public class multipopulation
extends java.lang.Object
implements java.lang.Cloneable
Modifier and Type | Method and Description |
---|---|
void |
Assessment(int i,
double[] valor) |
void |
Better(int nclases,
int[] better,
Int_t nbetter,
int[][] ind_clase) |
int |
ClassIndividual(int ind) |
java.lang.Object |
clone() |
void |
Code(int i,
genetcode code)
Returns in "code" the genetic code of the individual "i"
|
void |
CruceBasedLogical_Estacionario(double it)
Stationary crossover operator
|
int |
Elite() |
boolean |
Higher(double[] v1,
double[] v2,
int n) |
void |
InitialPopulation_4L(double[][] I,
int rango,
int n_items,
int[][] sujetos,
int tama,
int[] tama_dom)
Generates an initial population
|
double |
MeanFitness_Stationary() |
double |
MeanFitness_Stationary(Double_t min_f0,
Double_t max_f0,
Double_t min_f1,
Double_t max_f1,
Double_t min_f2,
Double_t max_f2) |
double |
MeanFitness() |
boolean |
Modified(int i) |
int |
N_individuals() |
int |
N_Val() |
void |
Paint() |
void |
Paint(int i) |
void |
PaintFitness_Stationary(int i) |
void |
PaintFitness(int i) |
void |
PaintFitnessInFile(int i) |
void |
PaintIndividual(int i) |
void |
Pasar(int i,
int j) |
void |
PutCode(int i,
genetcode code) |
void |
PutModified(int i) |
double |
Sort_4L(example_set E,
int n_clases,
int[] n_examples_per_class,
int[][] ind_clase,
double[] adap_reglas,
double[] peso_reglas,
int n_examples)
Sorts the individuals according to their fitness values
|
void |
Sort() |
void |
Sort(int n_clases,
int[] n_examples_per_class)
Sorts the individuals according to their fitness values
|
char[] |
Subpopulation_Binary(int subpoblacion,
int individuo,
Int_t tama)
Returns the binary subpopulation of the individual "individuo"
|
int[] |
Subpopulation_Integer(int subpoblacion,
int individuo,
Int_t tama)
Returns the integer subpopulation of the individual "individuo"
|
double[] |
Subpopulation_Real(int subpoblacion,
int individuo,
Int_t tama)
Returns the real subpopulation of the individual "individuo"
|
void |
Swap_bool(boolean[] v,
int i,
int j) |
void |
Swap_double(double[] v,
int i,
int j) |
void |
Swap_int(int[] v,
int i,
int j) |
void |
Swap(int i,
int j) |
void |
UniformMutation_Stationary()
Stationary uniform mutation operator
|
double |
ValueFitness(int i) |
double |
ValueFitness(int i,
int j) |
int |
ValueIndividual(int population,
int ind) |
public java.lang.Object clone()
clone
in class java.lang.Object
public void Swap(int i, int j)
public void Swap_int(int[] v, int i, int j)
public void Swap_double(double[] v, int i, int j)
public void Swap_bool(boolean[] v, int i, int j)
public void Sort()
public int ClassIndividual(int ind)
public int ValueIndividual(int population, int ind)
public char[] Subpopulation_Binary(int subpoblacion, int individuo, Int_t tama)
Returns the binary subpopulation of the individual "individuo"
subpoblacion
- int The selected subpopulationindividuo
- int The selected individualtama
- Int_t Size of the subpopulationpublic int[] Subpopulation_Integer(int subpoblacion, int individuo, Int_t tama)
Returns the integer subpopulation of the individual "individuo"
subpoblacion
- int The selected subpopulationindividuo
- int The selected individualtama
- Int_t Size of the subpopulationpublic double[] Subpopulation_Real(int subpoblacion, int individuo, Int_t tama)
Returns the real subpopulation of the individual "individuo"
subpoblacion
- int The selected subpopulationindividuo
- int The selected individualtama
- Int_t Size of the subpopulationpublic void Sort(int n_clases, int[] n_examples_per_class)
Sorts the individuals according to their fitness values
n_clases
- int The number of classesexamples_per_class
- int[] Vector containing the number of examples per classpublic void Better(int nclases, int[] better, Int_t nbetter, int[][] ind_clase)
public boolean Higher(double[] v1, double[] v2, int n)
public double Sort_4L(example_set E, int n_clases, int[] n_examples_per_class, int[][] ind_clase, double[] adap_reglas, double[] peso_reglas, int n_examples)
Sorts the individuals according to their fitness values
E
- example_set The set of examplesn_clases
- int The number of classesn_examples_per_class
- int[] Vector containing the number of examples per classind_clase
- int[][] Number of individuals per classadap_reglas
- double[] double[] Vector with the adaptation of rulespeso_reglas
- double[] Vector with the weight of rulesx_examples
- int Number of examplespublic void Paint(int i)
public void Paint()
public double MeanFitness()
public double MeanFitness_Stationary()
public double MeanFitness_Stationary(Double_t min_f0, Double_t max_f0, Double_t min_f1, Double_t max_f1, Double_t min_f2, Double_t max_f2)
public void PaintFitness(int i)
public void PaintFitnessInFile(int i) throws java.io.IOException
java.io.IOException
public void PaintFitness_Stationary(int i)
public void PaintIndividual(int i)
public int N_individuals()
public int Elite()
public int N_Val()
public void Code(int i, genetcode code)
Returns in "code" the genetic code of the individual "i"
i
- int The selected individualcode
- genetcode The structure storing the genetic codepublic void PutCode(int i, genetcode code)
public boolean Modified(int i)
public void PutModified(int i)
public void Assessment(int i, double[] valor)
public void InitialPopulation_4L(double[][] I, int rango, int n_items, int[][] sujetos, int tama, int[] tama_dom)
Generates an initial population
I
- double[][] Information measuresrango
- int Number of classesn_items
- int Number of examplessujetos
- int[][] The individuals found according to the examplestama
- int Number of antecedent variablestama_dom
- int[] Information related to the domain of each variablepublic void UniformMutation_Stationary()
Stationary uniform mutation operator
public void Pasar(int i, int j)
public void CruceBasedLogical_Estacionario(double it)
Stationary crossover operator
public double ValueFitness(int i)
public double ValueFitness(int i, int j)