for i in range(5):
final_results_maxs = []
final_results_it = []
for p in range(2, ps + 1):
print(p)
fitness = mlrose.FlipFlop()
istate = np.array(np.zeros(p), dtype=int)
problem = mlrose.DiscreteOpt(length=p,
fitness_fn=fitness,
maximize=True,
max_val=2)
schedule = mlrose.ExpDecay(init_temp=0.5,
exp_const=0.005,
min_temp=0.001)
best_state, best_fitness, fitness_curve = mlrose.mimic(
problem,
max_attempts=500,
pop_size=500,
keep_pct=0.3,
max_iters=1000,
curve=True)
# Define alternative N-Queens fitness function for maximization problem
# Initialize custom fitness function object
# Define initial state
print('The best state found is: ', best_state)
pop_size=200,
mutation_prob=0.1,
curve=True,
max_iters=i,
random_state=3)
end = time.time()
print('genetic algorithm @ {} iterations'.format(i))
print(best_fitness)
print(end - start)
print(len(c))
times_ga.append(end - start)
fitns_ga.append(best_fitness)
# ________________________________________________________________________________________________________________________
# Define decay schedule
schedule = mlrose.ExpDecay()
times_sa = []
fitns_sa = []
# Solve problem using simulated annealing
for i in iterations:
start = time.time()
best_state, best_fitness, c = mlrose.simulated_annealing(problem_fit,
schedule=schedule,
max_attempts=10,
max_iters=i,
init_state=None,
curve=True,
random_state=3)
end = time.time()
print('sa algorithm @ {} iterations'.format(i))
print(best_fitness)