139 randomize(target);
140 problem.run(target, 0);
141
142 }
143
144 }
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