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Java > Open Source Codes > examples > chromInit > ChromosomeInit


1 /*
2  * This file is part of JGAP.
3  *
4  * JGAP offers a dual license model containing the LGPL as well as the MPL.
5  *
6  * For licencing information please see the file license.txt included with JGAP
7  * or have a look at the top of class org.jgap.Chromosome which representatively
8  * includes the JGAP license policy applicable for any file delivered with JGAP.
9  */
10 package examples.chromInit;
11
12 import org.jgap.*;
13 import org.jgap.impl.*;
14
15 /**
16  * Simple test class that demonstrates how to initialize chromosomes with
17  * different numbers of Genes.
18  *
19  * @author Klaus Meffert
20  * @since 2.4
21  */
22 public class ChromosomeInit {
23   /** String containing the CVS revision. Read out via reflection!*/
24   private static final String JavaDoc CVS_REVISION = "$Revision: 1.6 $";
25
26   public static void main(String JavaDoc[] args) {
27     int numEvolutions = 500;
28     // Create configuration by using DefaultConfiguration and removing
29 // CrossoverOperator. Removal necessary because that operator does not
30 // work with chromosomes of different gene size!
31 Configuration gaConf = new DefaultConfiguration();
32     // The next statement is dirty and to be avoided outside this example!
33 gaConf.getGeneticOperators().remove(0);
34     gaConf.setPreservFittestIndividual(true);
35     gaConf.setKeepPopulationSizeConstant(false);
36     try {
37       int chromeSize;
38       if (args.length == 0) {
39         chromeSize = 7;
40       }
41       else {
42         chromeSize = Integer.parseInt(args[0]);
43       }
44       if (chromeSize > 15) {
45         System.err.println("This example does not handle " +
46                            "Chromosomes greater than 15 bits in length.");
47         System.exit( -1);
48       }
49       IChromosome sampleChromosome = new Chromosome(gaConf,
50           new BooleanGene(gaConf), chromeSize);
51       gaConf.setSampleChromosome(sampleChromosome);
52       gaConf.setPopulationSize(20);
53       gaConf.setFitnessFunction(new MaxFunction());
54       // Completely initialize the population with custom code.
55 // Notice that we assign the double number of Genes to
56 // each other Chromosome.
57 // ------------------------------------------------------
58 int populationSize = gaConf.getPopulationSize();
59       Population pop = new Population(gaConf, populationSize);
60       for (int i = 0; i < populationSize; i++) {
61         int mult;
62         // Every second Chromosome has double the number of Genes.
63 // -------------------------------------------------------
64 if (i % 2 == 0) {
65           mult = 1;
66         }
67         else {
68           mult = 2;
69         }
70         Gene[] sampleGenes = sampleChromosome.getGenes();
71         Gene[] newGenes = new Gene[sampleGenes.length * mult];
72         RandomGenerator generator = gaConf.getRandomGenerator();
73         for (int j = 0; j < newGenes.length; j = j + mult) {
74           // We use the newGene() method on each of the genes in the
75 // sample Chromosome to generate our new Gene instances for
76 // the Chromosome we're returning. This guarantees that the
77 // new Genes are setup with all of the correct internal state
78 // for the respective gene position they're going to inhabit.
79 // ----------------------------------------------------------
80 newGenes[j] = sampleGenes[j / mult].newGene();
81           // Set the gene's value (allele) to a random value.
82 // ------------------------------------------------
83 newGenes[j].setToRandomValue(generator);
84           if (mult > 1) {
85             newGenes[j + 1] = sampleGenes[j / 2].newGene();
86             // Set the gene's value (allele) to a random value.
87 // ------------------------------------------------
88 newGenes[j + 1].setToRandomValue(generator);
89           }
90         }
91         IChromosome chrom = Chromosome.randomInitialChromosome(gaConf);
92         chrom.setGenes(newGenes);
93         pop.addChromosome(chrom);
94       }
95       // Now we need to construct the Genotype. This could otherwise be
96 // accomplished more easily by writing
97 // "Genotype genotype = Genotype.randomInitialGenotype(...)"
98 Genotype genotype = new Genotype(gaConf, pop);
99       int progress = 0;
100       int percentEvolution = numEvolutions / 100;
101       for (int i = 0; i < numEvolutions; i++) {
102         genotype.evolve();
103         // Print progress.
104 // ---------------
105 if (percentEvolution > 0 && i % percentEvolution == 0) {
106           progress++;
107           IChromosome fittest = genotype.getFittestChromosome();
108           double fitness = fittest.getFitnessValue();
109           System.out.println("Fittest Chromosome has value " + fitness);
110         }
111       }
112       IChromosome fittest = genotype.getFittestChromosome();
113       System.out.println("Fittest Chromosome has value " +
114                          fittest.getFitnessValue());
115     }
116     catch (InvalidConfigurationException e) {
117       e.printStackTrace();
118       System.exit( -2);
119     }
120   }
121 }
122
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