AG.java

/***********************************************************************

	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.MamLocalTunRules;
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, n_reglas;

	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, double cruce, double mutacion, double valor_a, double valor_b, Adap funcion) {
		int i;

		long_poblacion = n_poblacion;
		prob_cruce = cruce;
		prob_mutacion = mutacion;
		a = valor_a;
		b = valor_b;
		fun_adap = funcion;

		sample = new int[long_poblacion];
	}


	/** Reads the RB of a input file */
	private void leer_BR (String fichero, int n_variables){
		int i;
		String cadena;

		cadena = Fichero.leeFichero(fichero);
		StringTokenizer sT = new StringTokenizer(cadena, "\n\r\t ", false);
		sT.nextToken();
		sT.nextToken();
		sT.nextToken();

		n_reglas = Integer.parseInt(sT.nextToken());
		n_genes = 3 * n_variables * n_reglas;

		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);
		}

		for (i=0; i<n_genes; i++) 
			New[0].Gene[i] = Double.parseDouble(sT.nextToken());

		New[0].n_e = 1;
	}


	public void Intercambio () {
		temp = Old;
		Old = New;
		New = temp;
	}

	/** Inicialization of the population */
	public void Initialize (String fichero, int n_variables) {
		int i, j;

		leer_BR(fichero, n_variables);

		prob_mutacion = prob_mutacion / (double) n_genes;

		C = new Structure[4];
		for (i=0; i<4; i++)  C[i] = new Structure(n_genes);

		intervalos = new TipoIntervalo[n_genes];
		for (i=0; i<n_genes; i++)  intervalos[i] = new TipoIntervalo();

		/* 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);
	}
}