def objective_func(self, x):
opts = self.vec_to_opts(x)
print(opts)
# run automata
a = Automata(opts)
a.run()
stats = a.stats()
fitness = np.float64(-stats["score_sum"])
print("Fitness: ", fitness)
# if fitness < self.best_fitness:
# self.best_fitness = fitness
# self.best_automata = a
return fitness
population_size = 30
optimization_steps = 5000
scaling_factor = 0.01
cross_over_rate = 0.5
problem_size = 4
# x = [-0.1, 0.1, -0.1, 1.0]
# y = [1000.0, 1000.0, 1000.0, 1000.0]
# x_c = x.ctypes.data_as(c.POINTER(c.c_double))
# y_c = x.ctypes.data_as(c.POINTER(c.c_double))
bounds = [(-0.5, 0.5), (-0.5, 0.5), (-0.5, 0.5), (-0.5, 0.5), (-0.5, 0.5),
(0.7, 0.99)]
result = de(
opt.objective_func,
bounds,
updating="deferred",
workers=2,
maxiter=optimization_steps,
)
x = result.x
opts = opt.vec_to_opts(x)
a = Automata(opts)
a.run()
print("BestAutomata", a)
print(a.stats())
p = Plotter(a)
p.plot(True) # animate
