def main():
# run algorithm & write results to csv
for s in range(SAMPLE_SIZE):
GA.main(SAMPLE_NAME + str(s))
# init averages samples csv file
GA_csv.init(SAMPLE_NAME)
# create averaged results csv from samples
average_samples()
# cleanup sample files
for s in range(SAMPLE_SIZE):
os.remove(SAMPLE_NAME + str(s) + '.csv')
def main(argv=''):
global CSV_NAME
if argv != '':
CSV_NAME = argv
GA_csv.init(CSV_NAME)
run(generation_limit, P_size, G_size, C_rate, M_rate)
if new_population[p].fitness < new_population[n_worst].fitness:
n_worst = p
# replace least fit in new_population with fittest in new_population
for p in range(len(old_population)):
if p == n_worst and old_population[o_best].fitness > new_population[n_worst].fitness:
genome = old_population[o_best].genome
else:
genome = new_population[p].genome
survivors.append(Solution(genome))
return survivors
# ===========
# entry point
# ===========
if __name__ == '__main__':
if (len(sys.argv) >= 2):
CSV_NAME = sys.argv[1]
GA_csv.init(CSV_NAME)
run(generation_limit, P_size, G_size, C_rate, M_rate)
def main(argv=''):
global CSV_NAME
if argv != '':
CSV_NAME = argv
GA_csv.init(CSV_NAME)
run(generation_limit, P_size, G_size, C_rate, M_rate)