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;
}
GAULFUNC int ga_sa( population *pop,
entity *initial,
const int max_iterations )
{
int iteration=0; /* Current iteration number. */
entity *putative; /* Current solution. */
entity *best; /* Current solution. */
entity *tmp; /* Used to swap working solutions. */
/* 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->sa_params) die("ga_population_set_sa_params(), or similar, must be used prior to ga_sa().");
/* Prepare working entities. */
putative = ga_get_free_entity(pop);
best = ga_get_free_entity(pop);
/* Do we need to generate a random starting solution? */
if (!initial)
{
plog(LOG_VERBOSE, "Will perform simulated annealling with random starting solution.");
initial = ga_get_free_entity(pop);
ga_entity_seed(pop, best);
}
else
{
plog(LOG_VERBOSE, "Will perform simulated annealling 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.
*/
pop->sa_params->temperature = pop->sa_params->initial_temp;
while ( (pop->iteration_hook?pop->iteration_hook(iteration, best):TRUE) &&
iteration<max_iterations )
{
iteration++;
if (pop->sa_params->temp_freq == -1)
{
pop->sa_params->temperature = pop->sa_params->initial_temp
+ ((double)iteration/max_iterations)
* (pop->sa_params->final_temp-pop->sa_params->initial_temp);
}
else
{
if ( pop->sa_params->temperature > pop->sa_params->final_temp
&& iteration%pop->sa_params->temp_freq == 0 )
{
pop->sa_params->temperature -= pop->sa_params->temp_step;
}
}
/*
* Generate and score a new solution.
*/
pop->mutate(pop, best, putative);
pop->evaluate(pop, putative);
/*
* Use the acceptance criterion to decide whether this new solution should
* be selected or discarded.
*/
if ( pop->sa_params->sa_accept(pop, best, putative) )
{
tmp = best;
best = putative;
putative = tmp;
}
/*
* Save the current best solution in the initial entity, if this
* is now the best found so far.
*/
if ( initial->fitness<best->fitness )
{
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);
ga_entity_dereference(pop, putative);
return iteration;
}
GAULFUNC int ga_random_search( population *pop,
entity *best,
const int max_iterations )
{
int iteration=0; /* Current iteration number. */
entity *putative; /* Current solution. */
entity *tmp; /* Used to swap entities. */
/* Checks. */
if (!pop) die("NULL pointer to population structure passed.");
if (pop->size < 1) die("Population is empty.");
if (!pop->evaluate) die("Population's evaluation callback is undefined.");
if (!pop->seed) die("Population's seed callback is undefined.");
/* Prepare working entity. */
putative = ga_get_free_entity(pop);
/* Do we need to generate a random starting solution? */
if (!best)
{
plog(LOG_VERBOSE, "Will perform random search with random starting solution.");
best = ga_get_free_entity(pop);
ga_entity_seed(pop, best);
}
else
{
plog(LOG_VERBOSE, "Will perform random search with specified starting solution.");
}
/*
* 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 a new solution.
*/
ga_entity_blank(pop, putative);
pop->seed(pop, putative);
pop->evaluate(pop, putative);
/*
* Decide whether this new solution should be selected or discarded based
* on the relative fitnesses.
*/
if ( putative->fitness > best->fitness )
{
tmp = best;
best = putative;
putative = tmp;
}
/*
* 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, putative);
return iteration;
}
GAULFUNC int ga_search( population *pop,
entity *best)
{
int iteration=0; /* Current iteration number. */
entity *putative; /* Current solution. */
entity *tmp; /* Used to swap entities. */
int enumeration=0; /* Enumeration index. */
boolean finished=FALSE; /* Whether search is complete. */
/* Checks. */
if (!pop) die("NULL pointer to population structure passed.");
if (!pop->evaluate) die("Population's evaluation callback is undefined.");
if (!pop->search_params) die("ga_population_set_search_params(), or similar, must be used prior to ga_search().");
if (!pop->search_params->scan_chromosome) die("Population's chromosome scan callback is undefined.");
/* Prepare working entity. */
putative = ga_get_free_entity(pop);
plog(LOG_VERBOSE, "Will perform systematic search.");
/* Do we need to allocate starting solution? */
if (!best)
{
best = ga_get_free_entity(pop);
ga_entity_seed(pop, best);
}
/*
* Ensure that initial solution is scored.
*/
if (best->fitness==GA_MIN_FITNESS) pop->evaluate(pop, best);
/*
* Prepare internal data for the enumeration algorithm.
