/**
* main program
*/
int
main (void)
{
float cpu1,cpu2;
cpu1 = ((float) clock())/CLOCKS_PER_SEC;
srand (time (NULL));
ga_struct *population = malloc (sizeof (ga_struct) * POPSIZE);
ga_struct *beta_population = malloc (sizeof (ga_struct) * POPSIZE);
init_population (population, beta_population);
char *gen_str = population[0].gen;
char element[5] = "\0";
strncpy (element, gen_str, 4);
//if (strcmp ("0000", element) == 0)
int index = 0;
for (; index < POPSIZE; index++)
{
cal_fitness (population);
sort_by_fitness (population);
// print current best individual
printf ("binary string: %s - fitness: %d\n", population[0].gen,
population[0].fitness);
if (population[0].fitness == 0)
{
//~ print equation
decode_gen (&population[0]);
break;
}
mate (population, beta_population);
swap (&population, &beta_population);
}
free_population (population);
free_population (beta_population);
cpu2 = ((float) clock())/CLOCKS_PER_SEC;
printf("Execution time (s) = %le\n",cpu2-cpu1);
return 0;
}
int main(void)
{
int popsize = 0, max_iter = 0;
char target[128];
char str_buffer[64];
FILE *fp = fopen("conf", "r");
fscanf(fp, "%s %d", &str_buffer, &popsize);
fscanf(fp, "%s %s", &str_buffer, &target);
fscanf(fp, "%s %d", &str_buffer, &max_iter);
int target_length = strlen(target);
srand(time(NULL));
printf("Genetic Algorithm to find a user-specified target string\n");
printf("Quinn Thibeault - 2015\n");
printf("Target string: %s Length: %d\n", target, target_length);
printf("Population Size: %d\n", popsize);
printf("Maximum Iterations: %d\n\n", max_iter);
struct organism population[POPSIZE], buffer[POPSIZE];
struct organism *p_pop = population;
struct organism *p_buf = buffer;
init_population(p_pop, target_length);
init_population(p_buf, target_length);
gen_random_population(p_pop, target_length);
for(int i=0;i<ITER; ++i){
calc_fitness(p_pop, target, target_length);
sort_by_fitness(p_pop);
//print_most_fit(p_pop);
if(p_pop->fitness == 0){
printf("Number of generations: %d\n", i);
break;
}
regen_population(p_pop, p_buf, target_length);
swap(&p_pop, &p_buf);
}
print_most_fit(p_pop);
return 0;
}
void SolverEvolver::evolve()
{
sort_by_fitness();
std::vector<Individual> child_population;
while (child_population.size() < population_size_)
{
Individual& c0 = select();
Individual& c1 = select();
if (rand_.NextBool(combination_p_))
{
crossover(c0.genome, c1.genome);
}
mutate(c0.genome);
mutate(c1.genome);
child_population.push_back(c0);
child_population.push_back(c1);
}
population_ = child_population;
initate_population();
}
