/*
* 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.features.selection;
import java.util.LinkedList;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.features.FeatureOperator;
import com.rapidminer.operator.features.Population;
import com.rapidminer.operator.features.PopulationOperator;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeBoolean;
import com.rapidminer.parameter.ParameterTypeCategory;
import com.rapidminer.parameter.ParameterTypeDouble;
import com.rapidminer.parameter.ParameterTypeInt;
/**
* Genetic algorithms are general purpose optimization / search algorithms that
* are suitable in case of no or little problem knowledge. <br/>
*
* A genetic algorithm works as follows
* <ol>
* <li>Generate an initial population consisting of
* <code>population_size</code> individuals. Each attribute is switched on
* with probability <code>p_initialize</code></li>
* <li>For all individuals in the population
* <ul>
* <li>Choose two individuals from the population and perform crossover with
* probability <code>p_crossover</code>. The type of crossover can be
* selected by <code>crossover_type</code>.</li>
* <li>Perform mutation, i.e. set used attributes to unused with probability
* <code>p_mutation</code> and vice versa. Other mutations may for example
* create new attributes etc. </li>
* </ul>
* </li>
* <li>Perform selection with a defined selection scheme.</li>
* <li>As long as the fitness improves, go to 2</li>
* </ol>
*
* If the example set contains value series attributes with blocknumbers, the
* whole block should be switched on and off.
*
* @author Ingo Mierswa
* @version $Id: AbstractGeneticAlgorithm.java,v 1.1 2006/04/14 07:47:17
* ingomierswa Exp $
*/
public abstract class AbstractGeneticAlgorithm extends FeatureOperator {
/** The parameter name for "Number of individuals per generation." */
public static final String PARAMETER_POPULATION_SIZE = "population_size";
/** The parameter name for "Number of generations after which to terminate the algorithm." */
public static final String PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS = "maximum_number_of_generations";
/** The parameter name for "Stop criterion: Stop after n generations without improval of the performance (-1: perform all generations)." */
public static final String PARAMETER_GENERATIONS_WITHOUT_IMPROVAL = "generations_without_improval";
/** The parameter name for "The selection scheme of this EA." */
public static final String PARAMETER_SELECTION_SCHEME = "selection_scheme";
/** The parameter name for "The fraction of the current population which should be used as tournament members (only tournament selection)." */
public static final String PARAMETER_TOURNAMENT_SIZE = "tournament_size";
/** The parameter name for "The scaling temperature (only Boltzmann selection)." */
public static final String PARAMETER_START_TEMPERATURE = "start_temperature";
/** The parameter name for "If set to true the selection pressure is increased to maximum during the complete optimization run (only Boltzmann and tournament selection)." */
public static final String PARAMETER_DYNAMIC_SELECTION_PRESSURE = "dynamic_selection_pressure";
/** The parameter name for "If set to true, the best individual of each generations is guaranteed to be selected for the next generation (elitist selection)." */
public static final String PARAMETER_KEEP_BEST_INDIVIDUAL = "keep_best_individual";
public static final String[] SELECTION_SCHEMES = { "uniform", "cut", "roulette wheel", "stochastic universal sampling", "Boltzmann", "rank", "tournament", "non dominated sorting" };
public static final int UNIFORM_SELECTION = 0;
public static final int CUT_SELECTION = 1;
public static final int ROULETTE_WHEEL = 2;
public static final int STOCHASTIC_UNIVERSAL = 3;
public static final int BOLTZMANN_SELECTION = 4;
public static final int RANK_SELECTION = 5;
public static final int TOURNAMENT_SELECTION = 6;
public static final int NON_DOMINATED_SORTING_SELECTION = 7;
/** The size of the population. */
private int numberOfIndividuals;
/** Maximum number of generations. */
private int maxGen;
/**
* Stop criterion: Stop after generationsWithoutImproval generations without
* an improval of the fitness.
*/
private int generationsWithoutImproval;
public AbstractGeneticAlgorithm(OperatorDescription description) {
super(description);
}
/**
* Returns an operator that performs the mutation. Can be overridden by
* subclasses.
*/
protected abstract PopulationOperator getMutationPopulationOperator(ExampleSet example) throws OperatorException;
/**
* Returns an operator that performs crossover. Can be overridden by
* subclasses.
