Trying to optimize parameters with another genetic algo error

[goking1234]

import numpy as np


def schaffer(p):

    x1, x2, x3 = p
    x = np.square(x1) + np.square(x2)
    return -(2*x1+x2+x3)

def second_level(z):
    z1 =z[0]
    z2=z[1]
    omg = GA(func=schaffer, n_dim=3, size_pop=z1, max_iter=z2, lb=[-5, -5, -5], ub=[5,5,5], precision=1e-2)
    best_x, best_y = omg.run()
    result=float(best_y[0])
    print(result)
    return result


from sko.GA import GA

ga = GA(func=second_level, n_dim=2, size_pop=10, max_iter=20, lb=[10, 10], ub=[20, 20], precision=1e-2)
best_x, best_y= ga.run()


print('best_x:', best_x, '\n', 'best_y:', best_y)

gives File "mtrand.pyx", line 741, in numpy.random.mtrand.RandomState.randint File "_bounded_integers.pyx", line 1362, in numpy.random._bounded_integers._rand_int32 TypeError: 'numpy.float64' object cannot be interpreted as an integer no matter what i try to do.

I want to make this recursive up to level n(ga3 to optimize level 2 and ga4 to optimze ga4 and so on). Please help

[guofei9987]

import numpy as np


def schaffer(p):
    x1, x2, x3 = p
    x = np.square(x1) + np.square(x2)
    return -(2 * x1 + x2 + x3)


def second_level(z):
    z1 = int(z[0])
    z2 = int(z[1])
    omg = GA(func=schaffer, n_dim=3, size_pop=z1, max_iter=z2, lb=[-5, -5, -5], ub=[5, 5, 5], precision=1e-2)
    best_x, best_y = omg.run()
    result = float(best_y[0])
    print(result)
    return result


from sko.GA import GA

ga = GA(func=second_level, n_dim=2, size_pop=10, max_iter=20, lb=[10, 10], ub=[20, 20], precision=[2, 1])
best_x, best_y = ga.run()

print('best_x:', best_x, '\n', 'best_y:', best_y)

[goking1234]

Thanks!