def eval_ga(row, arguments, fit_val):
start=time.time()
args=arguments
path_old = row[2]
xpath_old = row[4]
path_new = row[3]
xpath_new = row[5]
fit_val_temp=fit_val # *, tag, position, class, id
ga = GA(
pop_size=args.p,
eval_lim=args.f,
mut_rate=args.r,
mut_k=args.k,
crossover_rate=args.c,
tournament_k=args.t,
use_elitism=args.e,
use_lin_ranking=args.l,
dom_filepath=path_old.strip(),
xpath=xpath_old,
fitness_values=fit_val_temp,
verbose=args.s
)
ga.select_parents()
ga.evolve()
xpath_robust = ga.optimum.xpath
xpath_fitness = ga.optimum.fitness
end=time.time()
# print("{} seconds elapsed.".format(round(end-start,2)))
return xpath_robust, xpath_fitness
def ga_p(city_list):
cfg = GACfg(gene_len=25,
gene_size=25,
pop_size=1000,
elite_size=50,
tournament_num=50,
tournament_size=100,
mutation_rate=0.03,
repeatable=False)
ga = GA(fitness_func, cfg)
ga.prepare_kwargs(city_list=city_list)
ga.init_population()
progress = []
progress.append(1 / ga.cal_fitness()[0][1])
for _ in range(100):
early_stop = ga.evolve()
progress.append(1 / ga.cal_fitness()[0][1])
if early_stop:
break
print("Final distance: " + str(1 / ga.cal_fitness()[0][1]))
plt.plot(progress)
plt.ylabel('Distance')
plt.xlabel('Generation')
plt.show()
from ga import GA import random arr = [[(-1 if random.randint(0, 1) == 0 else 1) for i in range(30)] for i in range(100)] data = [] for row in arr : decision = -1 if random.randint(0, 1) == 0 else 1 row[0] = decision data.append( (True if decision==1 else False, row) ) print(data) ###### host = GA(data=data, poolSize=10) best = host.evolve(stableFactor=100, breedRate=0.3, mutateRate=0.2, mutationDegree=0.05, mutationComplexity=0.3) print(best)
file = row[0]
xpath = row[1]
result.append(file)
result.append(xpath)
for fit_val in fit_vals:
ga = GA(
pop_size=args.p,
eval_lim=args.f,
mut_rate=args.r,
mut_k=args.k,
crossover_rate=args.c,
tournament_k=args.t,
use_elitism=args.e,
use_lin_ranking=args.l,
dom_filepath=file,
xpath=xpath,
fitness_values=fit_val,
verbose=args.s
)
ga.select_parents()
ga.evolve()
result.append(ga.optimum.xpath)
results.append(result)
with open("results.csv", "w", newline='') as csvfile:
writer = csv.writer(csvfile)
for row in results:
writer.writerow(row)
