MultiObjectiveExample


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.multiobjective;
11  
12  import java.util.*;
13  import org.jgap.*;
14  import org.jgap.impl.*;
15  
16  /**
17   * Example for a multiobjective problem. Here, we have a function F with one
18   * input parameter t and two output values F1 and F2, with F1 = t�
19   * and F2 = (t - 2)�. This example is from Goldberg (pp. 199), who adapted it
20   * from Schaffer (1984).
21   *
22   * @author Klaus Meffert
23   * @since 2.6
24   */
25  public class MultiObjectiveExample {
26    /** String containing the CVS revision. Read out via reflection!*/
27    private final static String   CVS_REVISION = "$Revision: 1.3 $";
28  
29    /**
30     * The total number of times we'll let the population evolve.
31     */
32    private static final int MAX_ALLOWED_EVOLUTIONS = 200;
33  
34    /**
35     * Executes the genetic algorithm.
36     *
37     * @throws Exception
38     *
39     * @author Klaus Meffert
40     * @since 2.6
41     */
42    public void execute()
43        throws Exception   {
44      // Start with a DefaultConfiguration, which comes setup with the
45      // most common settings.
46      // -------------------------------------------------------------
47      Configuration conf = new DefaultConfiguration();
48      // Add BestChromosomesSelector with doublettes allowed.
49      // ----------------------------------------------------
50      conf.getNaturalSelectors(true).clear();
51      BestChromosomesSelector bestChromsSelector = new BestChromosomesSelector(
52          conf, 0.95d);
53      bestChromsSelector.setDoubletteChromosomesAllowed(true);
54      conf.addNaturalSelector(bestChromsSelector, true);
55  
56      conf.setFitnessEvaluator(new MOFitnessEvaluator());
57      conf.setPreservFittestIndividual(false);
58      conf.setKeepPopulationSizeConstant(false);
59      // Set the fitness function we want to use, which is our
60      // MinimizingMakeChangeFitnessFunction. We construct it with
61      // the target amount of change passed in to this method.
62      // ---------------------------------------------------------
63      BulkFitnessFunction myFunc =
64          new MultiObjectiveFitnessFunction();
65      conf.setBulkFitnessFunction(myFunc);
66  
67      // Set sample chromosome.
68      // ----------------------
69      Gene[] sampleGenes = new Gene[1];
70      sampleGenes[0] = new DoubleGene(conf, MultiObjectiveFitnessFunction.MIN_X,
71                                      MultiObjectiveFitnessFunction.MAX_X);
72      IChromosome sampleChromosome = new Chromosome(null, sampleGenes);
73      conf.setSampleChromosome(sampleChromosome);
74  
75      // Finally, we need to tell the Configuration object how many
76      // Chromosomes we want in our population. The more Chromosomes,
77      // the larger number of potential solutions (which is good for
78      // finding the answer), but the longer it will take to evolve
79      // the population (which could be seen as bad).
80      // ------------------------------------------------------------
81      conf.setPopulationSize(500);
82      // Create random initial population of Chromosomes.
83      // ------------------------------------------------
84      Genotype population = Genotype.randomInitialGenotype(conf);
85      // Now we initialize the population randomly, anyway!
86      // If you want to load previous results from file, remove the next line!
87      population = Genotype.randomInitialGenotype(conf);
88      // Evolve the population. Since we don't know what the best answer
89      // is going to be, we just evolve the max number of times.
90      // ---------------------------------------------------------------
91      for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
92        population.evolve();
93      }
94      // Remove solutions that are not Pareto-optimal.
95      // ---------------------------------------------
96      List chroms = population.getPopulation().getChromosomes();
97      int size = population.getPopulation().getChromosomes().size();
98      int i = 0;
99      boolean removed = false;
100     MOFitnessComparator comp = new MOFitnessComparator();
101     while (i<size-1) {
102       IChromosome chrom1 = population.getPopulation().getChromosome(i);
103       int j = i + 1;
104       while (j < size) {
105         IChromosome chrom2 = population.getPopulation().getChromosome(j);
106         int res = comp.compare(chrom1, chrom2);
107         if (res != 0) {
108           if (res == -1) {
109             population.getPopulation().getChromosomes().remove(i);
110             size--;
111             removed = true;
112             break;
113           }
114           else {
115             population.getPopulation().getChromosomes().remove(j);
116             size--;
117           }
118         }
119         else {
120           j++;
121         }
122       }
123       if (removed) {
124         removed = false;
125       }
126       else {
127         i++;
128       }
129     }
130     // Print all Pareto-optimal solutions.
131     // -----------------------------------
132     Collections.sort(chroms, comp);
133     for (int k=0;k<chroms.size();k++) {
134       Chromosome bestSolutionSoFar = (Chromosome) chroms.get(k);
135       System.out.println(MultiObjectiveFitnessFunction.
136                          getVector(bestSolutionSoFar));
137     }
138   }
139 
140   /**
141    * Main method to run the example.
142    *
143    * @param args ignored
144    * @throws Exception
145    *
146    * @author Klaus Meffert
147    * @since 2.6
148    */
149   public static void main(String  [] args)
150       throws Exception   {
151     MultiObjectiveExample instance = new MultiObjectiveExample();
152     instance.execute();
153   }
154 
155   /**
156    * @author Klaus Meffert
157    * @since 2.6
158    */
159   public class MOFitnessComparator
160       implements java.util.Comparator   {
161 
162     public int compare(final Object   a_chrom1, final Object   a_chrom2) {
163       List v1 = ( (Chromosome) a_chrom1).getMultiObjectives();
164       List v2 = ( (Chromosome) a_chrom2).getMultiObjectives();
165       int size = v1.size();
166       if (size != v2.size()) {
167         throw new RuntimeException  ("Size of objectives inconsistent!");
168       }
169       boolean better1 = false;
170       boolean better2 = false;
171       for (int i = 0; i < size; i++) {
172         double d1 = ( (Double  ) v1.get(i)).doubleValue();
173         double d2 = ( (Double  ) v2.get(i)).doubleValue();
174         if (d1 < d2) {
175           better1 = true;
176         }
177         else if (d2 < d1) {
178           better2 = true;
179         }
180       }
181       if (better1) {
182         if (better2) {
183           return 0;
184         }
185         else {
186           return 1;
187         }
188       }
189       else {
190         if (better2) {
191           return -1;
192         }
193         else {
194           return 0;
195         }
196       }
197     }
198   }
199 }
200
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