def test_ga_optimize_stochastic(self):
"""Verify that the GA is deterministic with same random seed.
"""
var_lower = np.array([-2] * 5)
var_upper = np.array([2] * 5)
integer_vars = np.array([1, 2])
categorical_info = (np.array([]), np.array([0, 1, 2, 3, 4]), [])
settings = RbfoptSettings(ga_base_population_size=100)
state = np.random.get_state()
quad_point = aux.ga_optimize(settings, 5, var_lower, var_upper,
integer_vars, categorical_info, quadratic)
shift_quad_point = aux.ga_optimize(settings, 5, var_lower, var_upper,
integer_vars, categorical_info,
shifted_quadratic)
for i in range(10):
np.random.set_state(state)
point = aux.ga_optimize(settings, 5, var_lower, var_upper,
integer_vars, categorical_info, quadratic)
self.assertAlmostEqual(np.dot(quad_point - point,
quad_point - point),
0,
msg='Obtained different point')
point = aux.ga_optimize(settings, 5, var_lower, var_upper,
integer_vars, categorical_info,
shifted_quadratic)
self.assertAlmostEqual(np.dot(shift_quad_point - point,
shift_quad_point - point),
0,
msg='Obtained different point')
def test_ga_optimize(self):
"""Verify that the genetic algorithm can solve simple problems.
"""
var_lower = np.array([-1] * 3)
var_upper = np.array([1] * 3)
integer_vars = np.array([])
settings = RbfoptSettings(ga_base_population_size = 100)
point = aux.ga_optimize(settings, 3, var_lower, var_upper,
integer_vars, quadratic)
self.assertLessEqual(quadratic([point])[0], 0.05,
msg='Could not solve quadratic with GA')
point = aux.ga_optimize(settings, 3, var_lower, var_upper,
integer_vars, shifted_quadratic)
self.assertLessEqual(shifted_quadratic([point])[0], 0.05,
msg='Could not solve shifted quadratic with GA')