*/
pop->search_params->chromosome_state = 0;
pop->search_params->allele_state = 0;
/*
* Do all the iterations:
*
* Stop when (a) All enumerations performed or
* (b) "pop->iteration_hook" returns FALSE.
*/
while ( (pop->iteration_hook?pop->iteration_hook(iteration, best):TRUE) &&
finished == FALSE )
{
iteration++;
/*
* Generate and score a new solution.
*/
ga_entity_blank(pop, putative);
finished = pop->search_params->scan_chromosome(pop, putative, enumeration);
pop->evaluate(pop, putative);
/*
* Decide whether this new solution should be selected or discarded based
* on the relative fitnesses.
*/
if ( putative->fitness > best->fitness )
{
tmp = best;
best = putative;
putative = tmp;
}
/*
* 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, putative);
return iteration;
}
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;
}
GAULFUNC entity *ga_allele_search( population *pop,
const int chromosomeid,
const int point,
const int min_val,
const int max_val,
entity *initial )
{
int val; /* Current value for point. */
entity *current, *best; /* The solutions. */
/* Checks. */
/* FIXME: More checks needed. */
if ( !pop ) die("Null pointer to population structure passed.");
current = ga_get_free_entity(pop); /* The 'working' solution. */
best = ga_get_free_entity(pop); /* The best solution so far. */
plog(LOG_WARNING, "ga_allele_search() is a deprecated function!");
/* Do we need to generate a random solution? */
if (initial==NULL)
{
plog(LOG_VERBOSE, "Will perform systematic allele search with random starting solution.");
pop->seed(pop, best);
}
else
{
plog(LOG_VERBOSE, "Will perform systematic allele search.");
ga_entity_copy(pop, best, initial);
}
/*
* Copy best solution over current solution.
*/
ga_entity_copy(pop, current, best);
best->fitness=GA_MIN_FITNESS;
/*
* Loop over the range of legal values.
*/
for (val = min_val; val < max_val; val++)
{
((int *)current->chromosome[chromosomeid])[point] = val;
ga_entity_clear_data(pop, current, chromosomeid);
pop->evaluate(pop, current);
/*
* Should we keep this solution?
*/
if ( best->fitness < current->fitness )
{ /* Copy this solution best solution. */
ga_entity_blank(pop, best);
ga_entity_copy(pop, best, current);
}
else
{ /* Copy best solution over current solution. */
ga_entity_blank(pop, current);
ga_entity_copy(pop, current, best);
}
}
plog(LOG_VERBOSE,
"After complete search the best solution has fitness score of %f",
best->fitness );
/*
* Current no longer needed. It is upto the caller to dereference the
* optimum solution found.
*/
ga_entity_dereference(pop, current);
return best;
}
int ga_steepestascent_double( population *pop,
entity *current,
const int max_iterations )
{
int iteration=0; /* Current iteration number. */
int i; /* Index into arrays. */
double *current_g; /* Current iteration gradient array. */
entity *putative; /* New solution. */
entity *tmpentity; /* Used to swap working solutions. */
double step_size; /* Current step size. */
double grms; /* Current RMS gradient. */
boolean force_terminate=FALSE; /* Force optimisation to terminate. */
/*
* Checks.
*/
if (!pop) die("NULL pointer to population structure passed.");
if (!pop->evaluate) die("Population's evaluation callback is undefined.");
if (!pop->gradient_params) die("ga_population_set_gradient_params(), or similar, must be used prior to ga_gradient().");
if (!pop->gradient_params->gradient) die("Population's first derivatives callback is undefined.");
/*
* Prepare working entity and gradient array.
*/
current_g = s_malloc(sizeof(double)*pop->len_chromosomes);
putative = ga_get_free_entity(pop);
/* Do we need to generate a random starting solution? */
if (current==NULL)
{
plog(LOG_VERBOSE, "Will perform gradient search with random starting solution.");
current = ga_get_free_entity(pop);
ga_entity_seed(pop, current);
}
else
{
plog(LOG_VERBOSE, "Will perform gradient search with specified starting solution.");
}
/*
* Get initial fitness and derivatives.
*/
pop->evaluate(pop, current);
grms = pop->gradient_params->gradient(pop, current, (double *)current->chromosome[0], current_g);
plog( LOG_VERBOSE,
"Prior to the first iteration, the current solution has fitness score of %f and a RMS gradient of %f",
current->fitness, grms );
/*
* Adjust step size based on gradient.
