WeaselSelector.java
01 package org.jenetics.ext;
02 
03 import static java.lang.String.format;
04 import static java.util.Objects.requireNonNull;
05 
06 import org.jenetics.Gene;
07 import org.jenetics.Optimize;
08 import org.jenetics.Phenotype;
09 import org.jenetics.Population;
10 import org.jenetics.Selector;
11 import org.jenetics.stat.MinMax;
12 
13 /**
14  * Selector implementation which is part of the
15  * <a href="https://en.wikipedia.org/wiki/Weasel_program">Weasel program</a>
16  * algorithm. The <i>Weasel program</i> is an thought experiment by Richard
17  * Dawkins to illustrate the functioning of the evolution: random <i>mutation</i>
18  * combined with non-random cumulative <i>selection</i>.
19  <p>
20  * The selector always returns populations which only contains "{@code count}"
21  * instances of the <i>best</i> {@link Phenotype}.
22  </p>
23  {@link org.jenetics.engine.Engine} setup for the <i>Weasel program:</i>
24  <pre>{@code
25  * final Engine<CharacterGene, Integer> engine = Engine
26  *     .builder(fitness, gtf)
27  *      // Set the 'WeaselSelector'.
28  *     .selector(new WeaselSelector<>())
29  *      // Disable survivors selector.
30  *     .offspringFraction(1)
31  *      // Set the 'WeaselMutator'.
32  *     .alterers(new WeaselMutator<>(0.05))
33  *     .build();
34  * }</pre>
35  *
36  @see <a href="https://en.wikipedia.org/wiki/Weasel_program">Weasel program</a>
37  @see WeaselMutator
38  *
39  @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
40  @since 3.5
41  @version 3.5
42  */
43 public class WeaselSelector<
44     extends Gene<?, G>,
45     extends Comparable<? super C>
46 >
47     implements Selector<G, C>
48 {
49     @Override
50     public Population<G, C> select(
51         final Population<G, C> population,
52         final int count,
53         final Optimize opt
54     ) {
55         requireNonNull(population, "Population");
56         requireNonNull(opt, "Optimization");
57         if (count < 0) {
58             throw new IllegalArgumentException(format(
59                 "Selection count must be greater or equal then zero, but was %s",
60                 count
61             ));
62         }
63 
64         final MinMax<Phenotype<G, C>> minMax = population.stream()
65             .collect(MinMax.toMinMax(opt.ascending()));
66 
67         return new Population<G, C>(count).fill(minMax::getMax, count);
68     }
69 }