org.dllearner.core.configurators
Class GPConfigurator

java.lang.Object
  org.dllearner.core.configurators.GPConfigurator

All Implemented Interfaces:

Configurator

public class GPConfigurator
extends Object
implements Configurator

automatically generated, do not edit manually. run org.dllearner.scripts.ConfigJavaGenerator to update

Constructor Summary

GPConfigurator(GP gP)

Method Summary

boolean	getAdc() adc whether to use automatically defined concept (this invents new helper concepts, but enlarges the search space.
String	getAlgorithmType() algorithmType algorithm type.
double	getCrossoverProbability() crossoverProbability crossover probability.
boolean	getElitism() elitism specifies whether to use elitism in selection.
int	getGenerations() generations number of generations (only valid if a fixed number of generations is used).
static GP	getGP(AbstractLearningProblem learningProblem, AbstractReasonerComponent reasoningService)
double	getHillClimbingProbability() hillClimbingProbability hill climbing probability.
int	getInitMaxDepth() initMaxDepth maximum depth to use when creating the initial population.
int	getInitMinDepth() initMinDepth minimum depth to use when creating the initial population.
int	getMaxConceptLength() maxConceptLength maximum concept length (higher length means lowest possible fitness).
double	getMutationProbability() mutationProbability mutation probability.
int	getNumberOfIndividuals() numberOfIndividuals number of individuals.
int	getNumberOfSelectedIndividuals() numberOfSelectedIndividuals number of selected individuals.
int	getPostConvergenceGenerations() postConvergenceGenerations number of generations after which to stop if no improvement wrt. the best solution has been achieved.
double	getRefinementProbability() refinementProbability refinement operator probability (values higher than 0 turn this into a hybrid GP algorithm - see publication).
String	getSelectionType() selectionType selection type.
int	getTournamentSize() tournamentSize tournament size (applies only to tournament selection).
boolean	getUseFixedNumberOfGenerations() useFixedNumberOfGenerations specifies whether to use a fixed number of generations.
boolean	isReinitNecessary() true, if this component needs reinitializsation.
void	setAdc(boolean adc)
void	setAlgorithmType(String algorithmType)
void	setCrossoverProbability(double crossoverProbability)
void	setElitism(boolean elitism)
void	setGenerations(int generations)
void	setHillClimbingProbability(double hillClimbingProbability)
void	setInitMaxDepth(int initMaxDepth)
void	setInitMinDepth(int initMinDepth)
void	setMaxConceptLength(int maxConceptLength)
void	setMutationProbability(double mutationProbability)
void	setNumberOfIndividuals(int numberOfIndividuals)
void	setNumberOfSelectedIndividuals(int numberOfSelectedIndividuals)
void	setPostConvergenceGenerations(int postConvergenceGenerations)
void	setRefinementProbability(double refinementProbability)
void	setSelectionType(String selectionType)
void	setTournamentSize(int tournamentSize)
void	setUseFixedNumberOfGenerations(boolean useFixedNumberOfGenerations)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

GPConfigurator

public GPConfigurator(GP gP)

Parameters:

gP - see GP

Method Detail

getGP

public static GP getGP(AbstractLearningProblem learningProblem,
                       AbstractReasonerComponent reasoningService)
                throws LearningProblemUnsupportedException

Parameters:

reasoningService - see reasoningService

learningProblem - see learningProblem

Returns:

GP

Throws:

LearningProblemUnsupportedException - see

getSelectionType

public String getSelectionType()

selectionType selection type. mandatory: false| reinit necessary: true default value: rankSelection

Returns:

String

getTournamentSize

public int getTournamentSize()

tournamentSize tournament size (applies only to tournament selection). mandatory: false| reinit necessary: true default value: 3

Returns:

int

getElitism

public boolean getElitism()

elitism specifies whether to use elitism in selection. mandatory: false| reinit necessary: true default value: true

Returns:

boolean

getAlgorithmType

public String getAlgorithmType()

algorithmType algorithm type. mandatory: false| reinit necessary: true default value: steadyState

Returns:

String

getMutationProbability

public double getMutationProbability()

mutationProbability mutation probability. mandatory: false| reinit necessary: true default value: 0.03

Returns:

double

getCrossoverProbability

public double getCrossoverProbability()

crossoverProbability crossover probability. mandatory: false| reinit necessary: true default value: 0.95

Returns:

double

getHillClimbingProbability

public double getHillClimbingProbability()

hillClimbingProbability hill climbing probability. mandatory: false| reinit necessary: true default value: 0.0

Returns:

double

getRefinementProbability

public double getRefinementProbability()

refinementProbability refinement operator probability (values higher than 0 turn this into a hybrid GP algorithm - see publication). mandatory: false| reinit necessary: true default value: 0.0

Returns:

double

getNumberOfIndividuals

public int getNumberOfIndividuals()

numberOfIndividuals number of individuals. mandatory: false| reinit necessary: true default value: 100

Returns:

int

getNumberOfSelectedIndividuals

public int getNumberOfSelectedIndividuals()

numberOfSelectedIndividuals number of selected individuals. mandatory: false| reinit necessary: true default value: 92

