/***********************************************************************
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_Postprocess.MamGlobalTunDB;
import java.io.*;
import org.core.*;
import java.util.*;
import java.lang.Math;
class AG {
public double prob_cruce, prob_mutacion, a, b;
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 [] Old;
public Structure [] New;
public Structure [] C;
public Structure [] temp;
public TipoIntervalo [] intervalos;
public Adap fun_adap;
public AG (int n_poblacion, int n_etiq, double cruce, double mutacion, double valor_a, double valor_b, Adap funcion) {
int i;
long_poblacion = n_poblacion;
n_genes = 3 * n_etiq;
prob_mutacion = mutacion / (double) n_genes;
prob_cruce = cruce;
prob_mutacion = mutacion;
a = valor_a;
b = valor_b;
fun_adap = funcion;
Old = new Structure[long_poblacion];
New = new Structure[long_poblacion];
for (i=0; i<long_poblacion; i++) {
Old[i] = new Structure(n_genes);
New[i] = new Structure(n_genes);
}
sample = new int[long_poblacion];
intervalos = new TipoIntervalo[n_genes];
for (i=0; i<n_genes; i++) intervalos[i] = new TipoIntervalo();
C = new Structure[4];
for (i=0; i<4; i++) C[i] = new Structure(n_genes);
}
public void Intercambio () {
temp = Old;
Old = New;
New = temp;
}
/** Inicialization of the population */
public void Initialize (BaseR base_reglas, int n_variables) {
int i, j, k, repetida, pos_gen, et_act;
int [] n_etiquetas = new int[n_variables];
for (j=0; j<n_variables; j++) n_etiquetas[j] = 0;
New[0].n_e = 1;
pos_gen = 0;
for (j=0; j<n_variables; j++) {
for (i=0; i<base_reglas.n_reglas; i++) {
et_act = 0;
for (k=0; k<j; k++) et_act += n_etiquetas[k];
k = 3 * et_act;
repetida = 0;
while (k<pos_gen && repetida==0) {
if (base_reglas.BaseReglas[i][j].x0==New[0].Gene[k] && base_reglas.BaseReglas[i][j].x1==New[0].Gene[k+1] && base_reglas.BaseReglas[i][j].x3==New[0].Gene[k+2])
repetida = 1;
else {
k += 3;
et_act++;
}
}
base_reglas.b_reglas[i][j] = et_act;
if (repetida==0) {
n_etiquetas[j]++;
New[0].Gene[pos_gen] = base_reglas.BaseReglas[i][j].x0;
New[0].Gene[pos_gen+1] = base_reglas.BaseReglas[i][j].x1;
New[0].Gene[pos_gen+2] = base_reglas.BaseReglas[i][j].x3;
pos_gen += 3;
}
}
}
/* we generate the variation intervals for each gene */
for (i=0; i<n_genes; i+=3) {
intervalos[i].min = New[0].Gene[i]-(New[0].Gene[i+1]-New[0].Gene[i])/2.0;
intervalos[i].max = New[0].Gene[i]+(New[0].Gene[i+1]-New[0].Gene[i])/2.0;
intervalos[i+1].min = New[0].Gene[i+1]-(New[0].Gene[i+1]-New[0].Gene[i])/2.0;
intervalos[i+1].max = New[0].Gene[i+1]+(New[0].Gene[i+2]-New[0].Gene[i+1])/2.0;
intervalos[i+2].min = New[0].Gene[i+2]-(New[0].Gene[i+2]-New[0].Gene[i+1])/2.0;
intervalos[i+2].max = New[0].Gene[i+2]+(New[0].Gene[i+2]-New[0].Gene[i+1])/2.0;
}
/* the remainder of the population is randomly generated out of a the intervals */
for (i=1; i<long_poblacion; i++) {
for (j=0; j<n_genes; j++)
New[i].Gene[j] = intervalos[j].min+(intervalos[j].max-intervalos[j].min)*Randomize.Rand();
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-1);
}
/* we shuffle the selected chromosomes */
for (i=0; i<long_poblacion; i++) {
j = Randomize.RandintClosed (i, long_poblacion-1);
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;
}
}
private double T_producto_logico (double x ,double y) {
if (x<y) return (x);
else return (y);
}
private double S_suma_logica (double x ,double y) {
if (x>y) return (x);
else return (y);
}
private double Promedio1 (double x, double y, double p) {
return (p*x + (1-p)*y);
}
/** Max-Min-Aritmetical Crossover */
public void Max_Min_Crossover () {
int mom, dad, i, j, temp;
int [] indice = new int[4];
double temp1, temp2;
for (mom=0; mom<last; mom+=2) {
dad = mom + 1;
for (i=0; i<n_genes; i++) {
temp1 = New[mom].Gene[i];
temp2 = New[dad].Gene[i];
/* we obtain 4 offsprings: appling the t-norma, the
t-conorma and 2 the average function */
C[0].Gene[i] = T_producto_logico (temp1, temp2);
C[1].Gene[i] = S_suma_logica (temp1, temp2);
C[2].Gene[i] = Promedio1 (temp1, temp2, a);
C[3].Gene[i] = Promedio1 (temp1, temp2, 1.0 - a);
}
/* Evaluation of the 4 offsprings */
C[0].Perf = fun_adap.eval (C[0].Gene);
C[1].Perf = fun_adap.eval (C[1].Gene);
C[2].Perf = fun_adap.eval (C[2].Gene);
C[3].Perf = fun_adap.eval (C[3].Gene);
/* we order the offsprings by means of the bubble method */
for (i=0; i<4; i++) indice[i]=i;
for (i=0; i<4; i++)
for (j=0; j<3-i; j++)
if (C[indice[j+1]].Perf < C[indice[j]].Perf) {
temp = indice[j];
indice[j] = indice[j+1];
indice[j+1] = temp;
}
for (i=0; i<n_genes; i++) {
New[mom].Gene[i] = C[indice[0]].Gene[i];
New[dad].Gene[i] = C[indice[1]].Gene[i];
}
/* we update the fitness of the offsprings */
New[mom].Perf = C[indice[0]].Perf;
New[dad].Perf = C[indice[1]].Perf;
New[mom].n_e = 0;
New[dad].n_e = 0;
Trials += 2;
}
}
private double delta (long t, double y, long n_generaciones) {
double r, potencia, subtotal, sub;
r = Randomize.Rand();
sub = 1.0 - (double)t / (double)n_generaciones;
potencia = Math.pow (sub, (double)b);
subtotal = Math.pow (r, potencia);
return (y * (1.0 - subtotal));
}
/** Mutation Non Uniform */
public void Mutacion_No_Uniforme (long Gen, long n_generaciones) {
int posiciones, i, j;
double nval, 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 (Randomize.Rand()<0.5)
nval = New[i].Gene[j] + delta (Gen, intervalos[j].max-New[i].Gene[j], n_generaciones);
else
nval=New[i].Gene[j] - delta (Gen, New[i].Gene[j]-intervalos[j].min, n_generaciones);
New[i].Gene[j]=nval;
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));
Mu_next++;
}
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 double [] solucion () {
return (New[Best_guy].Gene);
}
/** Returns the fitness of the best solution */
public double solucion_ec () {
return (New[Best_guy].Perf);
}
}