def solve_mimic(problem, problem_name):
start = timer()
best_state, best_fitness, fitness_curve = mimic(problem, curve=True)
end = timer()
plot = plot_curve(problem_name + "-MIMIC", "Iterations", "Fitness",
range(fitness_curve.shape[0]),
np.max(fitness_curve, axis=1)).show()
time = end - start
num_iters = fitness_curve.shape[0]
return best_fitness, time, num_iters, plot
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)
print('The fitness at the best state is: ', best_fitness)
print(f'fitness curve: {fitness_curve}')
final_results_maxs.append(np.max(fitness_curve))
final_results_it.append(np.argmax(fitness_curve))
print('rhc algorithm @ {} iterations'.format(i))
print(best_fitness)
print(end - start)
print(len(c))
times_rhc.append(end - start)
fitns_rhc.append(best_fitness)
# ________________________________________________________________________________________________________________________
# Solve using MIMIC
times_mim = []
fitns_mim = []
for i in iterations:
start = time.time()
best_state, best_fitness, c = mlrose.mimic(problem_fit,
pop_size=200,
keep_pct=0.4,
max_attempts=5,
max_iters=i,
curve=True,
random_state=3,
fast_mimic=True)
end = time.time()
print('MIMIC algorithm @ {} iterations'.format(i))
print(best_fitness)
print(end - start)
print(len(c))
times_mim.append(end - start)
fitns_mim.append(best_fitness)
# ________________________________________________________________________________________________________________________
plot_fitness_curve(x_axis=iterations,
curve_rhc=fitns_rhc,
