TournamentSelection.java

/*
 * Artificial Intelligence for Humans
 * Volume 2: Nature Inspired Algorithms
 * Java Version
 * http://www.aifh.org
 * http://www.jeffheaton.com
 *
 * Code repository:
 * https://github.com/jeffheaton/aifh
 *
 * Copyright 2014 by Jeff Heaton
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * For more information on Heaton Research copyrights, licenses
 * and trademarks visit:
 * http://www.heatonresearch.com/copyright
 */
package com.heatonresearch.aifh.evolutionary.opp.selection;

import com.heatonresearch.aifh.evolutionary.genome.Genome;
import com.heatonresearch.aifh.evolutionary.species.Species;
import com.heatonresearch.aifh.evolutionary.train.EvolutionaryAlgorithm;
import com.heatonresearch.aifh.evolutionary.train.basic.BasicEA;
import com.heatonresearch.aifh.randomize.GenerateRandom;

import java.io.Serializable;

/**
 * Tournament select can be used to select a fit (or unfit) genome from a
 * species. The selection is run a set number of rounds. Each round two random
 * participants are chosen. The more fit participant continues to the next
 * round.
 * <p/>
 * http://en.wikipedia.org/wiki/Tournament_selection
 */
public class TournamentSelection implements SelectionOperator, Serializable {

    /**
     * The serial id.
     */
    private static final long serialVersionUID = 1L;

    /**
     * The trainer being used.
     */
    private EvolutionaryAlgorithm trainer;

    /**
     * The number of rounds.
     */
    private int rounds;

    /**
     * Construct a tournament selection.
     *
     * @param theTrainer The trainer.
     * @param theRounds  The number of rounds to use.
     */
    public TournamentSelection(final EvolutionaryAlgorithm theTrainer,
                               final int theRounds) {
        this.trainer = theTrainer;
        this.rounds = theRounds;
    }

    /**
     * @return The number of rounds.
     */
    public int getRounds() {
        return this.rounds;
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public EvolutionaryAlgorithm getTrainer() {
        return this.trainer;
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public int performAntiSelection(final GenerateRandom rnd, final Species species) {
        int worstIndex = rnd.nextInt(species.getMembers().size());
        Genome worst = species.getMembers().get(worstIndex);
        BasicEA.calculateScoreAdjustment(worst,
                this.trainer.getScoreAdjusters());

        for (int i = 0; i < this.rounds; i++) {
            final int competitorIndex = rnd.nextInt(species.getMembers().size());
            final Genome competitor = species.getMembers().get(competitorIndex);

            // force an invalid genome to lose
            if (Double.isInfinite(competitor.getAdjustedScore())
                    || Double.isNaN(competitor.getAdjustedScore())) {
                return competitorIndex;
            }

            BasicEA.calculateScoreAdjustment(competitor,
                    this.trainer.getScoreAdjusters());
            if (!this.trainer.getSelectionComparator().isBetterThan(competitor,
                    worst)) {
                worst = competitor;
                worstIndex = competitorIndex;
            }
        }
        return worstIndex;
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public int performSelection(final GenerateRandom rnd, final Species species) {
        int bestIndex = rnd.nextInt(species.getMembers().size());
        Genome best = species.getMembers().get(bestIndex);
        BasicEA.calculateScoreAdjustment(best, this.trainer.getScoreAdjusters());

        for (int i = 0; i < this.rounds; i++) {
            final int competitorIndex = rnd.nextInt(species.getMembers().size());
            final Genome competitor = species.getMembers().get(competitorIndex);

            // only evaluate valid genomes
            if (!Double.isInfinite(competitor.getAdjustedScore())
                    && !Double.isNaN(competitor.getAdjustedScore())) {
                BasicEA.calculateScoreAdjustment(competitor,
                        this.trainer.getScoreAdjusters());
                if (this.trainer.getSelectionComparator().isBetterThan(
                        competitor, best)) {
                    best = competitor;
                    bestIndex = competitorIndex;
                }
            }
        }
        return bestIndex;
    }

    /**
     * Set the number of rounds.
     *
     * @param rounds The number of rounds.
     */
    public void setRounds(final int rounds) {
        this.rounds = rounds;
    }

    /**
     * Set the trainer.
     *
     * @param trainer The trainer.
     */
    public void setTrainer(final EvolutionaryAlgorithm trainer) {
        this.trainer = trainer;
    }
}