test.cpp

简单的遗传算法与模式搜索算法结合

/***************************************************************/
/* This is a simple genetic algorithm implementation where the */
/* evaluation function takes positive values only and the      */
/* fitness of an individual is the same as the value of the    */
/* objective function                                          */
/***************************************************************/

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>

/* Change any of these parameters to match your needs */

#define POPSIZE 20               /* population size */
#define MAXGENS 1000             /* max. number of generations */
#define NVARS 2                 /* no. of problem variables */
#define PXOVER 0.8               /* probability of crossover */
#define PMUTATION 0.001           /* probability of mutation */
#define TRUE 1
#define FALSE 0
#define PSEARCH 0.5

#define VAR 250
#define PSA 0
#define rho 0.00000005
#define itermax 100
#define epsilon 1E-6 
int generation;                  /* current generation no. */
int cur_best;                    /* best individual */
FILE *galog;                     /* an output(1) file */
FILE *output;                     /* an output(1) file */

struct genotype /* genotype (GT), a member of the population */
{
  double gene[NVARS];        /* a string of variables */
  double fitness;            /* GT's fitness */
  double upper[NVARS];       /* GT's variables upper bound */
  double lower[NVARS];       /* GT's variables lower bound */
  double rfitness;           /* relative fitness */
  double cfitness;           /* cumulative fitness */
};

struct genotype population[POPSIZE+1];    /* population */
struct genotype newpopulation[POPSIZE+1]; /* new population; */
                                          /* replaces the */
                                          /* old generation */

/* Declaration of procedures used by this genetic algorithm */

void initialize(void);
double randval(double, double);
void evaluate(void);
void keep_the_best(void);
void elitist(void);
void select(void);
void crossover(void);
void Xover(int,int);
void swap(double *, double *);
void mutate(void);
void report(void);

/***************************************************************/
/* Initialization function: Initializes the values of genes    */
/* within the variables bounds. It also initializes (to zero)  */
/* all fitness values for each member of the population. It    */
/* reads upper and lower bounds of each variable from the      */
/* input file `gadata.txt'. It randomly generates values       */
/* between these bounds for each gene of each genotype in the  */
/* population. The format of the input file `gadata.txt' is    */
/* var1_lower_bound var1_upper bound                           */
/* var2_lower_bound var2_upper bound ...                       */
/***************************************************************/

void initialize(void)
{
	FILE *infile;
	int i, j;
	double lbound, ubound;

	if ((infile = fopen("gadata.txt","r"))==NULL)
		  {
		  fprintf(galog,"\nCannot open input file!\n");
		  exit(1);
		  }

	remove("output.dat");
/* initialize variables within the bounds */

	for (i = 0; i < NVARS; i++)
		  {
		  fscanf(infile, "%lf",&lbound);
		  fscanf(infile, "%lf",&ubound);


		  for (j = 0; j < POPSIZE; j++)
			   {
			   population[j].fitness = 0;
			   population[j].rfitness = 0;
			   population[j].cfitness = 0;
			   population[j].lower[i] = lbound;
			   population[j].upper[i] = ubound;
			   population[j].gene[i]  = randval(population[j].lower[i],
									   population[j].upper[i]);
			   }
		  }

	fclose(infile);
}

/***********************************************************/
/* Random value generator: Generates a value within bounds */
/***********************************************************/

double randval(double low, double high)
{
	double val;
	val = ((double)(rand()%1000)/1000.0)*(high - low) + low;
	return(val);
}

/*************************************************************/
/* Evaluation function: This takes a user defined function.  */
/* Each time this is changed, the code has to be recompiled. */
/* The current function is:  x[1]^2-x[1]*x[2]+x[3]           */
/*************************************************************/

void evaluate(void)
{
	if ((output = fopen("output.dat","a"))==NULL)
		  {
		  exit(1);
		  }
	int mem;
	int i;
	double x[NVARS+1];

	for (mem = 0; mem < POPSIZE; mem++)
	{
		  for (i = 0; i < NVARS; i++)
				x[i+1] = population[mem].gene[i];

		  population[mem].fitness = x[1]*x[1]+x[2]*x[2];

		  
		  if (population[mem].fitness <40 && population[mem].fitness>35)
		  {
			  fprintf(output, "\n%5d,      %6.9f, %6.9f, %6.9f ", generation, 
										x[1],x[2],population[mem].fitness);
 		  }