*/
protected abstract PopulationOperator getCrossoverPopulationOperator(ExampleSet example) throws OperatorException;
/** Returns an empty list. */
protected List<PopulationOperator> getPreProcessingPopulationOperators(ExampleSet input) throws OperatorException {
return new LinkedList<PopulationOperator>();
}
/** Returns an empty list. */
protected List<PopulationOperator> getPostProcessingPopulationOperators(ExampleSet input) throws OperatorException {
return new LinkedList<PopulationOperator>();
}
/** Returns the list with pre eval pop ops. */
public final List<PopulationOperator> getPreEvaluationPopulationOperators(ExampleSet input) throws OperatorException {
// pre eval ops
List<PopulationOperator> preOp = new LinkedList<PopulationOperator>();
// crossover
PopulationOperator crossover = getCrossoverPopulationOperator(input);
if (crossover != null)
preOp.add(crossover);
// mutation
PopulationOperator mutation = getMutationPopulationOperator(input);
if (mutation != null)
preOp.add(mutation);
// other preevaluation ops
preOp.addAll(getPreProcessingPopulationOperators(input));
return preOp;
}
/** Returns the list with post eval pop ops. */
public final List<PopulationOperator> getPostEvaluationPopulationOperators(ExampleSet input) throws OperatorException {
// other post eval ops
List<PopulationOperator> postOp = new LinkedList<PopulationOperator>();
// selection
this.numberOfIndividuals = getParameterAsInt(PARAMETER_POPULATION_SIZE);
this.maxGen = getParameterAsInt(PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS);
this.generationsWithoutImproval = getParameterAsInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL);
if (this.generationsWithoutImproval < 1)
this.generationsWithoutImproval = this.maxGen;
boolean keepBest = getParameterAsBoolean(PARAMETER_KEEP_BEST_INDIVIDUAL);
boolean dynamicSelection = getParameterAsBoolean(PARAMETER_DYNAMIC_SELECTION_PRESSURE);
int selectionScheme = getParameterAsInt(PARAMETER_SELECTION_SCHEME);
switch (selectionScheme) {
case UNIFORM_SELECTION:
postOp.add(new UniformSelection(numberOfIndividuals, keepBest, getRandom()));
break;
case CUT_SELECTION:
postOp.add(new CutSelection(numberOfIndividuals));
break;
case ROULETTE_WHEEL:
postOp.add(new RouletteWheel(numberOfIndividuals, keepBest, getRandom()));
break;
case STOCHASTIC_UNIVERSAL:
postOp.add(new StochasticUniversalSampling(numberOfIndividuals, keepBest, getRandom()));
break;
case BOLTZMANN_SELECTION:
postOp.add(new BoltzmannSelection(numberOfIndividuals, getParameterAsDouble(PARAMETER_START_TEMPERATURE), this.maxGen, dynamicSelection, keepBest, getRandom()));
break;
case RANK_SELECTION:
postOp.add(new RankSelection(numberOfIndividuals, keepBest, getRandom()));
break;
case TOURNAMENT_SELECTION:
postOp.add(new TournamentSelection(numberOfIndividuals, getParameterAsDouble(PARAMETER_TOURNAMENT_SIZE), this.maxGen, dynamicSelection, keepBest, getRandom()));
break;
case NON_DOMINATED_SORTING_SELECTION:
postOp.add(new NonDominatedSortingSelection(numberOfIndividuals));
setCheckForMaximum(false); // disables the check for the
// maximal fitness
break;
default:
break;
}
postOp.addAll(getPostProcessingPopulationOperators(input));
return postOp;
}
/**
* Returns true if generation is >= maximum_number_of_generations or after
* generations_without_improval generations without improval.
*/
public boolean solutionGoodEnough(Population pop) {
return ((pop.getGeneration() >= maxGen) || (pop.getGenerationsWithoutImproval() >= generationsWithoutImproval));
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
ParameterType type = new ParameterTypeInt(PARAMETER_POPULATION_SIZE, "Number of individuals per generation.", 1, Integer.MAX_VALUE, 5);
type.setExpert(false);
types.add(type);
type = new ParameterTypeInt(PARAMETER_MAXIMUM_NUMBER_OF_GENERATIONS, "Number of generations after which to terminate the algorithm.", 1, Integer.MAX_VALUE, 30);
type.setExpert(false);
types.add(type);
types.add(new ParameterTypeInt(PARAMETER_GENERATIONS_WITHOUT_IMPROVAL, "Stop criterion: Stop after n generations without improval of the performance (-1: perform all generations).", -1, Integer.MAX_VALUE, -1));
types.add(new ParameterTypeCategory(PARAMETER_SELECTION_SCHEME, "The selection scheme of this EA.", SELECTION_SCHEMES, TOURNAMENT_SELECTION));
types.add(new ParameterTypeDouble(PARAMETER_TOURNAMENT_SIZE, "The fraction of the current population which should be used as tournament members (only tournament selection).", 0.0d, 1.0d, 0.25d));
types.add(new ParameterTypeDouble(PARAMETER_START_TEMPERATURE, "The scaling temperature (only Boltzmann selection).", 0.0d, Double.POSITIVE_INFINITY, 1.0d));
types.add(new ParameterTypeBoolean(PARAMETER_DYNAMIC_SELECTION_PRESSURE, "If set to true the selection pressure is increased to maximum during the complete optimization run (only Boltzmann and tournament selection).", true));
types.add(new ParameterTypeBoolean(PARAMETER_KEEP_BEST_INDIVIDUAL, "If set to true, the best individual of each generations is guaranteed to be selected for the next generation (elitist selection).", false));
return types;
}
}