unbalancedcrossover.java

著名的开源仿真软件yale

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2002, 2003
 *      Simon Fischer, Ralf Klinkenberg, Ingo Mierswa, 
 *          Katharina Morik, Oliver Ritthoff
 *      Artificial Intelligence Unit
 *      Computer Science Department
 *      University of Dortmund
 *      44221 Dortmund,  Germany
 *  email: [e-mail]
 *  web:   http://yale.cs.uni-dortmund.de/
 *
 *  This program is free software; you can redistribute it and/or
 *  modify it under the terms of the GNU General Public License as 
 *  published by the Free Software Foundation; either version 2 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
 *  General Public License for more details.
 *
 *  You should have received a copy of the GNU General Public License
 *  along with this program; if not, write to the Free Software
 *  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
 *  USA.
 */
package edu.udo.cs.yale.operator.features.ga;

import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.tools.RandomGenerator;

import java.util.List;
import java.util.ListIterator;
import java.util.LinkedList;

/** This <tt>PopulationOperator</tt> applies a crossover on two example sets. 
 *  Crossover type can be ONE_POINT or UNIFORM. In difference to SelectionCrossover 
 *  the attribute vectors can have different lengths. <br>
 *  This crossover type should only be used for SINGLE_VALUEs!
 *  @author ingo
 *  @version $Id: UnbalancedCrossover.java,v 2.3 2003/06/29 14:40:43 mierswa Exp $
 */
public class UnbalancedCrossover extends SelectionCrossover {

    /** Creates a new generating crossover with the given type which will
     *  be applied with the given probability. ParentsSurvive indicates if the original attributes
     *  maintain.
     */
    public UnbalancedCrossover(int type, double prob, boolean parentsSurvive) {
	super(type, prob, parentsSurvive, RandomGenerator.getGlobalRandomGenerator());
    }

    /** Applies the crossover. Works directly on the given example sets. 
     */
    public void crossover(ExampleSet es1, ExampleSet es2) {
	LinkedList dummyList1 = null;
	LinkedList dummyList2 = null;

	switch (type) {
	case SelectionCrossover.ONE_POINT:
	    int maxSize = Math.max(es1.getNumberOfAttributes(), es2.getNumberOfAttributes());
	    if (maxSize < 2) return;
	    int splitPoint = 1 + random.nextInt(maxSize - 1);

	    dummyList1 = new LinkedList();
	    dummyList2 = new LinkedList();

	    while (es1.getNumberOfAttributes() > splitPoint) 
		dummyList1.add(es1.removeAttribute(splitPoint));
	    while (es2.getNumberOfAttributes() > splitPoint) 
		dummyList2.add(es2.removeAttribute(splitPoint));

	    es1.addAllAttributes(dummyList2);
	    es2.addAllAttributes(dummyList1);

	    break;

	case SelectionCrossover.UNIFORM:
	    int biggerSize = Math.max(es1.getNumberOfAttributes(), es2.getNumberOfAttributes());

	    boolean[] swap = new boolean[biggerSize];
  	    for (int i = 0; i < swap.length; i++) {
  		swap[i] = random.nextBoolean();
  	    }

	    dummyList1 = new LinkedList();
	    dummyList2 = new LinkedList();

	    for (int i = swap.length - 1; i >= 0; i--) {
		if (swap[i]) {
		    if (i < es1.getNumberOfAttributes()) 
			dummyList1.addFirst(es1.removeAttribute(i));
		    if (i < es2.getNumberOfAttributes()) 
			dummyList2.addFirst(es2.removeAttribute(i));
		}
	    }

	    es1.addAllAttributes(dummyList2);
	    es2.addAllAttributes(dummyList1);

	    break;
	default:
	}
    }
}