Returns:

int

getUseFixedNumberOfGenerations

public boolean getUseFixedNumberOfGenerations()

useFixedNumberOfGenerations specifies whether to use a fixed number of generations. mandatory: false| reinit necessary: true default value: false

Returns:

boolean

getGenerations

public int getGenerations()

generations number of generations (only valid if a fixed number of generations is used). mandatory: false| reinit necessary: true default value: 20

Returns:

int

getPostConvergenceGenerations

public int getPostConvergenceGenerations()

postConvergenceGenerations number of generations after which to stop if no improvement wrt. the best solution has been achieved. mandatory: false| reinit necessary: true default value: 50

Returns:

int

getAdc

public boolean getAdc()

adc whether to use automatically defined concept (this invents new helper concepts, but enlarges the search space. mandatory: false| reinit necessary: true default value: false

Returns:

boolean

getInitMinDepth

public int getInitMinDepth()

initMinDepth minimum depth to use when creating the initial population. mandatory: false| reinit necessary: true default value: 4

Returns:

int

getInitMaxDepth

public int getInitMaxDepth()

initMaxDepth maximum depth to use when creating the initial population. mandatory: false| reinit necessary: true default value: 6

Returns:

int

getMaxConceptLength

public int getMaxConceptLength()

maxConceptLength maximum concept length (higher length means lowest possible fitness). mandatory: false| reinit necessary: true default value: 75

Returns:

int

setSelectionType

public void setSelectionType(String selectionType)

Parameters:

selectionType - selection type. mandatory: false| reinit necessary: true default value: rankSelection

setTournamentSize

public void setTournamentSize(int tournamentSize)

Parameters:

tournamentSize - tournament size (applies only to tournament selection). mandatory: false| reinit necessary: true default value: 3

setElitism

public void setElitism(boolean elitism)

Parameters:

elitism - specifies whether to use elitism in selection. mandatory: false| reinit necessary: true default value: true

setAlgorithmType

public void setAlgorithmType(String algorithmType)

Parameters:

algorithmType - algorithm type. mandatory: false| reinit necessary: true default value: steadyState

setMutationProbability

public void setMutationProbability(double mutationProbability)

Parameters:

mutationProbability - mutation probability. mandatory: false| reinit necessary: true default value: 0.03

setCrossoverProbability

public void setCrossoverProbability(double crossoverProbability)

Parameters:

crossoverProbability - crossover probability. mandatory: false| reinit necessary: true default value: 0.95

setHillClimbingProbability

public void setHillClimbingProbability(double hillClimbingProbability)

Parameters:

hillClimbingProbability - hill climbing probability. mandatory: false| reinit necessary: true default value: 0.0

setRefinementProbability

public void setRefinementProbability(double refinementProbability)

Parameters:

refinementProbability - refinement operator probability (values higher than 0 turn this into a hybrid GP algorithm - see publication). mandatory: false| reinit necessary: true default value: 0.0

setNumberOfIndividuals

public void setNumberOfIndividuals(int numberOfIndividuals)

Parameters:

numberOfIndividuals - number of individuals. mandatory: false| reinit necessary: true default value: 100

setNumberOfSelectedIndividuals

public void setNumberOfSelectedIndividuals(int numberOfSelectedIndividuals)

Parameters:

numberOfSelectedIndividuals - number of selected individuals. mandatory: false| reinit necessary: true default value: 92

setUseFixedNumberOfGenerations

public void setUseFixedNumberOfGenerations(boolean useFixedNumberOfGenerations)

Parameters:

useFixedNumberOfGenerations - specifies whether to use a fixed number of generations. mandatory: false| reinit necessary: true default value: false

setGenerations

public void setGenerations(int generations)

Parameters:

generations - number of generations (only valid if a fixed number of generations is used). mandatory: false| reinit necessary: true default value: 20

setPostConvergenceGenerations

public void setPostConvergenceGenerations(int postConvergenceGenerations)

Parameters:

postConvergenceGenerations - number of generations after which to stop if no improvement wrt. the best solution has been achieved. mandatory: false| reinit necessary: true default value: 50

setAdc

public void setAdc(boolean adc)

Parameters:

adc - whether to use automatically defined concept (this invents new helper concepts, but enlarges the search space. mandatory: false| reinit necessary: true default value: false

setInitMinDepth

public void setInitMinDepth(int initMinDepth)

Parameters:

initMinDepth - minimum depth to use when creating the initial population. mandatory: false| reinit necessary: true default value: 4

setInitMaxDepth

public void setInitMaxDepth(int initMaxDepth)

Parameters:

initMaxDepth - maximum depth to use when creating the initial population. mandatory: false| reinit necessary: true default value: 6

setMaxConceptLength

public void setMaxConceptLength(int maxConceptLength)

Parameters:

maxConceptLength - maximum concept length (higher length means lowest possible fitness). mandatory: false| reinit necessary: true default value: 75

isReinitNecessary

public boolean isReinitNecessary()

true, if this component needs reinitializsation.

Returns:

boolean