001 /*
002 * JENES
003 * A time asnd memory efficient Java library for genetic algorithms and more
004 * Copyright (C) 2011 Intelligentia srl
005 *
006 * This program is free software: you can redistribute it and/or modify
007 * it under the terms of the GNU General Public License as published by
008 * the Free Software Foundation, either version 3 of the License, or
009 * (at your option) any later version.
010 *
011 * This program is distributed in the hope that it will be useful,
012 * but WITHOUT ANY WARRANTY; without even the implied warranty of
013 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
014 * GNU General Public License for more details.
015 *
016 * You should have received a copy of the GNU General Public License
017 * along with this program. If not, see <http://www.gnu.org/licenses/>.
018 */
019 package jenes.tutorials.old.problem2;
020
021 import jenes.GenerationEventListener;
022 import jenes.GeneticAlgorithm;
023 import jenes.utils.Random;
024 import jenes.chromosome.IntegerChromosome;
025 import jenes.population.Individual;
026 import jenes.population.Population;
027 import jenes.population.Population.Statistics;
028 import jenes.stage.AbstractStage;
029 import jenes.stage.Parallel;
030 import jenes.stage.operator.common.OnePointCrossover;
031 import jenes.stage.operator.common.SimpleMutator;
032 import jenes.stage.operator.common.TournamentSelector;
033 import jenes.stage.operator.common.TwoPointsCrossover;
034 import jenes.tutorials.utils.Utils;
035
036
037 /**
038 * Tutorial showing how to extend <code>GeneticAlgorithm</code> and how to use
039 * the flexible and configurable breeding structure in Jenes.
040 * The problem consists in searching a pattern of integers with a given precision.
041 * Solutions flow through two different crossovers in parallel. Some are processed by
042 * a single point crossover, the other by a double point crossover.
043 * After solutions are mutated.
044 *
045 * This is the main class that specifies the problem.
046 *
047 * @author Luigi Troiano
048 * @author Pierpaolo Lombardi
049 *
050 * @version 1.3
051 *
052 * @since 1.0
053 */
054 public class PatternProblem implements GenerationEventListener<IntegerChromosome> {
055
056 private static int POPULATION_SIZE = 100;
057 private static int CHROMOSOME_LENGTH = 10;
058 private static int GENERATION_LIMIT = 1000;
059 private static int MAX_INT = 49;
060
061 private PatternGA algorithm = null;
062
063 public PatternProblem() {
064 IntegerChromosome chrom = new IntegerChromosome(CHROMOSOME_LENGTH,0,MAX_INT);
065 Individual<IntegerChromosome> ind = new Individual<IntegerChromosome>(chrom);
066 Population<IntegerChromosome> pop = new Population<IntegerChromosome>(ind, POPULATION_SIZE);
067
068 algorithm = new PatternGA(pop, GENERATION_LIMIT);
069 algorithm.setElitism(5);
070
071 AbstractStage<IntegerChromosome> selection = new TournamentSelector<IntegerChromosome>(2);
072
073 Parallel<IntegerChromosome> parallel = new Parallel<IntegerChromosome>(new SimpleDispenser<IntegerChromosome>(2));
074
075 AbstractStage<IntegerChromosome> crossover1p = new OnePointCrossover<IntegerChromosome>(0.8);
076 parallel.add(crossover1p);
077
078 AbstractStage<IntegerChromosome> crossover2p = new TwoPointsCrossover<IntegerChromosome>(0.5);
079 parallel.add(crossover2p);
080
081 AbstractStage<IntegerChromosome> mutation = new SimpleMutator<IntegerChromosome>(0.02);
082
083 algorithm.addStage(selection);
084 algorithm.addStage(parallel);
085 algorithm.addStage(mutation);
086 algorithm.setBiggerIsBetter(false);
087 algorithm.addGenerationEventListener(this);
088 }
089
090 public void run(int[] target, int precision) {
091 algorithm.setTarget(target);
092 algorithm.setPrecision(precision);
093 algorithm.evolve();
094
095 Population.Statistics stats = algorithm.getCurrentPopulation().getStatistics();
096 GeneticAlgorithm.Statistics algostats = algorithm.getStatistics();
097
098 System.out.println();
099 System.out.print("Target:[");
100 for( int i = 0; i < target.length; ++i ) {
101 System.out.print(target[i]+ ( i < target.length - 1 ? " " : ""));
102 }
103 System.out.println("]");
104 System.out.println();
105
106 System.out.println("Solution: ");
107 System.out.println(stats.getLegalLowestIndividual().getChromosome() );
108 System.out.println(stats.getLegalLowestIndividual());
109 System.out.format("found in %d ms and %d generations.\n", algostats.getExecutionTime(), algostats.getGenerations() );
110 System.out.println();
111 }
112
113 //XXX verificare se sta nei tutorials
114 public void onGeneration(GeneticAlgorithm ga, long time) {
115 Statistics stat = ga.getCurrentPopulation().getStatistics();
116 System.out.println("Current generation: " + ga.getGeneration());
117 System.out.println("\tBest score: " + stat.getLegalLowestScore());
118 System.out.println("\tAvg score: " + stat.getLegalScoreAvg());
119 }
120
121 private static void randomize(int[] sample) {
122 for(int i=0;i<sample.length;i++)
123 sample[i] = Random.getInstance().nextInt(0, MAX_INT+1);
124 }
125
126 public static void main(String[] args) {
127
128 Utils.printHeader();
129 System.out.println();
130
131 System.out.println("TUTORIAL 2:");
132 System.out.println("This algorithm aims to autonomously find a vector of integers that best matches with a target vector.");
133 System.out.println();
134
135 Random.getInstance().setStandardSeed();
136
137 PatternProblem problem = new PatternProblem();
138 int[] target = new int[CHROMOSOME_LENGTH];
139
140 randomize(target);
141 problem.run(target, 2);
142
143 randomize(target);
144 problem.run(target, 0);
145
146 }
147
148 }
|
|
Java2html
|
|
|
