GAULFUNC void ga_entity_dump(population *pop, entity *john)
{
printf( "Entity id %d rank %d\n",
ga_get_entity_id(pop, john),
ga_get_entity_rank(pop, john) );
printf( "Fitness %f\n", john->fitness );
printf( "data <%s> data0 <%s> chromo <%s> chromo0 <%s>\n",
john->data?"defined":"undefined",
john->data!=NULL&&((SLList *)john->data)->data!=NULL?"defined":"undefined",
john->chromosome?"defined":"undefined",
john->chromosome!=NULL&&john->chromosome[0]!=NULL?"defined":"undefined" );
return;
}
int ga_deterministiccrowding( population *pop,
const int max_generations )
{
int generation=0; /* Current generation number. */
int *permutation, *ordered; /* Arrays of entities. */
entity *mother, *father; /* Current entities. */
entity *son, *daughter, *entity; /* Current entities. */
int i; /* Loop variable over entities. */
double dist1, dist2; /* Genetic or phenomic distances. */
int rank; /* Rank of entity in population. */
/* Checks. */
if (!pop)
die("NULL pointer to population structure passed.");
if (!pop->dc_params)
die("ga_population_set_deterministiccrowding_params(), or similar, must be used prior to ga_deterministiccrowding().");
if (!pop->evaluate) die("Population's evaluation callback is undefined.");
if (!pop->mutate) die("Population's mutation callback is undefined.");
if (!pop->crossover) die("Population's crossover callback is undefined.");
if (!pop->dc_params->compare) die("Population's comparison callback is undefined.");
plog(LOG_VERBOSE, "The evolution by deterministic crowding has begun!");
pop->generation = 0;
/*
* Score the initial population members.
*/
if (pop->size < pop->stable_size)
gaul_population_fill(pop, pop->stable_size - pop->size);
for (i=0; i<pop->size; i++)
{
if (pop->entity_iarray[i]->fitness == GA_MIN_FITNESS)
pop->evaluate(pop, pop->entity_iarray[i]);
}
sort_population(pop);
ga_genocide_by_fitness(pop, GA_MIN_FITNESS);
/*
* Prepare arrays to store permutations.
*/
permutation = s_malloc(sizeof(int)*pop->size);
ordered = s_malloc(sizeof(int)*pop->size);
for (i=0; i<pop->size;i++)
ordered[i]=i;
plog( LOG_VERBOSE,
"Prior to the first generation, population has fitness scores between %f and %f",
pop->entity_iarray[0]->fitness,
pop->entity_iarray[pop->size-1]->fitness );
/*
* Do all the generations:
*
* Stop when (a) max_generations reached, or
* (b) "pop->generation_hook" returns FALSE.
*/
while ( (pop->generation_hook?pop->generation_hook(generation, pop):TRUE) &&
generation<max_generations )
{
generation++;
pop->generation = generation;
pop->orig_size = pop->size;
plog(LOG_DEBUG,
"Population size is %d at start of generation %d",
pop->orig_size, generation );
sort_population(pop);
random_int_permutation(pop->orig_size, ordered, permutation);
for ( i=0; i<pop->orig_size; i++ )
{
mother = pop->entity_iarray[i];
father = pop->entity_iarray[permutation[i]];
/*
* Crossover step.
*/
plog(LOG_VERBOSE, "Crossover between %d (rank %d fitness %f) and %d (rank %d fitness %f)",
ga_get_entity_id(pop, mother),
ga_get_entity_rank(pop, mother), mother->fitness,
ga_get_entity_id(pop, father),
ga_get_entity_rank(pop, father), father->fitness);
son = ga_get_free_entity(pop);
daughter = ga_get_free_entity(pop);
pop->crossover(pop, mother, father, daughter, son);
/*
* Mutation step.
*/
if (random_boolean_prob(pop->mutation_ratio))
{
plog(LOG_VERBOSE, "Mutation of %d (rank %d)",
ga_get_entity_id(pop, daughter),
ga_get_entity_rank(pop, daughter) );
entity = ga_get_free_entity(pop);
pop->mutate(pop, daughter, entity);
ga_entity_dereference(pop, daughter);
daughter = entity;
}
if (random_boolean_prob(pop->mutation_ratio))
{
plog(LOG_VERBOSE, "Mutation of %d (rank %d)",
ga_get_entity_id(pop, son),
ga_get_entity_rank(pop, son) );
entity = ga_get_free_entity(pop);
pop->mutate(pop, son, entity);
ga_entity_dereference(pop, son);
son = entity;
}
/*
* Apply environmental adaptations, score entities, sort entities, etc.
* FIXME: Currently no adaptation.
*/
pop->evaluate(pop, daughter);
pop->evaluate(pop, son);
/*
* Evaluate similarities.
*/
dist1 = pop->dc_params->compare(pop, mother, daughter) + pop->dc_params->compare(pop, father, son);
dist2 = pop->dc_params->compare(pop, mother, son) + pop->dc_params->compare(pop, father, daughter);
/*
* Determine which entities will survive, and kill the others.
*/
if (dist1 < dist2)
{
rank = ga_get_entity_rank(pop, daughter);
if (daughter->fitness < mother->fitness)
{
entity = pop->entity_iarray[i];
pop->entity_iarray[i] = pop->entity_iarray[rank];
pop->entity_iarray[rank] = entity;
}
ga_entity_dereference_by_rank(pop, rank);
rank = ga_get_entity_rank(pop, son);
if (son->fitness < father->fitness)
{
entity = pop->entity_iarray[permutation[i]];
pop->entity_iarray[permutation[i]] = pop->entity_iarray[rank];
pop->entity_iarray[rank] = entity;
}
ga_entity_dereference_by_rank(pop, rank);
}
else
{
rank = ga_get_entity_rank(pop, son);
if (son->fitness < mother->fitness)
{
entity = pop->entity_iarray[i];
pop->entity_iarray[i] = pop->entity_iarray[rank];
pop->entity_iarray[rank] = entity;
}
ga_entity_dereference_by_rank(pop, rank);
rank = ga_get_entity_rank(pop, daughter);
if (daughter->fitness < father->fitness)
{
entity = pop->entity_iarray[permutation[i]];
pop->entity_iarray[permutation[i]] = pop->entity_iarray[rank];
pop->entity_iarray[rank] = entity;
}
ga_entity_dereference_by_rank(pop, rank);
}
}
/*
* Use callback.
*/
plog(LOG_VERBOSE,
"After generation %d, population has fitness scores between %f and %f",
generation,
pop->entity_iarray[0]->fitness,
pop->entity_iarray[pop->size-1]->fitness );
} /* Generation loop. */
/*
* Ensure final ordering of population is correct.
*/
sort_population(pop);
return generation;
}