def make_next_generation(population, pizza):
mating_pool = population[:int(c_par * P)]
next_generation = []
for _ in range(int(c_rec * P) // 2):
A, B = random.sample(mating_pool, 2)
C, D = A.recombine(B, pizza)
next_generation += [C, D]
next_generation += mating_pool
for _ in range(int(c_mut * P)):
# A = random.choice(next_generation)
A = mating_pool[0]
B = A.copy()
levels = random.choice([1, 2, 3])
B.mutate(pizza, levels)
next_generation.append(B)
for _ in range(int(c_ran * P)):
A = Individual({}, slices, n_col, n_row, L, H)
A.fill_layout(pizza)
next_generation.append(A)
next_generation.sort(key=lambda x: x.efficiency(), reverse=True)
return next_generation
for pos, k in s_list:
_lay[tuple(pos)] = k
A = Individual(_lay, slices, n_col, n_row, L, H)
population.append(A)
return population
pizza, n_row, n_col, L, H = read_setup(inp_fpath)
slices = generate_possible_slices(L, H)
if resume == False:
population = []
for i in range(P):
A = Individual({}, slices, n_col, n_row, L, H)
A.fill_layout(pizza)
population.append(A)
population.sort(key=lambda x: x.efficiency(), reverse=True)
else:
print("Continuing previous optimization.")
population = load_population(
res_path + "generations_backup/G_%s.json" % i2s(i_start, 4), slices,
n_col, n_row, L, H, pizza)
print(len(population), P)
if len(population) < P:
print(
"Loaded population is smaller than the given max population.\nExtending it with random individuals."
)
while len(population) < P:
A = Individual({}, slices, n_col, n_row, L, H)
A.fill_layout(pizza)
