static entity *struggle_adaptation(population *pop, entity *child)
{
entity *adult; /* Adapted solution. */
int allele; /* Randomly selected allele. */
/*
* We must generate a new solution by copying the original solution.
* This function copys all genomic, and if appropriate, phenomic data.
* It is never safe to adapt the solution in place.
*/
adult = ga_entity_clone(pop, child);
/* Make point mutation. */
allele = random_int(strlen(target_text));
((char *)adult->chromosome[0])[allele]++;
struggle_score(pop, adult);
if (adult->fitness > child->fitness)
return adult;
/* Searching in that previous direction didn't help. */
((char *)adult->chromosome[0])[allele]-=2;
struggle_score(pop, adult);
if (adult->fitness > child->fitness)
return adult;
/* We must already be at a maxima. */
((char *)adult->chromosome[0])[allele]++;
adult->fitness = child->fitness;
return adult;
}
entity *test_adaptation(population *pop, entity *child){
entity *adult; /* Adapted solution. */
double * img;
Image * fc;
int i;
/*
* We must generate a new solution by copying the original solution.
* This function copys all genomic, and if appropriate, phenomic data.
* It is never safe to adapt the solution in place.
*/
adult = ga_entity_clone(pop, child);
img = (double *)adult->chromosome[0];
for(i = 0;i<TSIZE(amp);i++){
real_in->r[i] = img[i];
real_in->c[i] = img[i+TSIZE(amp)];
real_in->image[i] = norm(real_in,i);
}
freeimg(real_out);
if(get_algorithm(opts,&my_log) == HIO){
real_out = basic_hio_iteration(amp, real_in, support,opts,&my_log);
}else if(get_algorithm(opts,&my_log) == RAAR){
real_out = basic_raar_iteration(amp,exp_sigma, real_in, support,opts,&my_log);
}else if(get_algorithm(opts,&my_log) == HPR){
real_out = basic_hpr_iteration(amp, real_in, support,opts,&my_log);
}
adult->fitness = 0;
fc = image_fft(real_out);
for(i = 0;i<TSIZE(amp);i++){
img[i] = real_out->r[i];
img[i+TSIZE(amp)] = real_out->c[i];
adult->fitness -= fabs(fc->image[i] - amp->image[i]);
}
freeimg(fc);
return adult;
}
GAULFUNC int ga_tabu( population *pop,
entity *initial,
const int max_iterations )
{
int iteration=0; /* Current iteration number. */
int i, j; /* Index into putative solution array. */
entity *best; /* Current best solution. */
entity **putative; /* Current working solutions. */
entity *tmp; /* Used to swap working solutions. */
entity **tabu_list; /* Tabu list. */
int tabu_list_pos=0; /* Index into the tabu list. */
/* Checks. */
if (!pop) die("NULL pointer to population structure passed.");
if (!pop->evaluate) die("Population's evaluation callback is undefined.");
if (!pop->mutate) die("Population's mutation callback is undefined.");
if (!pop->rank) die("Population's ranking callback is undefined.");
if (!pop->tabu_params) die("ga_population_set_tabu_params(), or similar, must be used prior to ga_tabu().");
if (!pop->tabu_params->tabu_accept) die("Population's tabu acceptance callback is undefined.");
/* Prepare working entities. */
best = ga_get_free_entity(pop); /* The best solution so far. */
if ( !(putative = s_malloc(sizeof(entity *)*pop->tabu_params->search_count)) )
die("Unable to allocate memory");
for (i=0; i<pop->tabu_params->search_count; i++)
{
putative[i] = ga_get_free_entity(pop); /* The 'working' solutions. */
}
/* Allocate and clear an array for the tabu list. */
if ( !(tabu_list = s_malloc(sizeof(vpointer)*pop->tabu_params->list_length)) )
die("Unable to allocate memory");
for (i=0; i<pop->tabu_params->list_length; i++)
{
tabu_list[i] = NULL;
}
/* Do we need to generate a random starting solution? */
if (!initial)
{
plog(LOG_VERBOSE, "Will perform tabu-search with random starting solution.");
initial = ga_get_free_entity(pop);
ga_entity_seed(pop, best);
}
else
{
plog(LOG_VERBOSE, "Will perform tabu-search with specified starting solution.");
ga_entity_copy(pop, best, initial);
}
/*
* Ensure that initial solution is scored.
