def do_training():
print(genom_struct_path, MEMS_info, cuncurrency)
ga = GeneticAlgorithm(genom_struct_path)
start_time = time.time()
population = ga.run(init_population_size,
population_size,
mutation_rate,
num_iteratitions,
crossover_type,
calc_fitness,
fitness_goal,
cuncurrency=cuncurrency,
reverse_fitness_order=True,
population_np_path=population_np_path,
reload_np_population_rate=reload_np_population_rate)
save_models(population)
end_time = time.time()
print(population[:3].astype(float))
print(population[:, -1].astype(float))
print('Runtime :', end_time - start_time)
from utilities.objective_functions import TSPObjectiveFunction
from genetic_algorithm.genetic_algorithm import GeneticAlgorithm
obj = TSPObjectiveFunction(num_points=20)
solution = obj.min_v
number_of_generations = 50
population_size = 1000
percent_crossover = 0.6
percent_mutation = 0.1
GA = GeneticAlgorithm(objective_function=obj,
experiment_type='iter_lim',
population_size=population_size,
pct_crossover=percent_crossover,
pct_mutation=percent_mutation,
num_iters=number_of_generations)
GA_response = GA.minimize_objective()
print("")
print("GA answer: {}".format(GA_response[0]))
print("Percent Error: {}".format(GA_response[1]))
print("Timing of code: {}".format(GA_response[2]))
print("Correct answer: {}".format(solution))
# Load logger
global logger
logging.config.dictConfig(yaml.load(open('logging.yaml')))
logger = logging.getLogger(GeneticAlgorithm.LOGGER_HANDLER_NAME)
path = 'genom_struct.csv'
init_population_size = 6000
population_size = 100
mutation_rate = 0.20
num_iteratitions = 100
crossover_type = GeneticAlgorithm.TWO_POINT_CROSSOVER
fitness_goal = 0.00001
load_dataset()
ga = GeneticAlgorithm(path)
start_time = time.time()
population = ga.run(init_population_size,
population_size,
mutation_rate,
num_iteratitions,
crossover_type,
calc_fitness,
fitness_goal,
cuncurrency=20,
reverse_fitness_order=False)
save_models(population)
end_time = time.time()
print(population[:3].astype(float))
print(population[:, -1].astype(float))
print('Runtime :', end_time - start_time)
# Build priority Matrix
priority_matrix = build_priority_matrix(args.priority, clients_index, salesman_index)
if args.debug == 1 and not args.priority is None:
priority_matrix = build_priority_matrix_test(clients, salesman, initial_territory)
print(priority_matrix)
# Genetic Algorithm with Configuration
ga = GeneticAlgorithm(
SalesTerritories(salesman, clients,
dist_matrix, priority_matrix, initial_territory,
limit=args.generations,
size=args.init_population,
prob_crossover=args.prob_crossover,
prob_mutation=args.prob_mutation,
local_search=args.local_search,
elitism=args.elitism,
params=params)
)
log("Init Otimization...\n\n")
population = ga.run(args.processes)
# Best Solution
best_territory = ga.best_solution
log("Best Solution: "+str(ga.genetics.fitness(best_territory)))
# History
if __name__ == "__main__":
log_file = "./logs/gen_alg.log"
logger.create_rotating_log(log_file)
log = logger.logger
log.info("----- Starting Genetic Algorithm -----")
champ_info = get_champion_information(
"genetic_algorithm/full_champion_data.json")
enemy_team = create_enemy_team_comp(ENEMY_TEAM, champ_info)
filtered_champ_info = filter_champion_information(ENEMY_TEAM, champ_info)
genetic_algorithm = GeneticAlgorithm(
population=POPULATION,
crossover_rate=CROSSOVER_RATE,
mutation_rate=MUTATION_RATE,
base_team_composition=enemy_team,
champion_information=filtered_champ_info,
meta=META)
run_gen_alg(genetic_algorithm, NUM_GENERATIONS)
print("enemy team")
for champion in enemy_team.champions:
print(f"{champion.id}: {champion.name}")
print("----------------------------------------")
print("\nyour team")
for champion in genetic_algorithm.best_individuals[0].champions:
print(champion.name)
print(champion.matchups)
print("----------------------------------------")