/**
* Copyright (C) 2001-2017 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;
/**
* Similar to a the roulette wheel selection the fitness values of all individuals build a partition
* of the 360 degrees of a wheel. The wheel is turned only once and the individuals are selected
* based on equidistant marks on the wheel. Optionally the best individual is also kept.
*
* @author Ingo Mierswa ingomierswa Exp $
*/
public class StochasticUniversalSampling implements PopulationOperator {
private int popSize;
private boolean keepBest;
private Random random;
public StochasticUniversalSampling(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;
}
@Override
public void operate(Population population) {
List<Individual> newGeneration = new LinkedList<Individual>();
if (keepBest) {
newGeneration.add(population.getBestEver());
}
int numberOfMarks = popSize - newGeneration.size();
double distance = 1.0d / numberOfMarks;
double r = random.nextDouble() / numberOfMarks;
for (int i = 0; i < numberOfMarks; i++) {
double f = 0;
int j = 0;
Individual individual = null;
do {
individual = population.get(j++);
f += filterFitness(individual.getFitness().getMainCriterion().getFitness());
} while (f < r);
newGeneration.add(individual);
r += distance;
}
population.clear();
population.addAll(newGeneration);
}
}