/***********************************************************************
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.TSKLocalTunRules;
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, porc_pob_ee;
public int Best_guy;
public int long_poblacion, n_genes, primer_gen_C2;
public int n_gen_ee;
public static double S_sigma_consecuentes = 0.00001;
public static double c = 0.9; /*0.817*/
public int [] sample;
public int [] indices_ordenacion;
public int last;
public Structure [] Old;
public Structure [] New;
public Structure [] C;
public Structure [] temp;
public Structure [] YaExplotados;
public Structure Hijo;
public TipoIntervalo [] intervalos;
private TipoIntervalo intervalo_mut;
public Adap fun_adap;
public BaseR base_reglas;
public AG (int n_poblacion, double cruce, double mutacion, double valor_a, double valor_b, double porc_pob_ee11, int gen_ee, Adap funcion, BaseR base) {
int i;
base_reglas = base;
fun_adap = funcion;
long_poblacion = n_poblacion;
prob_cruce = cruce;
prob_mutacion = mutacion;
a = valor_a;
b = valor_b;
porc_pob_ee = porc_pob_ee11;
n_gen_ee = gen_ee;
n_genes = (3 * base_reglas.tabla.n_var_estado + base_reglas.tabla.n_variables) * base_reglas.n_reglas;
prob_mutacion = prob_mutacion / (double) n_genes;
Old = new Structure[long_poblacion];
New = new Structure[long_poblacion];
YaExplotados = new Structure[long_poblacion];
for (i=0; i<long_poblacion; i++) {
Old[i] = new Structure(n_genes);
New[i] = new Structure(n_genes);
YaExplotados[i] = new Structure(n_genes);
}
Hijo = new Structure(n_genes);
sample = new int [long_poblacion];
indices_ordenacion = new int [long_poblacion];
intervalo_mut = new TipoIntervalo();
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);
}
private int ceil (double v) {
int valor;
valor = (int) Math.round(v);
if ((double)valor < v) valor++;
return (valor);
}
public void Intercambio () {
temp = Old;
Old = New;
New = temp;
}
/** Inicialization of the population */
public void Initialize () {
int i, j, temp, mitad_Pob;
double Valor_Inicial_Sigma = 0.001;
if (prob_mutacion < 1.0)
Mu_next = ceil (Math.log(Randomize.Rand()) / Math.log(1.0 - prob_mutacion));
else Mu_next = 1;
Trials=0;
/* Los conjuntos difusos de los antecedentes de las reglas constituyen la
primera parte del primer cromosoma de la poblacion inicial.
Se inicializa C1 en el primer cromosoma. */
New[0].n_e = 1;
primer_gen_C2 = 0;
for (i=0; i<base_reglas.n_reglas; i++) {
for (j=0; j<base_reglas.tabla.n_var_estado; j++) {
New[0].Gene[primer_gen_C2] = base_reglas.BaseReglas[i].Ant[j].x0;
New[0].Gene[primer_gen_C2+1] = base_reglas.BaseReglas[i].Ant[j].x1;
New[0].Gene[primer_gen_C2+2] = base_reglas.BaseReglas[i].Ant[j].x3;
primer_gen_C2 += 3;
}
}
/* Se establecen los intervalos en los que varia cada gen de la primera
parte en la primera generacion */
for (i=0; i<primer_gen_C2; 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;
}
/* Se inicializa la segunda parte del primer cromosoma con los parametros
de los consecuentes de las reglas de la BC inicial, junto con los inter-
valos correspondientes */
for (i=0; i<base_reglas.n_reglas; i++) {
for (j=0; j<base_reglas.tabla.n_variables; j++) {
temp = primer_gen_C2 + i * (base_reglas.tabla.n_variables) + j;
New[0].Gene[temp] = Math.atan (base_reglas.BaseReglas[i].Cons[j]);
intervalos[temp].min = -(Math.PI/2) + 1E-10;
intervalos[temp].max = (Math.PI/2) - 1E-10;
}
}
/* Se genera la segunda mitad de la poblacion inicial generando aleatoriamen-
te C1 y manteniendo C2 */
mitad_Pob = ceil(long_poblacion/2);
for (i=1; i<mitad_Pob; i++) {
for (j=0; j<primer_gen_C2; j++)
New[i].Gene[j] = Randomize.Randdouble(intervalos[j].min, intervalos[j].max);
for (j=primer_gen_C2; j<n_genes; j++)
