edu.udo.cs.yale.operator.features.selection
Class GeneticAlgorithm

java.lang.Object
  edu.udo.cs.yale.operator.Operator
      edu.udo.cs.yale.operator.OperatorChain
          edu.udo.cs.yale.operator.features.FeatureOperator
              edu.udo.cs.yale.operator.features.selection.AbstractGeneticAlgorithm
                  edu.udo.cs.yale.operator.features.selection.GeneticAlgorithm

All Implemented Interfaces:

ConfigurationListener

public class GeneticAlgorithm
extends AbstractGeneticAlgorithm

A genetic algorithm for feature selection (mutation=switch features on and off, crossover=interchange used features). Selection is done by roulette wheel. Genetic algorithms are general purpose optimization / search algorithms that are suitable in case of no or little problem knowledge.
A genetic algorithm works as follows

1. Generate an initial population consisting of population_size individuals. Each attribute is switched on with probability p_initialize
2. For all individuals in the population
   • Perform mutation, i.e. set used attributes to unused with probability p_mutation and vice versa.
   • Choose two individuals from the population and perform crossover with probability p_crossover. The type of crossover can be selected by crossover_type.
3. Perform selection, map all individuals to sections on a roulette wheel whose size is proportional to the individual's fitness and draw population_size individuals at random according to their probability.
4. As long as the fitness improves, go to 2

If the example set contains value series attributes with blocknumbers, the whole block will be switched on and off.

Version:

$Id: GeneticAlgorithm.java,v 1.4 2006/08/03 14:39:29 ingomierswa Exp $

Author:

Ingo Mierswa, Simon Fischer

Field Summary

Fields inherited from class edu.udo.cs.yale.operator.features.selection.AbstractGeneticAlgorithm

BOLTZMANN_SELECTION, CUT_SELECTION, NON_DOMINATED_SORTING_SELECTION, RANK_SELECTION, ROULETTE_WHEEL, SELECTION_SCHEMES, STOCHASTIC_UNIVERSAL, TOURNAMENT_SELECTION, UNIFORM_SELECTION

Constructor Summary

GeneticAlgorithm(OperatorDescription description)

Method Summary

Population	createInitialPopulation(ExampleSet es) Sets up a population of given size and creates ExampleSets with randomly selected attributes (the probability to be switched on is controlled by pInitialize).
protected PopulationOperator	getCrossoverPopulationOperator(ExampleSet eSet) Returns an operator that performs crossover.
protected PopulationOperator	getMutationPopulationOperator(ExampleSet eSet) Returns an operator that performs the mutation.
java.util.List<ParameterType>	getParameterTypes() Returns a list of ParameterTypes describing the parameters of this operator.

Methods inherited from class edu.udo.cs.yale.operator.features.selection.AbstractGeneticAlgorithm

getPostEvaluationPopulationOperators, getPostProcessingPopulationOperators, getPreEvaluationPopulationOperators, getPreProcessingPopulationOperators, solutionGoodEnough

Methods inherited from class edu.udo.cs.yale.operator.features.FeatureOperator

apply, evaluate, evaluate, getCheckForMaximum, getInnerOperatorCondition, getInputClasses, getMaxNumberOfInnerOperators, getMinNumberOfInnerOperators, getNumberOfSteps, getOutputClasses, getRandom, setCheckForMaximum

Methods inherited from class edu.udo.cs.yale.operator.OperatorChain

addAddListener, addOperator, addOperator, checkDeprecations, checkIO, checkNumberOfInnerOperators, checkProperties, clearErrorList, clearStepCounter, cloneOperator, countStep, createExperimentTree, delete, experimentFinished, experimentStarts, getAllInnerOperators, getCurrentStep, getIndexOfOperator, getInnerOperatorForName, getInnerOperatorsXML, getNumberOfAllOperators, getNumberOfChildrensSteps, getNumberOfOperators, getOperator, getOperatorFromAll, getOperators, isEnabled, performAdditionalChecks, removeAddListener, removeOperator, setEnabled, setExperiment, shouldReturnInnerOutput

Methods inherited from class edu.udo.cs.yale.operator.Operator

addError, addValue, addWarning, apply, createExperimentTree, createFromXML, createMarkedExperimentTree, getAddOnlyAdditionalOutput, getApplyCount, getDeliveredOutputClasses, getDeprecationInfo, getDesiredInputClasses, getErrorList, getExperiment, getInput, getInput, getInput, getInputDescription, getIOContainerForInApplyLoopBreakpoint, getName, getOperatorClassName, getOperatorDescription, getParameter, getParameterAsBoolean, getParameterAsColor, getParameterAsDouble, getParameterAsFile, getParameterAsInt, getParameterAsString, getParameterList, getParameters, getParameterType, getParent, getStartTime, getStatus, getUserDescription, getValue, getValues, getXML, hasBreakpoint, hasBreakpoint, hasInput, inApplyLoop, isParameterSet, logMessage, register, remove, rename, resume, setBreakpoint, setInput, setListParameter, setOperatorParameters, setParameter, setParameters, setParent, setUserDescription, toString, writeXML

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

GeneticAlgorithm

public GeneticAlgorithm(OperatorDescription description)

Method Detail

createInitialPopulation

public Population createInitialPopulation(ExampleSet es)
                                   throws UndefinedParameterError

Sets up a population of given size and creates ExampleSets with randomly selected attributes (the probability to be switched on is controlled by pInitialize).

Specified by:

createInitialPopulation in class FeatureOperator

Throws:

UndefinedParameterError

getMutationPopulationOperator

protected PopulationOperator getMutationPopulationOperator(ExampleSet eSet)
                                                    throws UndefinedParameterError

Returns an operator that performs the mutation. Can be overridden by subclasses.

Specified by:

getMutationPopulationOperator in class AbstractGeneticAlgorithm

Throws:

UndefinedParameterError

getCrossoverPopulationOperator

protected PopulationOperator getCrossoverPopulationOperator(ExampleSet eSet)
                                                     throws UndefinedParameterError

Returns an operator that performs crossover. Can be overridden by subclasses.

Specified by:

getCrossoverPopulationOperator in class AbstractGeneticAlgorithm

Throws:

UndefinedParameterError

getParameterTypes

public java.util.List<ParameterType> getParameterTypes()

Description copied from class: Operator

Returns a list of ParameterTypes describing the parameters of this operator. The default implementation returns an empty list if no input objects can be retained and special parameters for those input objects which can be prevented from being consumed.

Overrides:

getParameterTypes in class AbstractGeneticAlgorithm