/*
 *  RapidMiner
 *
 *  Copyright (C) 2001-2014 by RapidMiner and the contributors
 *
 *  Complete list of developers available at our web site:
 *
 *       http://rapidminer.com
 *
 *  This program is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Affero General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This program is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Affero General Public License for more details.
 *
 *  You should have received a copy of the GNU Affero General Public License
 *  along with this program.  If not, see http://www.gnu.org/licenses/.
 */
package com.rapidminer.tools.math.optimization.ec.es;

import java.util.LinkedList;
import java.util.List;
import java.util.Random;

/**
 * Performs tournaments with k participants. The winner of each tournament is
 * added to the next population. This will be repeated until the desired
 * population size is reached. The tournament size k represents the selection
 * pressure. For small sizes (like 2) relatively bad individuals have a good
 * chance to survive. If k reaches the population size, only the best individual
 * will survive.
 * 
 * @author Ingo Mierswa
 *          Exp $
 */
public class TournamentSelection implements PopulationOperator {

	private double popSize;

	private double tournamentFraction;

	private boolean keepBest = false;

    private Random random;
    
    
	public TournamentSelection(int popSize, double tournamentFraction, boolean keepBest, Random random) {
		this.popSize = popSize;
		this.keepBest = keepBest;
		this.tournamentFraction = tournamentFraction;
        this.random = random;
	}

	public void operate(Population population) {
		List<Individual> newGeneration = new LinkedList<Individual>();
		if (population.getNumberOfIndividuals() > 0) {
		    int tournamentSize = Math.max((int) Math.round(population.getNumberOfIndividuals() * tournamentFraction), 1);
		    if (keepBest && (population.getBestEver() != null)) {
		        newGeneration.add(population.getBestEver());
		    }

		    while (newGeneration.size() < this.popSize) {
		        Individual winner = null;
		        for (int k = 0; k < tournamentSize; k++) {
		            Individual current = population.get(random.nextInt(population.getNumberOfIndividuals()));
		            if ((winner == null) || (current.getFitnessValues()[0] > winner.getFitnessValues()[0]))
		                winner = current;
		        }
		        newGeneration.add(winner);
		    }
        }
		population.clear();
		population.addAll(newGeneration);
	}
}