1 package net.bmahe.genetics4j.core.selection;
2
3 import java.util.Comparator;
4 import java.util.List;
5 import java.util.random.RandomGenerator;
6
7 import org.apache.commons.lang3.Validate;
8 import org.apache.logging.log4j.LogManager;
9 import org.apache.logging.log4j.Logger;
10
11 import net.bmahe.genetics4j.core.Genotype;
12 import net.bmahe.genetics4j.core.Individual;
13 import net.bmahe.genetics4j.core.Population;
14 import net.bmahe.genetics4j.core.spec.AbstractEAConfiguration;
15 import net.bmahe.genetics4j.core.spec.selection.ProportionalTournament;
16 import net.bmahe.genetics4j.core.spec.selection.SelectionPolicy;
17
18 public class ProportionalTournamentSelector<T extends Comparable<T>> implements Selector<T> {
19 final static public Logger logger = LogManager.getLogger(ProportionalTournamentSelector.class);
20
21 private final SelectionPolicy selectionPolicy;
22 private final RandomGenerator randomGenerator;
23
24 public ProportionalTournamentSelector(final SelectionPolicy _selectionPolicy,
25 final RandomGenerator _randomGenerator) {
26 Validate.notNull(_selectionPolicy);
27 Validate.isInstanceOf(ProportionalTournament.class, _selectionPolicy);
28 Validate.notNull(_randomGenerator);
29
30 this.selectionPolicy = _selectionPolicy;
31 this.randomGenerator = _randomGenerator;
32 }
33
34 @Override
35 public Population<T> select(final AbstractEAConfiguration<T> eaConfiguration, final int numIndividuals,
36 final List<Genotype> population, final List<T> fitnessScore) {
37 Validate.notNull(eaConfiguration);
38 Validate.notNull(population);
39 Validate.notNull(fitnessScore);
40 Validate.isTrue(numIndividuals > 0);
41 Validate.isTrue(population.size() == fitnessScore.size());
42
43 @SuppressWarnings("unchecked")
44 final ProportionalTournament<T> proportionalTournament = (ProportionalTournament<T>) selectionPolicy;
45 final Comparator<Individual<T>> firstComparator = proportionalTournament.firstComparator();
46 final Comparator<Individual<T>> secondComparator = proportionalTournament.secondComparator();
47 final int numCandidates = proportionalTournament.numCandidates();
48 final double proportionFirst = proportionalTournament.proportionFirst();
49
50 final Comparator<Individual<T>> firstComparatorOptimize = switch (eaConfiguration.optimization()) {
51 case MAXIMIZE -> firstComparator;
52 case MINIMIZE -> firstComparator.reversed();
53 };
54
55 final Comparator<Individual<T>> secondComparatorOptimize = switch (eaConfiguration.optimization()) {
56 case MAXIMIZE -> secondComparator;
57 case MINIMIZE -> secondComparator.reversed();
58 };
59
60 logger.debug("Selecting {} individuals", numIndividuals);
61
62 final Population<T> selectedIndividuals = new Population<>();
63
64 while (selectedIndividuals.size() < numIndividuals) {
65
66 final Comparator<Individual<T>> comparator = randomGenerator.nextDouble() < proportionFirst
67 ? firstComparatorOptimize
68 : secondComparatorOptimize;
69
70 final Individual<T> selected = randomGenerator.ints(numCandidates, 0, population.size())
71 .boxed()
72 .map((i) -> Individual.of(population.get(i), fitnessScore.get(i)))
73 .max(comparator)
74 .get();
75
76 selectedIndividuals.add(selected.genotype(), selected.fitness());
77 }
78
79 return selectedIndividuals;
80 }
81 }