if __name__ == '__main__':
no_of_iterations = int(input("No. of iterations = "))
mutation_probability = float(input("mutation probability = "))
A = [1, 2, 3, 4, 5, 6]
S = [[0, 1], [2, 3], [4, 5]]
S2 = [[0, 1, 2], [3, 4, 5]]
population = Population(get_population(6))
algorithm = Algorithm(A, S)
ft = []
for i in range(no_of_iterations):
population = algorithm.compute(population, mutation_probability)
el = max(population.getAll(), key=lambda x: algorithm.fitness(x))
ft.append(algorithm.fitness(el))
print("Fitness = " + str(algorithm.fitness(el)))
print("Elem = " + str(el))
print("\n")
population.get_population().sort(key=lambda x: algorithm.fitness(x))
fitness_optim, individual_optim = algorithm.fitness(
population.get(0)), population.get(0)
print("Optimum fitness = " + str(fitness_optim))
print("Optimum Individual = " + str(individual_optim))
plot.plot(ft)
plot.ylabel("Fitness")
if __name__ == '__main__':
no_of_iterations = int(input("No. of iterations = "))
mutation_probability = float(input("mutation probability = "))
dim = [1, 3, 2, 2, 5, 4]
colors = [1, 1, 1, 1, 1, 1]
population = Population(compute_random_perm(6))
algorithm = Algorithm(dim, colors)
for i in range(no_of_iterations):
population = algorithm.compute(population, mutation_probability)
population.get_population().sort(key=lambda x: algorithm.fitness(x))
fitness_optim, individual_optim = algorithm.fitness(
population.get(0)), population.get(0)
print("Optimum fitness = " + str(fitness_optim))
print("Optimum Individual = \n")
perm = individual_optim.get_perm()
for i in perm:
print("[" + str(dim[i]) + ", " + str(colors[i]) + "]")
# for i in population.get_population():
# print (algorithm.fitness(i))
