edu.udo.cs.yale.operator.features.construction
Class AbstractGeneratingGeneticAlgorithm

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.construction.AbstractGeneratingGeneticAlgorithm

All Implemented Interfaces:

ConfigurationListener

Direct Known Subclasses:

GeneratingGeneticAlgorithm, YAGGA

public abstract class AbstractGeneratingGeneticAlgorithm
extends AbstractGeneticAlgorithm

In contrast to its superclass GeneticAlgorithm, the 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.
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 http://yale.cs.uni-dortmund.de

Version:

$Id: AbstractGeneratingGeneticAlgorithm.java,v 1.1 2006/04/14 07:47:17 ingomierswa Exp $

Author:

Ingo Mierswa

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

AbstractGeneratingGeneticAlgorithm(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 exampleSet) Returns an UnbalancedCrossover.
protected abstract PopulationOperator	getGeneratingPopulationOperator(ExampleSet exampleSet) Returns a specialized generating mutation, e.g. a AttributeGenerator.
java.util.List<FeatureGenerator>	getGenerators() Returns a list with all generator which should be used.
java.util.List<ParameterType>	getParameterTypes() Returns a list of ParameterTypes describing the parameters of this operator.
protected java.util.List<PopulationOperator>	getPreProcessingPopulationOperators(ExampleSet exampleSet) Returns an empty list.

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

getMutationPopulationOperator, getPostEvaluationPopulationOperators, getPostProcessingPopulationOperators, getPreEvaluationPopulationOperators, 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

AbstractGeneratingGeneticAlgorithm

public AbstractGeneratingGeneticAlgorithm(OperatorDescription description)

Method Detail

getGeneratingPopulationOperator

protected abstract PopulationOperator getGeneratingPopulationOperator(ExampleSet exampleSet)
                                                               throws OperatorException

Returns a specialized generating mutation, e.g. a AttributeGenerator.

Throws:

OperatorException

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

getPreProcessingPopulationOperators

protected java.util.List<PopulationOperator> getPreProcessingPopulationOperators(ExampleSet exampleSet)
                                                                          throws OperatorException

Description copied from class: AbstractGeneticAlgorithm

Returns an empty list.

Overrides:

getPreProcessingPopulationOperators in class AbstractGeneticAlgorithm

Throws:

OperatorException

getCrossoverPopulationOperator

protected PopulationOperator getCrossoverPopulationOperator(ExampleSet exampleSet)
                                                     throws UndefinedParameterError

Returns an UnbalancedCrossover.

Specified by:

getCrossoverPopulationOperator in class AbstractGeneticAlgorithm

Throws:

UndefinedParameterError

getGenerators

public java.util.List<FeatureGenerator> getGenerators()

Returns a list with all generator which should be used.

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