abstractgeneratinggeneticalgorithm.java

一个很好的LIBSVM的JAVA源码。对于要研究和改进SVM算法的学者。可以参考。来自数据挖掘工具YALE工具包。

/*
 *  YALE - Yet Another Learning Environment
 *  Copyright (C) 2001-2004
 *      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.parameter.*;
import edu.udo.cs.yale.operator.OperatorException;
import edu.udo.cs.yale.generator.*;
import edu.udo.cs.yale.operator.features.*;
import edu.udo.cs.yale.tools.LogService;

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

/** In contrast to its superclass {@link GeneticAlgorithm}, the {@link GeneratingGeneticAlgorithm}
 *  generates new attributes and thus can change the length of an individual. Therfore specialized mutation
 *  and crossover operators are being applied. Generators are chosen at random from a list of generators
 *  specified by boolean parameters. <br/>
 *
 *  Since this operator does not contain algorithms to extract features from value series, it is restricted
 *  to example sets with only single attributes. For automatic feature extraction from values series the 
 *  value series plugin for Yale written by Ingo Mierswa should be used. It is available at 
 *  <a href="http://yale.cs.uni-dortmund.de">http://yale.cs.uni-dortmund.de</a>
 *
 *  @yale.reference Ritthoff/etal/2001a
 *  @yale.xmlclass GeneratingGeneticAlgorithm
 *  @author ingo
 *  @version $Id: AbstractGeneratingGeneticAlgorithm.java,v 2.3 2004/08/27 11:57:36 ingomierswa Exp $
 */
public abstract class AbstractGeneratingGeneticAlgorithm extends AbstractGeneticAlgorithm {

    /** Returns a specialized generating mutation, e.g. a <code>AttributeGenerator</code>. */
    protected abstract PopulationOperator getGeneratingPopulationOperator();

    protected List getPreProcessingPopulationOperators() {
	List popOps = super.getPreProcessingPopulationOperators();
	PopulationOperator generator = getGeneratingPopulationOperator();
	if (generator != null)
	    popOps.add(generator);
	return popOps;
    }

    /** Returns an <code>UnbalancedCrossover</code>. */
    protected PopulationOperator getCrossoverPopulationOperator() {
	double pCrossover = getParameterAsDouble("p_crossover");
	int crossoverType = getParameterAsInt("crossover_type");
	return new UnbalancedCrossover(crossoverType, pCrossover);
    }

    /** Returns a list with all generator which should be used. */
    public List getGenerators() {
	List generators = new ArrayList();	
	if (getParameterAsBoolean("use_plus")) 
	    generators.add(new BasicArithmeticOperationGenerator(BasicArithmeticOperationGenerator.SUM));
	if (getParameterAsBoolean("use_diff")) 
	    generators.add(new BasicArithmeticOperationGenerator(BasicArithmeticOperationGenerator.DIFFERENCE));
	if (getParameterAsBoolean("use_mult")) 
	    generators.add(new BasicArithmeticOperationGenerator(BasicArithmeticOperationGenerator.PRODUCT));
	if (getParameterAsBoolean("use_div")) 
	    generators.add(new BasicArithmeticOperationGenerator(BasicArithmeticOperationGenerator.QUOTIENT));
	if (getParameterAsBoolean("reciprocal_value")) generators.add(new ReciprocalValueGenerator());
	return generators;
    }

    public List getParameterTypes() {
	List types = super.getParameterTypes();
	ParameterType type = new ParameterTypeDouble("p_crossover", 
						     "Probability for an individual to be selected for crossover.", 
						     0, 1, 0.5);
	type.setExpert(false);
	types.add(type);
	types.add(new ParameterTypeCategory("crossover_type", "Type of the crossover.", 
					    SelectionCrossover.CROSSOVER_TYPES, SelectionCrossover.UNIFORM));
	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));
	types.add(new ParameterTypeBoolean("reciprocal_value", "Generate reciprocal values.", false));
	return types;
    }
}