def GA(args):
population_size, file_name,scoring_function,generations,mating_pool_size,mutation_rate, \
scoring_args, max_score, prune_population, seed = args
np.random.seed(seed)
random.seed(seed)
population = make_initial_population(population_size, file_name)
scores = sc.calculate_scores(population, scoring_function, scoring_args)
fitness = calculate_normalized_fitness(scores)
high_scores = []
for generation in range(generations):
mating_pool = make_mating_pool(population, fitness, mating_pool_size)
new_population = reproduce(mating_pool, population_size, mutation_rate)
new_scores = sc.calculate_scores(new_population, scoring_function,
scoring_args)
population, scores = sanitize(population + new_population,
scores + new_scores, population_size,
prune_population)
fitness = calculate_normalized_fitness(scores)
high_scores.append((scores[0], Chem.MolToSmiles(population[0])))
if scores[0] >= max_score:
break
return (scores, population, high_scores, generation + 1)
def GA(args):
population_size, file_name,scoring_function,generations,mating_pool_size,mutation_rate, \
scoring_args, max_score, prune_population = args
population = make_initial_population(population_size, file_name)
scores = sc.calculate_scores(population, scoring_function, scoring_args)
fitness = calculate_normalized_fitness(scores)
for generation in range(generations):
mating_pool = make_mating_pool(population, fitness, mating_pool_size)
new_population = reproduce(mating_pool, population_size, mutation_rate)
new_scores = sc.calculate_scores(new_population, scoring_function,
scoring_args)
population, scores = sanitize(population + new_population,
scores + new_scores, population_size,
prune_population)
fitness = calculate_normalized_fitness(scores)
if scores[0] >= max_score:
break
return (scores, population, generation + 1)