* This scales the step size according to the initial gradient so that the
* calculation doesn't blow-up completely.
*/
/* step_size=(pop->len_chromosomes*pop->gradient_params->step_size)/grms;*/
step_size=pop->gradient_params->step_size;
/*
* Do all the iterations:
*
* Stop when (a) max_iterations reached, or
* (b) "pop->iteration_hook" returns FALSE.
* The iteration hook could evaluate the RMS gradient, or the maximum component
* of the gradient, or any other termination criteria that may be desirable.
*/
while ( force_terminate==FALSE &&
(pop->iteration_hook?pop->iteration_hook(iteration, current):TRUE) &&
iteration<max_iterations )
{
iteration++;
for( i=0; i<pop->len_chromosomes; i++ )
((double *)putative->chromosome[0])[i]=((double *)current->chromosome[0])[i]+step_size*current_g[i];
pop->evaluate(pop, putative);
#if GA_DEBUG>2
printf("DEBUG: current_d = %f %f %f %f\n", current_d[0], current_d[1], current_d[2], current_d[3]);
printf("DEBUG: current_g = %f %f %f %f grms = %f\n", current_g[0], current_g[1], current_g[2], current_g[3], grms);
printf("DEBUG: putative_d = %f %f %f %f fitness = %f\n", putative_d[0], putative_d[1], putative_d[2], putative_d[3], putative->fitness);
#endif
if ( current->fitness > putative->fitness )
{ /* New solution is worse. */
do
{
step_size *= pop->gradient_params->alpha;
/*printf("DEBUG: step_size = %e\n", step_size);*/
for( i=0; i<pop->len_chromosomes; i++ )
((double *)putative->chromosome[0])[i]=((double *)current->chromosome[0])[i]+step_size*current_g[i];
pop->evaluate(pop, putative);
} while( current->fitness > putative->fitness && step_size > ApproxZero);
if (step_size <= ApproxZero && grms <= ApproxZero) force_terminate=TRUE;
}
else
{ /* New solution is an improvement. */
step_size *= pop->gradient_params->beta;
#if GA_DEBUG>2
printf("DEBUG: step_size = %e\n", step_size);
#endif
}
/* Store improved solution. */
tmpentity = current;
current = putative;
putative = tmpentity;
grms = pop->gradient_params->gradient(pop, current, (double *)current->chromosome[0], current_g);
/*
* Use the iteration callback.
*/
plog( LOG_VERBOSE,
"After iteration %d, the current solution has fitness score of %f and RMS gradient of %f (step_size = %f)",
iteration, current->fitness, grms, step_size );
} /* Iteration loop. */
/*
* Cleanup.
*/
ga_entity_dereference(pop, putative);
return iteration;
}
GAULFUNC int ga_random_ascent_hillclimbing( population *pop,
entity *best,
const int max_iterations )
{
int iteration=0; /* Current iteration number. */
entity *putative; /* Current solution. */
entity *tmp; /* Used to swap working solutions. */
int chromo_id; /* Chromosome number. */
int allele_id; /* Allele number. */
/* Checks. */
if (!pop) die("NULL pointer to population structure passed.");
if (!pop->evaluate) die("Population's evaluation callback is undefined.");
if (!pop->climbing_params)
die("ga_population_set_hillclimbing_params(), or similar, must be used prior to ga_random_ascent_hillclimbing().");
if (!pop->climbing_params->mutate_allele)
die("Population's allele mutation callback is undefined.");
/* Prepare working entity. */
putative = ga_get_free_entity(pop);
/* Do we need to generate a random starting solution? */
if (!best)
{
plog(LOG_VERBOSE, "Will perform hill climbing with random starting solution.");
best = ga_get_free_entity(pop);
ga_entity_seed(pop, best);
}
else
{
plog(LOG_VERBOSE, "Will perform hill climbing with specified starting solution.");
}
/*
* 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 a new solution.
*/
chromo_id = random_int(pop->num_chromosomes);
allele_id = random_int(pop->len_chromosomes);
pop->climbing_params->mutate_allele(pop, best, putative, chromo_id, allele_id);
pop->mutate(pop, best, putative);
pop->evaluate(pop, putative);
/*
* Decide whether this new solution should be selected or discarded based
* on the relative fitnesses.
*/
if ( putative->fitness > best->fitness )
{
tmp = best;
best = putative;
putative = tmp;
}
/*
* 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, putative);
return iteration;
}