selectsinglegreatestfitness.java

遗传算法源代码,实现了选择操作、交叉操作和变异操作

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/**
 * SelectionAlgorithm Class
 *
 * location: net.openai.ai.ga.selection.SelectionAlgorithm
 *
 */
package net.openai.ai.ga.selection.common;

import net.openai.ai.ga.selection.*;
import net.openai.ai.ga.population.*;
import net.openai.ai.ga.cell.*;

import java.util.Iterator;

/**
 * <code>SelectGreatestFitness</code> is a <code>SelectionAlgorithm</code>
 * that will return a portion of the passed <code>Population</code> that
 * have the greatest fitness. It will choose either a fixed number or a
 * percentage.
 *
 * @author	Jared Grubb
 * @version	%I%, %G%
 * @since	JDK1.3
 */
public class SelectSingleGreatestFitness implements SelectionAlgorithm {
    /**
     * Creates a new <code>SelectSingleGreatestFitness</code> object for
     * choosing a single <code>Cell</code> out of the specified <code>
     * Population</code> who has the greatest fitness.
     */
    public SelectSingleGreatestFitness() {
    }

    /**
     * Chooses the <code>Cell</code> in the specified <code>Population</code>
     * with the greatest fitness.
     *
     * The returned population is guaranteed to return the cell of the
     * specified population such that there are no other members in that
     * population who are greater than that returned. This means that cells
     * who tie the highest are ignored. For example, if
     * the fitness of a population was {1,1,2,3,3,3,4,5,5} then only one cell
     * of fitness 5 will be returned.
     *
     * @param pop  the <code>Population</code> to choose from
     * @returns a selected <code>Population</code> of one cell
     */
    public Population selectFromPopulation(Population pop) {
        Population output = new Population();

        Cell tempCell=null;
        double tempFitness;

        Cell bestCell = null;
        double bestFitness = Double.NEGATIVE_INFINITY;

        for(Iterator i=pop.getCellIterator(); i.hasNext(); ) {
            tempCell = (Cell) i.next();
            tempFitness = tempCell.getFitness();

            if (tempFitness>=bestFitness) {
               bestFitness = tempFitness;
               bestCell = tempCell;
            }
        }
        output.addCell(bestCell);
        return output;
    }
}



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