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()
def f2(x1, x2):
return (1. * (x2 - (5.1 / (4. * math.pi ** 2)) * x1 ** 2 +
(5. / math.pi) * x1 - 6.) ** 2 + 10. * (1 - 1 / (8 * math.pi)) *
math.cos(x1) + 10.)
def f3()
# TEST NUM. 1 -- f1(x)
g = GA(10, 10, p_mut, p_xover, l_dna)
b_gene = ""
b_fitness = 999999
g.init_population()
for _ in range(g.n_gen):
decimals = [g.in_context(-2., 2., g.to_decimal(g.population[i]), l_dna)
for i in range(g.n_pop)]
scores = [f1(x) for x in decimals]
for i, gene in enumerate(g.population):
if scores[i] < b_fitness:
b_fitness = scores[i]
b_gene = gene
children = []
