keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD

Class Genetic

java.lang.Object

keel.Algorithms.Subgroup_Discovery.NMEEFSD.NMEEFSD.Genetic

public class Genetic
extends java.lang.Object

Methods to define the genetic algorithm and to apply operators and reproduction schema

Since:

JDK1.5

Version:

1.0

Author:

Written by Cristóbal J. Carmona (University of Jaen) 11/08/2008

Constructor Summary

Constructors

Constructor and Description
Genetic()

Method Summary

Modifier and Type	Method and Description
void	CrossMultipoint(TableVar Variables, int dad, int mom, int contador, int neje) Cross operator for the genetic algorithm
Population	GeneticAlgorithm(TableVar Variables, TableDat Examples, java.lang.String nFile) Composes the genetic algorithm applying the operators
java.lang.String	getDiversity() Gets the type of diversity of the algorithm
int	getGen() Gets the value of a gene
int	getLengthPopulation() Gets the lenght of the population
float	getMinCnf() Gets the minimum confidence
int	getNEval() Gets the number of evalutions of the algorithms
java.lang.String	getNObjectives(int pos) Gets the name of the objective
int	getNumObjectives() Gets the number of objectives
float	getPorcCob() Gets the percentage of biased initialisation in the re-initialisation based on coverage
float	getProbCross() Gets the cross probability
float	getProbMutation() Gets the mutation probability
java.lang.String	getReInitCob() Gets if the algorithm uses re-initialisation based on coverage
java.lang.String	getRulesRep() Gets the rules representation of the algorithm
java.lang.String	getStrictDominance() Gets if the algorithm considers strict dominance
int	getTrials() Gets the number of trials in the algorithm
void	iniNObjectives() Initialises the structure for the name of the objectives
void	JoinTemp(int neje) Joins two populations
void	Mutation(TableVar Variables, int pos) Mutates an individual
java.util.Vector	RemoveRepeatedCAN(Population original) Eliminates the repeated individuals for canonical representation
java.util.Vector	RemoveRepeatedDNF(Population original, TableVar Variables) Eliminates the repeated individuals for DNF representation
int	Select() Applies the selection schema of the genetic algorithm.
void	setDiversity(java.lang.String value) Sets the type of diversity of the algorithm
void	setGen(int value) Sets the value of a gene
void	setLengthPopulation(int value) Sets the lenght of the population
void	setMinCnf(float value) Sets the minimum confidence
void	setNEval(int value) Sets the number of evaluations of the algorithm
void	setNObjectives(int pos, java.lang.String value) Sets the name of an objective
void	setNumObjectives(int nobj) Sets the number of objectives
void	setPorcCob(float value) Sets the percentage of biased initialisation in the re-initialisation based on coverage
void	setProbCross(float value) Sets the cross probability in the algorithm
void	setProbMutation(float value) Sets the mutation probability
void	setReInitCob(java.lang.String value) Sets the value of re-initialisation based on coverage
void	setRulesRep(java.lang.String value) Sets the rules representation of the algorithm
void	setStrictDominance(java.lang.String value) Sets if the algorithm considers strict dominance
void	setTrials(int value) Sets the number of trials in the algorithm

Methods inherited from class java.lang.Object

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

Constructor Detail

Genetic

public Genetic()

Method Detail

setNumObjectives

public void setNumObjectives(int nobj)

Sets the number of objectives

Parameters:

nobj - Number of objectives

getNumObjectives

public int getNumObjectives()

Gets the number of objectives

Returns:

Number of objectives

setNObjectives

public void setNObjectives(int pos,
                           java.lang.String value)

Sets the name of an objective

Parameters:

pos - Position of the objective

value - Name of the objective

iniNObjectives

public void iniNObjectives()

Initialises the structure for the name of the objectives

getNObjectives

public java.lang.String getNObjectives(int pos)

Gets the name of the objective

Parameters:

pos - Position of the objective

Returns:

Name of the objective

setLengthPopulation

public void setLengthPopulation(int value)

Sets the lenght of the population

Parameters:

value - Lenght of the population

getLengthPopulation

public int getLengthPopulation()

Gets the lenght of the population

Returns:

Lenght of the population

setNEval

public void setNEval(int value)

