/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.meta;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.performance.PerformanceVector;
import com.rapidminer.tools.LoggingHandler;
import com.rapidminer.tools.RandomGenerator;
import com.rapidminer.tools.math.optimization.ec.es.ESOptimization;
import com.rapidminer.tools.math.optimization.ec.es.Individual;
/**
* Evolutionary Strategy approach for an evolutionary parameter optimization.
*
* @author Ingo Mierswa
* @version $Id: ESParameterOptimization.java,v 1.3 2008/05/09 19:22:38 ingomierswa Exp $
*/
public class ESParameterOptimization extends ESOptimization {
/** The parent operator. Used for fitness evaluation. */
private EvolutionaryParameterOptimizationOperator operator;
/** Creates a new evolutionary SVM optimization. */
public ESParameterOptimization(EvolutionaryParameterOptimizationOperator operator,
int individualSize,
int initType, // start population creation type para
int maxIterations, int generationsWithoutImprovement, int popSize, // GA paras
int selectionType, double tournamentFraction, boolean keepBest, // selection paras
int mutationType, // type of mutation
double crossoverProb,
boolean showPlot, RandomGenerator random, LoggingHandler logging) {
super(0, 1, popSize, individualSize, initType, maxIterations, generationsWithoutImprovement, selectionType, tournamentFraction, keepBest, mutationType, crossoverProb, showPlot, random, logging);
this.operator = operator;
}
public PerformanceVector evaluateIndividual(Individual individual) throws OperatorException {
return operator.setParametersAndEvaluate(individual);
}
public void nextIteration() throws OperatorException {
this.operator.inApplyLoop();
}
}