/***********************************************************************
This file is part of KEEL-software, the Data Mining tool for regression,
classification, clustering, pattern mining and so on.
Copyright (C) 2004-2010
F. Herrera (herrera@decsai.ugr.es)
L. S�nchez (luciano@uniovi.es)
J. Alcal�-Fdez (jalcala@decsai.ugr.es)
S. Garc�a (sglopez@ujaen.es)
A. Fern�ndez (alberto.fernandez@ujaen.es)
J. Luengo (julianlm@decsai.ugr.es)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see http://www.gnu.org/licenses/
**********************************************************************/
package keel.Algorithms.RE_SL_Methods.mogulIRL;
/**
* <p>Title: </p>
*
* <p>Description: </p>
*
* <p>Copyright: Copyright (c) 2007</p>
*
* <p>Company: </p>
*
* @author not attributable
* @version 1.0
*/
import org.core.*;
class AG_Sel {
public double prob_cruce, prob_mutacion;
public int Mu_next, Trials;
public double Best_current_perf;
public int Best_guy;
public int long_poblacion, n_genes;
public int [] sample;
public int last;
public Structure_Sel [] Old;
public Structure_Sel [] New;
public Structure_Sel [] temp;
public Adap_Sel fun_adap;
public AG_Sel (int n_poblacion, int genes, double cruce, double mutacion, Adap_Sel funcion) {
int i;
long_poblacion = n_poblacion;
n_genes = genes;
prob_cruce = cruce;
prob_mutacion = mutacion / (double) n_genes;
fun_adap = funcion;
sample = new int[long_poblacion];
Old = new Structure_Sel[long_poblacion];
New = new Structure_Sel[long_poblacion];
for (i=0; i<long_poblacion; i++) {
Old[i] = new Structure_Sel(n_genes);
New[i] = new Structure_Sel(n_genes);
}
}
public void Intercambio () {
temp=Old;
Old=New;
New=temp;
}
/** Inicialization of the population */
public void Initialize () {
int i, j;
last = (int) ((prob_cruce * long_poblacion) - 0.5);
Trials = 0;
if (prob_mutacion < 1.0) {
Mu_next = (int) (Math.log(Randomize.Rand()) / Math.log(1.0 - prob_mutacion));
Mu_next++;
}
else Mu_next = 1;
for (j=0; j<n_genes; j++) New[0].Gene[j] = '1';
New[0].n_e = 1;
for (i=1; i < long_poblacion; i++) {
for (j=0; j<n_genes; j++)
if (Randomize.RandintClosed(0,2) == 0) New[i].Gene[j] = '0';
else New[i].Gene[j] = '1';
New[i].n_e = 1;
}
}
/* Selection based on the Baker's Estocastic Universal Sampling */
void Select() {
double expected, factor, perf, ptr, sum, rank_max, rank_min;
int i, j, k, best, temp;
rank_min = 0.75;
/* we assign the ranking to each element:
The best: ranking = long_poblacion-1
The worse: ranking = 0 */
for (i=0; i<long_poblacion; i++) Old[i].n_e = 0;
/* we look for the best ordered non element */
for (i=0; i<long_poblacion-1; i++) {
best = -1;
perf = 0.0;
for (j=0; j<long_poblacion; j++) {
if ((Old[j].n_e==0) && (best==-1 || Old[j].Perf < perf)) {
perf = Old[j].Perf;
best = j;
}
}
/* we assign the ranking */
Old[best].n_e = long_poblacion - 1 - i;
}
/* we normalize the ranking */
rank_max = 2.0 - rank_min;
factor = (rank_max-rank_min)/(double)(long_poblacion-1);
/* we assign the number of replicas of each chormosome according to the select probability */
k = 0;
ptr = Randomize.Rand ();
for (sum = i = 0; i<long_poblacion; i++) {
