def compute_fitness(G, W, k, P):
fitness = []
for p in P:
parts = create_partitions(G, W, k, p)
wp = [weight_part(part, W) for part in parts]
fitness.append((p, max(wp) - min(wp)))
return fitness
def driver(G, W, k, MAX_GEN):
n = len(G)
P = []
for i in range(2*n):
temp = range(n)
random.shuffle(temp)
P.append(tuple(temp))
for i in range(MAX_GEN):
fitness = compute_fitness(G, W, k, P)
fitness.sort(key=lambda tup: tup[1])
P = [fitness[j][0] for j in range(n)]
P = crossover(P, k, n)
fitness = compute_fitness(G, W, k, P)
fitness.sort(key=lambda tup: tup[1])
p = fitness[0][0]
return create_partitions(G, W, k, p)
