prog.cpp

一个Ｃ语言写的用锦标赛方法来做选择算法的智能计算

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 */

//***************************************************************
/* finally select survivors using cumulative fitness. */
//
//struct genotype GMax;
//
//for (i = 0; i < POPSIZE; i++)
//{ 
//		  GMax.fitness = 0;
//	  srand((unsigned)time(NULL));
//		  for(int k=0;k<K;k++)
//		  {
//			  p = rand()%1000/1000.0;
//			  if (p < population[0].cfitness && GMax.fitness<population[0].fitness)
//				  GMax = population[0];      
//			  else
//					{
//						for (j = 0; j < POPSIZE;j++)   
//						{
//							  if (p >= population[j].cfitness && 
//										  p<population[j+1].cfitness 
//												&& GMax.fitness<population[j+1].fitness)
//									GMax = population[j+1];
//						}
//					}	 
//		  }
//		  newpopulation[i]=GMax;
//}
//

//*************************************************************
//int q[K];
//int m, n, min, best;
//double max;
//
//for(i=0;i<POPSIZE;i++)
//{
//	for(j=0;j<K;j++)
//		q[j] = ((int)(rand()%POPSIZE));
//	min=q[0];
//	best=min;
//	max=population[min].rfitness;
//
//	//best=q[0];
//	//max=population[best].rfitness;
//
//	for(j=1;j<K;j++)
//	{
//		min=q[j];
//		if(population[min].rfitness>max)
//		{
//			max=population[min].rfitness;
//			best=min;
//		}
//	}
//    newpopulation[i] = population[best];
//}
//********************************************************************

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)
            {
            ++first;
            if (first % 2 == 0)
                  Xover(one, mem);
            else
                  one = mem;
            }
      }
}
/**************************************************************/
/* Crossover: performs crossover of the two selected parents. */
/**************************************************************/

void Xover(int one, int two)
{
int i;
int point; /* crossover point */

/* select crossover point */
if(NVARS > 1)
   {
   if(NVARS == 2)
         point = 1;
   else
         point = (rand() % (NVARS - 1)) + 1;

   for (i = 0; i < point; i++)
        swap(&population[one].gene[i], &population[two].gene[i]);

   }
}

/*************************************************************/
/* Swap: A swap procedure that helps in swapping 2 variables */
/*************************************************************/

void swap(double *x, double *y)
{
double temp;

temp = *x;
*x = *y;
*y = temp;

}

/**************************************************************/
/* Mutation: Random uniform mutation. A variable selected for */
/* mutation is replaced by a random value between lower and   */
/* upper bounds of this variable                              */
/**************************************************************/

void mutate(void)
{
int i, j;
double lbound, hbound;
double x;

for (i = 0; i < POPSIZE; i++)
      for (j = 0; j < NVARS; j++)
            {
            x = rand()%1000/1000.0;
            if (x < PMUTATION)
                  {
                  /* find the bounds on the variable to be mutated */
                  lbound = population[i].lower[j];
                  hbound = population[i].upper[j];  
                  population[i].gene[j] = randval(lbound, hbound);
                  }
            }
}

/***************************************************************/
/* Report function: Reports progress of the simulation. Data   */
/* dumped into the  output file are separated by commas        */
/***************************************************************/

void report(void)
{
int i;
double best_val;            /* best population fitness */
double avg;                 /* avg population fitness */
double stddev;              /* std. deviation of population fitness */
double sum_square;          /* sum of square for std. calc */
double square_sum;          /* square of sum for std. calc */
double sum;                 /* total population fitness */

sum = 0.0;
sum_square = 0.0;

for (i = 0; i < POPSIZE; i++)
      {
      sum += population[i].fitness;
      sum_square += population[i].fitness * population[i].fitness;
      }

avg = sum/(double)POPSIZE;
square_sum = avg * avg * POPSIZE;
stddev = sqrt(fabs(sum_square - square_sum)/(POPSIZE - 1));
best_val = population[POPSIZE].fitness;

fprintf(galog, "\n%5d      %6.9f  %6.9f %6.9f ", generation, 
                                      best_val, avg, stddev);
}


/**************************************************************/
/* Main function: Each generation involves selecting the best */
/* members, performing crossover & mutation and then          */
/* evaluating the resulting population, until the terminating */
/* condition is satisfied                                     */
/**************************************************************/

void main(void)
{
int i;
if ((galog = fopen("galog.txt","w"))==NULL)
      {
      exit(1);
      }
generation = 0;

fprintf(galog, "\n generation  best  average  standard \n");
fprintf(galog, " number      value fitness  deviation \n");

initialize();
evaluate();
keep_the_best();
while(generation<MAXGENS)
      {
      generation++;
      select();
      crossover();
      mutate();
      report();
      evaluate();
      elitist();
      }
fprintf(galog,"\n\n Simulation completed\n");
fprintf(galog,"\n Best member: \n");

FILE *fBest;

int gener = Best[0]+1;
fBest=fopen("Best.txt","w");
fprintf(fBest,"%-8d %-8.3f %-8.3f   %-10d  %-12.9f",POPSIZE,PXOVER,PMUTATION,gener,Best[1]);
fclose(fBest);

for (i = 0; i < NVARS; i++)
   {
   fprintf (galog,"\n var(%d) = %3.9f",i,population[POPSIZE].gene[i]);
   }
fprintf(galog,"\n\n Best fitness = %3.9f",population[POPSIZE].fitness);
fclose(galog);
printf("Success\n");
}
/***************************************************************/