yagga.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.operator.OperatorException;
import edu.udo.cs.yale.operator.parameter.*;
import edu.udo.cs.yale.generator.*;
import edu.udo.cs.yale.operator.features.*;
import edu.udo.cs.yale.tools.LogService;
import edu.udo.cs.yale.example.ExampleSet;
import edu.udo.cs.yale.example.AttributeSelectionExampleSet;

import java.util.ArrayList;
import java.util.List;
import java.util.LinkedList;

/** YAGGA is an acronym for Yet Another Generating Genetic Algorithm. 
 *  Its approach to generating new attributes differs from the original one. 
 *  The (generating) mutation can do one of the following things with 
 *  different probabilities:
 *  <ul>
 *    <li>Probability {@yale.math p/4}: Add a newly generated attribute to the feature vector</li>
 *    <li>Probability {@yale.math p/4}: Add a randomly chosen original attribute to the feature vector</li>
 *    <li>Probability {@yale.math p/2}: Remove a randomly chosen attribute from the feature vector</li>
 *  </ul>
 *  Thus it is guaranteed that the length of the feature vector can both
 *  grow and shrink. On average it will keep its original length, unless
 *  longer or shorter individuals prove to have a better fitness.
 *
 *  @yale.xmlclass YAGGA
 *  @author ingo, simon
 *  @version $Id: YAGGA.java,v 2.4 2003/07/09 15:19:58 fischer Exp $
 */
public class YAGGA extends GeneticAlgorithm {

    /** Returns the generating crossover <tt>PopulationOperator</tt>.
     */
    PopulationOperator getCrossoverPopulationOperator() {
	double pCrossover = getParameterAsDouble("p_crossover");
	int crossoverType = getParameterAsInt("crossover_type");
	return new UnbalancedCrossover(crossoverType, pCrossover, false);
    }

    /** Returns the generating mutation <tt>PopulationOperator</tt>.
     */
    PopulationOperator getMutationPopulationOperator() throws OperatorException {
	double pMutation = getParameterAsDouble("p_mutation");
	
	// creates the generators
	ArrayList generators = new ArrayList();
	
	if (getParameterAsBoolean("reciprocal_value")) {
	    FeatureGenerator g =  new ReciprocalValueGenerator(true);
	    generators.add(g);
	}
	if (getParameterAsBoolean("function_characteristica")) {
	    FeatureGenerator g =  new FunctionCharacteristicaGenerator();
	    generators.add(g);
	}
	if (getParameterAsBoolean("use_plus")) {
	    FeatureGenerator g =  new BasicArithmeticOperationGenerator(0, true);
	    generators.add(g);
	}
	if (getParameterAsBoolean("use_diff")) {
	    FeatureGenerator g =  new BasicArithmeticOperationGenerator(1, true);
	    generators.add(g);
	}
	if (getParameterAsBoolean("use_mult")) {
	    FeatureGenerator g =  new BasicArithmeticOperationGenerator(2, true);
	    generators.add(g);
	}
	if (getParameterAsBoolean("use_div")) {
	    FeatureGenerator g =  new BasicArithmeticOperationGenerator(3, true);
	    generators.add(g);
	}

	if (generators.size()==0) {
	    LogService.logMessage("No FeatureGenerators specified for " + getName() + ".", LogService.WARNING);
	} 

	// adds generation to the PreEval - List.
 	ExampleSet eSet = (ExampleSet)getInput(ExampleSet.class, false);

	List attributes = new LinkedList();
	for (int i = 0; i < eSet.getNumberOfAttributes(); i++) {
	    attributes.add(eSet.getAttribute(i));
	}

 	// adds generation to the PreEval - List.
 	return new GeneratingMutation(attributes, pMutation, generators);
    }    

    /** Creates a initial population.
     */
    public Population createInitialPopulation(AttributeSelectionExampleSet es) {
	Population pop1 = super.createInitialPopulation(es);
	Population pop2 = new Population();
	for (int i = 0; i < pop1.getNumberOfIndividuals(); i++) {
	    pop2.add(new AttributeSelectionExampleSet(pop1.get(i).createExampleSet()));
	}
	return pop2;
    }

    public List getParameterTypes() {
	List types = super.getParameterTypes();
	types.add(new ParameterTypeInt("max_number_of_new_attributes", "Max number of attributes to generate for an individual.", 0, Integer.MAX_VALUE, 1));
	types.add(new ParameterTypeDouble("p_generate", "Probability for an individual to be selected for generation.", 0, 1, 0.1));
	types.add(new ParameterTypeBoolean("reciprocal_value", "Generate reciprocal values.", true));
	types.add(new ParameterTypeBoolean("function_characteristica", "Generate function characteristica (for C9).", false));
	types.add(new ParameterTypeBoolean("use_plus", "Generate sums.", true));
	types.add(new ParameterTypeBoolean("use_diff", "Generate differences.", true));
	types.add(new ParameterTypeBoolean("use_mult", "Generate products.", true));
	types.add(new ParameterTypeBoolean("use_div", "Generate quotients.", true));
	return types;
    }
}