/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.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.operator.features.selection;
import java.util.LinkedList;
import java.util.List;
import java.util.Random;
import com.rapidminer.operator.features.Individual;
import com.rapidminer.operator.features.Population;
import com.rapidminer.operator.features.PopulationOperator;
/**
* Selects a given fixed number of individuals by subdividing a roulette wheel
* in sections of size proportional to the individuals' fitness values.
* Optionally keep the best individual.
*
* @author Simon Fischer, Ingo Mierswa
* @version $Id: RouletteWheel.java,v 1.3 2008/05/09 19:23:18 ingomierswa Exp $
*/
public class RouletteWheel implements PopulationOperator {
private int popSize;
private boolean keepBest;
private Random random;
public RouletteWheel(int popSize, boolean keepBest, Random random) {
this.popSize = popSize;
this.keepBest = keepBest;
this.random = random;
}
/** The default implementation returns true for every generation. */
public boolean performOperation(int generation) {
return true;
}
/**
* Subclasses may override this method and recalculate the fitness based on
* the given one, e.g. Boltzmann selection or scaled selection. The default
* implementation simply returns the given fitness.
*/
public double filterFitness(double fitness) {
return fitness;
}
public void operate(Population population) {
List<Individual> newGeneration = new LinkedList<Individual>();
if (keepBest) {
newGeneration.add(population.getBestIndividualEver());
}
double fitnessSum = 0;
for (int i = 0; i < population.getNumberOfIndividuals(); i++) {
fitnessSum += filterFitness(population.get(i).getPerformance().getMainCriterion().getFitness());
}
while (newGeneration.size() < popSize) {
double r = fitnessSum * random.nextDouble();
int j = 0;
double f = 0;
Individual individual = null;
do {
individual = population.get(j++);
f += filterFitness(individual.getPerformance().getMainCriterion().getFitness());
} while (f < r);
newGeneration.add(individual);
}
population.clear();
population.addAllIndividuals(newGeneration);
}
}