Sets the number of evaluations of the algorithm

Parameters:

value - Number of evaluations

getNEval

public int getNEval()

Gets the number of evalutions of the algorithms

Returns:

Number of evaluations

setProbCross

public void setProbCross(float value)

Sets the cross probability in the algorithm

Parameters:

value - Cross probability

getProbCross

public float getProbCross()

Gets the cross probability

Returns:

Cross probability

setProbMutation

public void setProbMutation(float value)

Sets the mutation probability

Parameters:

value - Mutation probability

getProbMutation

public float getProbMutation()

Gets the mutation probability

Returns:

Mutation probability

setGen

public void setGen(int value)

Sets the value of a gene

Parameters:

value - Value of the gene

getGen

public int getGen()

Gets the value of a gene

Returns:

Value of the gene

setTrials

public void setTrials(int value)

Sets the number of trials in the algorithm

Parameters:

value - Number of trials

getTrials

public int getTrials()

Gets the number of trials in the algorithm

Returns:

Number of trials

getDiversity

public java.lang.String getDiversity()

Gets the type of diversity of the algorithm

Returns:

Type of diversity

setDiversity

public void setDiversity(java.lang.String value)

Sets the type of diversity of the algorithm

Parameters:

value - Type of diversity

getReInitCob

public java.lang.String getReInitCob()

Gets if the algorithm uses re-initialisation based on coverage

Returns:

The uses of re-initialisation based on coverage

setReInitCob

public void setReInitCob(java.lang.String value)

Sets the value of re-initialisation based on coverage

Parameters:

value - Value of the re-inisitalisation based on coverage

getPorcCob

public float getPorcCob()

Gets the percentage of biased initialisation in the re-initialisation based on coverage

Returns:

Percentage of biases

setPorcCob

public void setPorcCob(float value)

Sets the percentage of biased initialisation in the re-initialisation based on coverage

Parameters:

value - Value of the percentage

getMinCnf

public float getMinCnf()

Gets the minimum confidence

Returns:

Minimum confidence

setMinCnf

public void setMinCnf(float value)

Sets the minimum confidence

Parameters:

value - Minimum confidence

getRulesRep

public java.lang.String getRulesRep()

Gets the rules representation of the algorithm

Returns:

Representation of the rules

setRulesRep

public void setRulesRep(java.lang.String value)

Sets the rules representation of the algorithm

Parameters:

value - Representation of the rule

getStrictDominance

public java.lang.String getStrictDominance()

Gets if the algorithm considers strict dominance

Returns:

The value of strict dominance

setStrictDominance

public void setStrictDominance(java.lang.String value)

Sets if the algorithm considers strict dominance

Parameters:

value - The value of strict dominance

JoinTemp

public void JoinTemp(int neje)

Joins two populations

Parameters:

neje - Number of examples

Select

public int Select()

Applies the selection schema of the genetic algorithm. Binary tournament selection from elite to inter

Returns:

Position of the individual selected

CrossMultipoint

public void CrossMultipoint(TableVar Variables,
                            int dad,
                            int mom,
                            int contador,
                            int neje)

Cross operator for the genetic algorithm

Parameters:

Variables - Variables structure

dad - Position of the daddy

mom - Position of the mummy

contador - Position to insert the son

neje - Number of examples

Mutation

public void Mutation(TableVar Variables,
                     int pos)

Mutates an individual

Parameters:

Variables - Variables structure

pos - Position of the individual to mutate

GeneticAlgorithm

public Population GeneticAlgorithm(TableVar Variables,
                                   TableDat Examples,
                                   java.lang.String nFile)

Composes the genetic algorithm applying the operators

Parameters:

Variables - Variables structure

Examples - Examples structure

nFile - Fichero to write the process

Returns:

Final Pareto population

RemoveRepeatedCAN

public java.util.Vector RemoveRepeatedCAN(Population original)

Eliminates the repeated individuals for canonical representation

Parameters:

original - A population

Returns:

A vector which marks the inviduals repeated

RemoveRepeatedDNF

public java.util.Vector RemoveRepeatedDNF(Population original,
                                          TableVar Variables)

Eliminates the repeated individuals for DNF representation

Parameters:

original - A population

Variables - Variables structure

Returns:

A vector which marks the inviduals repeated