	}
	fclose(output);
}

/***************************************************************/
/* Keep_the_best function: This function keeps track of the    */
/* best member of the population. Note that the last entry in  */
/* the array Population holds a copy of the best individual    */
/***************************************************************/

void keep_the_best()
{
	int mem;
	int i;
	cur_best = 0; /* stores the index of the best individual */

	for (mem = 0; mem < POPSIZE; mem++)
		  {
		  if (population[mem].fitness > population[POPSIZE].fitness)
				{
				cur_best = mem;
				population[POPSIZE].fitness = population[mem].fitness;
				}
		  }
	/* once the best member in the population is found, copy the genes */
	for (i = 0; i < NVARS; i++)
		  population[POPSIZE].gene[i] = population[cur_best].gene[i];
}

/****************************************************************/
/* Elitist function: The best member of the previous generation */
/* is stored as the last in the array. If the best member of    */
/* the current generation is worse then the best member of the  */
/* previous generation, the latter one would replace the worst  */
/* member of the current population                             */
/****************************************************************/

void elitist()
{
	int i;
	double best, worst;             /* best and worst fitness values */
	int best_mem, worst_mem; /* indexes of the best and worst member */

	best = population[0].fitness;
	worst = population[0].fitness;
	for (i = 0; i < POPSIZE - 1; ++i)
		  {
		  if(population[i].fitness > population[i+1].fitness)
				{      
				if (population[i].fitness >= best)
					  {
					  best = population[i].fitness;
					  best_mem = i;
					  }
				if (population[i+1].fitness <= worst)
					  {
					  worst = population[i+1].fitness;
					  worst_mem = i + 1;
					  }
				}
		  else
				{
				if (population[i].fitness <= worst)
					  {
					  worst = population[i].fitness;
					  worst_mem = i;
					  }
				if (population[i+1].fitness >= best)
					  {
					  best = population[i+1].fitness;
					  best_mem = i + 1;
					  }
				}
		  }
	/* if best individual from the new population is better than */
	/* the best individual from the previous population, then    */
	/* copy the best from the new population; else replace the   */
	/* worst individual from the current population with the     */
	/* best one from the previous generation                     */

	if (best >= population[POPSIZE].fitness)
		{
		for (i = 0; i < NVARS; i++)
		   population[POPSIZE].gene[i] = population[best_mem].gene[i];
		population[POPSIZE].fitness = population[best_mem].fitness;
		}
	else
		{
		for (i = 0; i < NVARS; i++)
		   population[worst_mem].gene[i] = population[POPSIZE].gene[i];
		population[worst_mem].fitness = population[POPSIZE].fitness;
		} 
}
/**************************************************************/
/* Selection function: Standard proportional selection for    */
/* maximization problems incorporating elitist model - makes  */
/* sure that the best member survives                         */
/**************************************************************/

void select(void)
{
	int mem, i, j, k;
	double sum = 0;
	double p;

	/* find total fitness of the population */
	for (mem = 0; mem < POPSIZE; mem++)
		  {
		  sum += population[mem].fitness;
		  }

	/* calculate relative fitness */
	for (mem = 0; mem < POPSIZE; mem++)
		  {
		  population[mem].rfitness =  population[mem].fitness/sum;
		  }
	population[0].cfitness = population[0].rfitness;

	/* calculate cumulative fitness */
	for (mem = 1; mem < POPSIZE; mem++)
		  {
		  population[mem].cfitness =  population[mem-1].cfitness +       
							  population[mem].rfitness;
		  }

	/* finally select survivors using cumulative fitness. */

	for (i = 0; i < POPSIZE; i++)
		  { 
		  p = rand()%1000/1000.0;
		  if (p < population[0].cfitness)
				newpopulation[i] = population[0];      
		  else
				{
				for (j = 0; j < POPSIZE;j++)      
					  if (p >= population[j].cfitness && 
								  p<population[j+1].cfitness)
							newpopulation[i] = population[j+1];
				}
		  }
	/* once a new population is created, copy it back */

	for (i = 0; i < POPSIZE; i++)
		  population[i] = newpopulation[i];      
}

/***************************************************************/
/* Crossover selection: selects two parents that take part in  */
/* the crossover. Implements a single point crossover          */
/***************************************************************/

void crossover(void)
{
	int i, mem, one;
	int first  =  0; /* count of the number of members chosen */
	double x;

	for (mem = 0; mem < POPSIZE; ++mem)
		  {
		  x = rand()%1000/1000.0;
		  if (x < PXOVER)
				{