遗传算法c源程序

/***************************************************************/
/* 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
#include
#include

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

#define POPSIZE 50               /* population size */
#define MAXGENS 1000             /* max. number of generations */
#define NVARS 3                  /* no. of problem variables */
#define PXOVER 0.8               /* probability of crossover */
#define PMUTATION 0.15           /* probability of mutation */
#define TRUE 1
#define FALSE 0

int generation;                  /* current generation no. */
int cur_best;                    /* best individual */
FILE *galog;                     /* an output 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);
     }

/* 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)
{
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[1]*x[2]) + x[3];
     }
}

/***************************************************************/
/* 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                       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)
           {
           ++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));
best_val = population[POPSIZE].fitness;

fprintf(galog, "\n%5d,      %6.3f, %6.3f, %6.3f \n\n", 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                                     */
/**************************************************************/

int main(void)
{
int i;

if ((galog = fopen("galog.txt","w"))==NULL)
     {
     printf("open file galog.txt error!\n");
     return -1;
     }
generation = 0;
     printf("open file galog.txt OK!\n");

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

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

for (i = 0; i < NVARS; i++)
  {
  fprintf (galog,"\n var(%d) = %3.3f",i,population[POPSIZE].gene);
  }
fprintf(galog,"\n\n Best fitness = %3.3f",population[POPSIZE].fitness);
fclose(galog);
printf("Success\n");
return 0;
}
/***************************************************************/