New[i].Gene[j] = New[0].Gene[j];
New[i].n_e = 1;
}
/* Se genera el resto de la poblacion inicial generando aleatoriamente C1
a partir de los intervalos anteriores y mutando C2 */
for (i=mitad_Pob; i<long_poblacion; i++) {
for (j=0; j<primer_gen_C2; j++)
New[i].Gene[j] = Randomize.Randdouble(intervalos[j].min,intervalos[j].max);
for (j=primer_gen_C2; j<n_genes; j++)
/* Comprobamos que no se salgan del intervalo permitido [-PI/2,PI/2] */
do
New[i].Gene[j] = New[0].Gene[j] + ValorNormal (Valor_Inicial_Sigma);
while (New[i].Gene[j]<=-(Math.PI/2) || New[i].Gene[j]>=(Math.PI/2));
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];
for (mom=0; mom<last; mom+=2) {
dad = mom + 1;
for (i=0; i<n_genes; i++) {
/* we obtain 4 offsprings: appling the t-norma, the
t-conorma and 2 the average function */
C[0].Gene[i] = T_producto_logico (New[mom].Gene[i], New[dad].Gene[i]);
C[1].Gene[i] = S_suma_logica (New[mom].Gene[i], New[dad].Gene[i]);
C[2].Gene[i] = Promedio1 (New[mom].Gene[i], New[dad].Gene[i], a);
C[3].Gene[i] = Promedio1 (New[mom].Gene[i], New[dad].Gene[i], 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;
/* Se determinan los intervalos de mutacion de ese gen y se calcula el
valor mutado */
if (j >= primer_gen_C2) { /* Consecuente: muta en [-PI/2,PI/2] */
intervalo_mut.min = intervalos[j].min;
intervalo_mut.max = intervalos[j].max;
}
else {
switch (j%3) {
case 0: /* Punto izquierdo: muta en [intervalos[j].min,cromosoma[j+1]] */
intervalo_mut.min = intervalos[j].min;
intervalo_mut.max = New[i].Gene[j+1];
break;
case 1: /* Punto central: muta en [cromosoma[j-1],cromosoma[j+1]] */
intervalo_mut.min = New[i].Gene[j-1];
intervalo_mut.max = New[i].Gene[j+1];
break;
case 2: /* Punto derecho: muta en [cromosoma[j-1],intervalos[j].max] */
intervalo_mut.min = New[i].Gene[j-1];
intervalo_mut.max = intervalos[j].max;
break;
}
}
/* we mutate the gene */
if (Randomize.Rand()<0.5)
nval = New[i].Gene[j] + delta (Gen, intervalo_mut.max - New[i].Gene[j], n_generaciones);
else
nval=New[i].Gene[j] - delta (Gen, New[i].Gene[j] - intervalo_mut.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 += ceil (Math.log(m)/Math.log(1.0-prob_mutacion));
}
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);
}
/** Returns 1 if the best current rule is in the list "L" yet */
private int Pertenece_AG (Structure C, Structure [] L, int n_explotados) {
int crom, gen, esta;
crom=0;
while (crom<n_explotados) {
esta = 1;
gen = 0;
while (gen<n_genes && esta==1) {
if (C.Gene[gen] != L[crom].Gene[gen]) esta = 0;
else gen++;
}
if (esta==1) return (1);
crom++;
}
return (0);
}
/** Calculates the new value of sigma according to the number of mutation with hit*/
private double AdaptacionSigma (double old_sigma, double p, double n) {
/* if p<1/5, sigma lowers (c<1 -> sigma*c^(1/n)<sigma) */
if (p < 0.2) return (old_sigma*Math.pow (c,1.0 / n));
/* if p>1/5, sigma increases (c<1 -> sigma/c^(1/n)>sigma)*/
if (p > 0.2) return (old_sigma / Math.pow (c, 1.0 / n));
/* if p=1/5, sigma doesn't change*/
return (old_sigma);
}
/** Generates a normal value with hope 0 and tipical deviation "desv */
private double ValorNormal (double desv) {
double u1, u2;
/* we generate 2 uniform values [0,1] */
u1 = Randomize.Rand ();
u2 = Randomize.Rand ();
/* we calcules a normal value with the uniform values */
return (desv * Math.sqrt (-2 * Math.log(u1)) * Math.sin (2*Math.PI*u2));
}
/** Evolution Strategy (1+1) */
void EE_1_1 (Structure Padre, int Muta_C1, int Muta_C2) {
int j, gen, n_mutaciones, n_exitos, it_sin_exito, fin;
double x0, x1, x2, newx1, newx, S, m, sigma, new_sigma;
/* Inicialization of the counters */
n_mutaciones = n_exitos = it_sin_exito = fin=0;
sigma = new_sigma = 1.0;
do {
if (Muta_C1==1) {
