实例（C语言实现的遗遗传算法）

/***************************************************************/
/* 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 [color=#555555] = lbound;
population[j].upper= ubound;
population[j].gene = randval(population[j].lower,
population[j].upper);
}
}
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 [color=#555555];

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 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 [color=#555555] = population[cur_best].gene;
}
/****************************************************************/
/* 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 [color=#555555].fitness > population[i+1].fitness)
{      
if (population.fitness >= best)
{
best = population.fitness;
best_mem = i;
}
if (population[i+1].fitness [color=#555555].fitness [color=#555555].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 [color=#555555] = population[best_mem].gene;
population[POPSIZE].fitness = population[best_mem].fitness;
}
else
{
for (i = 0; i [color=#555555] = population[POPSIZE].gene;
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 [color=#555555] = population[0];      
else
{
for (j = 0; j = population[j].cfitness &&
p[color=#555555] = population[j+1];
}
}
/* once a new population is created, copy it back */
for (i = 0; i [color=#555555] = newpopulation;      
}
/***************************************************************/
/* 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 1)
{
if(NVARS == 2)
point = 1;
else
point = (rand() % (NVARS - 1)) + 1;
for (i = 0; i [color=#555555], &population[two].gene);
}
}
/*************************************************************/
/* 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 [color=#555555].lower[j];
hbound = population.upper[j];  
population.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 [color=#555555].fitness;
sum_square += population.fitness * population.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 nn", 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[color=#555555]);
}
fprintf(galog,"nn Best fitness = %3.3f",population[POPSIZE].fitness);
fclose(galog);
printf("Successn");
}
/***************************************************************/