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A library of composable components enabling simpler Computational Intelligence
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/**           __  __
 *    _____ _/ /_/ /_    Computational Intelligence Library (CIlib)
 *   / ___/ / / / __ \   (c) CIRG @ UP
 *  / /__/ / / / /_/ /   http://cilib.net
 *  \___/_/_/_/_.___/
 */
package net.sourceforge.cilib.entity.operators;

import com.google.common.collect.Lists;
import java.util.List;
import net.sourceforge.cilib.controlparameter.ConstantControlParameter;
import net.sourceforge.cilib.controlparameter.ControlParameter;
import net.sourceforge.cilib.entity.Entity;
import net.sourceforge.cilib.entity.operators.crossover.CrossoverStrategy;
import net.sourceforge.cilib.entity.operators.crossover.OnePointCrossoverStrategy;
import net.sourceforge.cilib.math.random.ProbabilityDistributionFunction;
import net.sourceforge.cilib.math.random.UniformDistribution;
import net.sourceforge.cilib.util.selection.Samples;
import net.sourceforge.cilib.util.selection.recipes.RandomSelector;
import net.sourceforge.cilib.util.selection.recipes.Selector;

public class CrossoverOperator implements Operator {

    private static final long serialVersionUID = -5058325193277909244L;

    private CrossoverStrategy crossoverStrategy;
    private ControlParameter crossoverProbability;
    private ProbabilityDistributionFunction randomDistribution;
    private Selector selectionStrategy;

    public CrossoverOperator() {
        this(new OnePointCrossoverStrategy(), ConstantControlParameter.of(0.5),
                new RandomSelector(), new UniformDistribution());
    }

    public CrossoverOperator(CrossoverStrategy strategy, ControlParameter probability,
            Selector selector, ProbabilityDistributionFunction random) {
        this.crossoverProbability = probability;
        this.randomDistribution = random;
        this.selectionStrategy = selector;
        this.crossoverStrategy = strategy;
    }

    public CrossoverOperator(CrossoverOperator copy) {
        this.crossoverProbability = copy.crossoverProbability.getClone();
        this.randomDistribution = copy.randomDistribution;
        this.selectionStrategy = copy.selectionStrategy;
        this.crossoverStrategy = copy.crossoverStrategy.getClone();
    }

    @Override
    public CrossoverOperator getClone() {
        return new CrossoverOperator(this);
    }

    public  List crossover(fj.data.List parentCollection) {
        if (randomDistribution.getRandomNumber() < crossoverProbability.getParameter()) {
            return crossoverStrategy.crossover(selectionStrategy
                    .on(parentCollection).select(Samples
                    .first(crossoverStrategy.getNumberOfParents())));
        }

        return Lists.newArrayList();
    }

    public ControlParameter getCrossoverProbability() {
        return crossoverProbability;
    }

    public void setCrossoverProbability(ControlParameter crossoverProbability) {
        this.crossoverProbability = crossoverProbability;
    }

    public ProbabilityDistributionFunction getRandomDistribution() {
        return randomDistribution;
    }

    public void setRandomDistribution(ProbabilityDistributionFunction randomNumber) {
        this.randomDistribution = randomNumber;
    }

    public Selector getSelectionStrategy() {
        return selectionStrategy;
    }

    public void setSelectionStrategy(Selector selectionStrategy) {
        this.selectionStrategy = selectionStrategy;
    }

    public CrossoverStrategy getCrossoverStrategy() {
        return crossoverStrategy;
    }

    public void setCrossoverStrategy(CrossoverStrategy crossoverStrategy) {
        this.crossoverStrategy = crossoverStrategy;
    }
}