/* Mutation of C1 */
for (gen=0; gen<primer_gen_C2; gen+=3) {
/* we obtain the fuzzy set */
x0 = Padre.Gene[gen];
x1 = Padre.Gene[gen+1];
x2 = Padre.Gene[gen+2];
/* Adaptation of S and mutation of the center point */
S = Adap.Minimo (x1-x0,x2-x1)/2.0;
m = ValorNormal (new_sigma*S);
newx1 = x1 + m;
if (newx1<=x0) {
Hijo.Gene[gen+1]=x0;
newx1=x0;
}
else {
if (newx1>=x2) {
Hijo.Gene[gen+1]=x2;
newx1=x2;
}
else Hijo.Gene[gen+1]=newx1;
}
/* Adaptation of S and mutation of the left point */
S=Adap.Minimo (x0-intervalos[gen].min,newx1-x0)/2.0;
m=ValorNormal (new_sigma*S);
newx=x0 + m;
if (newx<=intervalos[gen].min)
Hijo.Gene[gen]=intervalos[gen].min;
else {
if (newx>=newx1) Hijo.Gene[gen]=newx1;
else Hijo.Gene[gen]=newx;
}
/* Adaptation of S and mutation of the center right */
S=Adap.Minimo (x2-newx1,intervalos[gen+2].max-x2)/2.0;
m=ValorNormal (new_sigma*S);
newx=x2 + m;
if (newx<=newx1) Hijo.Gene[gen+2]=newx1;
else {
if (newx>=intervalos[gen+2].max)
Hijo.Gene[gen+2] = intervalos[gen+2].max;
else
Hijo.Gene[gen+2]=newx;
}
}
}
/* we don't mutate the antecedent (C1) */
else
for (gen=0; gen<primer_gen_C2; gen++)
Hijo.Gene[gen] = Padre.Gene[gen];
if (Muta_C2==1) {
/* Mutation of C2 */
for (gen=primer_gen_C2; gen<n_genes; gen++) {
m = ValorNormal (new_sigma*S_sigma_consecuentes);
newx = Padre.Gene[gen] + m;
if (newx<intervalos[gen].min)
Hijo.Gene[gen]=intervalos[gen].min;
else {
if (newx>intervalos[gen].max)
Hijo.Gene[gen]=intervalos[gen].max;
else
Hijo.Gene[gen]=newx;
}
}
}
/* we don't mutate the consequent (C2) */
else
for (gen=primer_gen_C2; gen<n_genes; gen++)
Hijo.Gene[gen] = Padre.Gene[gen];
/* we evaluate the son */
Hijo.Perf = fun_adap.eval (Hijo.Gene);
/* we count the mutation */
n_mutaciones += 1;
/* if the son is better than the father this relieve his father, we accept sigma and we count another hit */
if (Hijo.Perf < Padre.Perf) {
n_exitos+=1;
it_sin_exito=0;
sigma=new_sigma;
for (j=0;j<n_genes;j++)
Padre.Gene[j]=Hijo.Gene[j];
Padre.Perf=Hijo.Perf;
}
else it_sin_exito++;
/* we adapt sigma */
new_sigma = AdaptacionSigma (sigma,n_exitos/(double)n_mutaciones, (double)n_genes-base_reglas.tabla.n_var_estado);
if (it_sin_exito>=n_gen_ee) fin = 1;
}while (fin==0);
}
/* Main of the Evolution Strategy (1+1) */
public void Estrategia_Evolucion () {
int i, j, temp, cromosoma, n_ya_explotados, n_a_explotar;
/* we evaluate the population */
for (i=0; i<long_poblacion; i++)
if (New[i].n_e==1) {
New[i].Perf = fun_adap.eval (New[i].Gene);
New[i].n_e = 0;
}
/* we order the population by means of the bubble method */
for (i=0; i<long_poblacion; i++)
indices_ordenacion[i] = i;
for (i=0; i<long_poblacion; i++)
for (j=0; j<long_poblacion-i-1; j++)
if (New[indices_ordenacion[j+1]].Perf < New[indices_ordenacion[j]].Perf) {
temp = indices_ordenacion[j];
indices_ordenacion[j] = indices_ordenacion[j+1];
indices_ordenacion[j+1] = temp;
}
/* the evolution strategy is applied to each individual of the population with fitness better than 0 */
i = 0;
n_ya_explotados = 0;
n_a_explotar = (int) (porc_pob_ee * long_poblacion);
while ((i<long_poblacion) && (n_ya_explotados<n_a_explotar)) {
/* we initialize the index of the chromosome */
cromosoma = indices_ordenacion[i];
/* we store this chromosome in the list of the exploited */
for (j=0; j<n_genes; j++)
YaExplotados[n_ya_explotados].Gene[j] = New[cromosoma].Gene[j];
/* Inicialization of the counters */
YaExplotados[n_ya_explotados].Perf = New[cromosoma].Perf;
n_ya_explotados++;
/* we apply the ES(1+1) */
EE_1_1 (New[cromosoma], 1, 1);
/* we look for the next unrecurrent individual */
if (n_ya_explotados<n_a_explotar)
do
i++;
while (i<long_poblacion && Pertenece_AG (New[indices_ordenacion[i]],YaExplotados,n_ya_explotados)==1);
}
}
}