prog.cpp

用C++编写基于用遗传算法解一个简单参数优化问题

/* 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];      
}

//锦标赛
void select_tournament(void)
{
int  i, j;
int r,s;

for(i=0;i<POPSIZE;i++){
  r=rand()%POPSIZE;
  do{s=rand()%POPSIZE;
 }while(r==s);
 if(population[r].fitness>population[s].fitness) 
 { newpopulation[i].fitness=population[r].fitness;
  for(j=0;j<NVARS;j++)
  newpopulation[i].gene[j]=population[r].gene[j];
  }
else { newpopulation[i].fitness=population[s].fitness;
  for(j=0;j<NVARS;j++)
  newpopulation[i].gene[j]=population[s].gene[j];
  }
}
for (i = 0; i < POPSIZE; i++)
      population[i] = newpopulation[i];      

}

//期望值
void select_expect(void)
{
	int i=0,k=0,p=0,r=0,j=0;
	double temp=0.0;
	double sum=0.0;
	double savenum[POPSIZE]={0};
	int index[POPSIZE]={0};

	//计算群体中各个个体在下一代中的期望生存数目
	for(i=0;i<POPSIZE;i++)
		sum=sum+population[i].fitness;
	for(i=0;i<POPSIZE;i++)
		savenum[i]=POPSIZE*population[i].fitness/sum;
	//产生下一代群体
	k=-1;
	for(i=0;i<POPSIZE;i++)
	{
		p=(int)savenum[i];
		if(p>0)
		{
			for(j=1;j<=p;j++)
			{
				k++;
				for(r=0;r<NVARS;r++)
					newpopulation[j].gene[r]=population[j].gene[r];
			}
		}
	}
	for(i=0;i<POPSIZE;i++)
	{
		savenum[i]=savenum[i]-(int)savenum[i];
		index[i]=i;
	}
	for(i=0;i<POPSIZE-1;i++)
	{
		for(j=i+1;j<POPSIZE;j++)
		{
			if(savenum[i]>savenum[j])
			{
				temp=savenum[i];
				savenum[i]=savenum[j];
				savenum[j]=temp;
				p=index[i];
				index[i]=index[j];
				index[j]=p;
			}
		}
	}
	j=k+1;
	for(i=0;i<POPSIZE-j;i++)
	{
		k++;
		for(r=0;r<NVARS;r++)
			newpopulation[k].gene[r]=population[ index[i] ].gene[r];
	}
}

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

void crossover(void)
{
int 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((sum_square - square_sum)/(POPSIZE - 1));
//stddev = ((sum_square - square_sum)/(POPSIZE - 1));////////////////// give up 'sqrt'
best_val = population[POPSIZE].fitness;

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


/**************************************************************/
/* 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)
{

// add 2007-9-29 将 output 置于全局，以适用于 MAXGENS 突破 507 。
if ((output = fopen("output.dat","w+"))==NULL)
      {
      exit(1);
      }

if ((galog = fopen("galog.txt","w+"))==NULL)
      {
      exit(1);
      }

int i=0;
generation = 0;

fprintf(galog, "\n generation  best  average  standard \n");
fprintf(galog, " number\tbest_fitness\n");


	generation = 0;

	srand(time(NULL));

	int sel=0;

	printf("\n------------------------------------------------------------------\n");
	printf("\n                       遗传算法 选择算子 问题                      \n");
	printf("\n------------------------------------------------------------------\n\n");
	printf("选择方法：1、轮盘赌 2、锦标赛 3、期望值方法 4、选择排序\n");
	printf(">>");	scanf("%d",&sel);

	initialize();//read data and initialize 'population'
	evaluate();
	keep_the_best();//This function keeps track of the best member of the population. 
	while(generation<MAXGENS)
	{
		  generation++;
		  if(sel==1)
			  select();
		  else if(sel==2)
			  select_tournament();
		  else if(sel==3)
			  select_expect();
		  else
			  select_ranking();
		  crossover();
		  mutate();
		  report();
		  evaluate();
		  elitist();
	}//while

fprintf(galog,"\n\n Simulation completed\n");
fprintf(galog,"\n Best member: \n");

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);
fclose(output);
printf("Success\n");
}
/***************************************************************/