*/
if (best->fitness==GA_MIN_FITNESS) pop->evaluate(pop, best);
plog( LOG_VERBOSE,
"Prior to the first iteration, the current solution has fitness score of %f",
best->fitness );
/*
* Do all the iterations:
*
* Stop when (a) max_iterations reached, or
* (b) "pop->iteration_hook" returns FALSE.
*/
while ( (pop->iteration_hook?pop->iteration_hook(iteration, best):TRUE) &&
iteration<max_iterations )
{
iteration++;
/*
* Generate and score new solutions.
*/
for (i=0; i<pop->tabu_params->search_count; i++)
{
pop->mutate(pop, best, putative[i]);
pop->evaluate(pop, putative[i]);
}
/*
* Sort new solutions (putative[0] will have highest rank).
* We assume that there are only a small(ish) number of
* solutions and, therefore, a simple bubble sort is adequate.
*/
for (i=1; i<pop->tabu_params->search_count; i++)
{
for (j=pop->tabu_params->search_count-1; j>=i; j--)
{
if ( pop->rank(pop, putative[j], pop, putative[j-1]) > 0 )
{ /* Perform a swap. */
tmp = putative[j];
putative[j] = putative[j-1];
putative[j-1] = tmp;
}
}
}
/*
* Save best solution if it is an improvement, otherwise
* select the best non-tabu solution (if any).
* If appropriate, update the tabu list.
*/
if ( pop->rank(pop, putative[0], pop, best) > 0 )
{
tmp = best;
best = putative[0];
putative[0] = tmp;
if (tabu_list[tabu_list_pos] == NULL)
{
tabu_list[tabu_list_pos] = ga_entity_clone(pop, best);
}
else
{
ga_entity_blank(pop, tabu_list[tabu_list_pos]);
ga_entity_copy(pop, tabu_list[tabu_list_pos], best);
}
tabu_list_pos++;
if (tabu_list_pos >= pop->tabu_params->list_length)
tabu_list_pos=0;
}
else
{
if ( -1 < (j = gaul_check_tabu_list(pop, putative, tabu_list)) )
{
tmp = best;
best = putative[j];
putative[j] = tmp;
if (tabu_list[tabu_list_pos] == NULL)
{
tabu_list[tabu_list_pos] = ga_entity_clone(pop, best);
}
else
{
ga_entity_blank(pop, tabu_list[tabu_list_pos]);
ga_entity_copy(pop, tabu_list[tabu_list_pos], best);
}
tabu_list_pos++;
if (tabu_list_pos >= pop->tabu_params->list_length)
tabu_list_pos=0;
}
}
/*
* Save the current best solution in the initial entity, if this
* is now the best found so far.
*/
if ( pop->rank(pop, best, pop, initial) > 0 )
{
ga_entity_blank(pop, initial);
ga_entity_copy(pop, initial, best);
}
/*
* Use the iteration callback.
*/
plog( LOG_VERBOSE,
"After iteration %d, the current solution has fitness score of %f",
iteration,
best->fitness );
} /* Iteration loop. */
/*
* Cleanup.
*/
ga_entity_dereference(pop, best);
for (i=0; i<pop->tabu_params->search_count; i++)
{
ga_entity_dereference(pop, putative[i]);
}
for (i=0; i<pop->tabu_params->list_length; i++)
{
if (tabu_list[i] != NULL)
ga_entity_dereference(pop, tabu_list[i]);
}
s_free(putative);
s_free(tabu_list);
return iteration;
}