expected = rank_min + Old[i].n_e * factor;
for (sum += expected; sum>=ptr; ptr++) sample[k++] = i;
}
/* we complete the population if necessary */
if (k != long_poblacion) {
for (;k<long_poblacion; k++) sample[k] = Randomize.RandintClosed (0, long_poblacion);
}
/* we shuffle the selected chromosomes */
for (i=0; i<long_poblacion; i++) {
j = Randomize.RandintClosed (i, long_poblacion);
temp = sample[j];
sample[j] = sample[i];
sample[i] = temp;
}
/* we create the new population */
for (i=0; i<long_poblacion; i++) {
k = sample[i];
for (j=0; j<n_genes; j++) New[i].Gene[j] = Old[k].Gene[j];
New[i].Perf = Old[k].Perf;
New[i].n_e = 0;
}
}
/* Multipoint Crossover */
void Cruce_Multipunto () {
int mom, dad, xpoint1, xpoint2, i, j;
char temp;
for (mom=0; mom<last; mom+=2) {
dad = mom+1;
/* we generate the 2 crossing points */
xpoint1 = Randomize.RandintClosed (0,n_genes);
if (xpoint1 != n_genes-1)
xpoint2 = Randomize.RandintClosed (xpoint1 + 1, n_genes);
else xpoint2 = n_genes - 1;
/* we cross the individuals between xpoint1 and xpoint2 */
for (i=xpoint1; i<=xpoint2; i++) {
temp = New[mom].Gene[i];
New[mom].Gene[i] = New[dad].Gene[i];
New[dad].Gene[i] = temp;
}
New[mom].n_e = 1;
New[dad].n_e = 1;
}
}
/* Operador de Mutacion Uniforme */
void Mutacion_Uniforme () {
int posiciones, i, j;
double m;
posiciones = n_genes * long_poblacion;
if (prob_mutacion>0) {
while (Mu_next<posiciones) {
/* we determinate the chromosome and the gene */
i = Mu_next / n_genes;
j = Mu_next % n_genes;
/* we mutate the gene */
if (New[i].Gene[j]=='0') New[i].Gene[j]='1';
else New[i].Gene[j]='0';
New[i].n_e=1;
/* we calculate the next position */
if (prob_mutacion<1) {
m = Randomize.Rand();
Mu_next += (int) (Math.log(m) / Math.log(1.0 - prob_mutacion)) + 1;
}
else Mu_next += 1;
}
}
Mu_next -= posiciones;
}
/** Fitness Function */
void Evaluate() {
double performance;
int i, j;
for (i=0; i<long_poblacion; i++) {
/* if the chromosome aren't evaluated, it's evaluate */
if (New[i].n_e == 1) {
New[i].Perf = fun_adap.eval (New[i].Gene);
performance = New[i].Perf;
New[i].n_e = 0;
Trials++; /* we increment the number of evaluated chromosomes */
}
else performance = New[i].Perf;
/* we calculate the position of the best individual */
if (i == 0) {
Best_current_perf = performance;
Best_guy = 0;
}
else if (performance < Best_current_perf) {
Best_current_perf = performance;
Best_guy = i;
}
}
}
/* Elitist selection */
void Elitist () {
int i, k, found;
/* if the best individual of the old population aren't in the new population, we remplace the last individual for this */
for (i=0, found=0; i<long_poblacion && (found==0); i++)
for (k=0, found=1; (k<n_genes) && (found==1); k++)
if (New[i].Gene[k] != Old[Best_guy].Gene[k]) found = 0;
if (found == 0) {
for (k=0; k<n_genes; k++)
New[long_poblacion-1].Gene[k] = Old[Best_guy].Gene[k];
New[long_poblacion-1].Perf = Old[Best_guy].Perf;
New[long_poblacion-1].n_e = 0;
}
}
/** Returns the best solution*/
public char [] solucion () {
return (New[Best_guy].Gene);
}
/** Returns the fitness of the best solution */
public double solucion_ec () {
return (New[Best_guy].Perf);
}
}