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import numpy as np
from gradient_free_optimizers import HillClimbingOptimizer
n_iter = 10
def get_score(pos_new):
x1 = pos_new[0]
return -x1 * x1
space_dim = np.array([100])
init_positions = [np.array([10])]
opt = HillClimbingOptimizer(init_positions, space_dim, opt_para={})
for nth_init in range(len(init_positions)):
pos_new = opt.init_pos(nth_init)
score_new = get_score(pos_new)
opt.evaluate(score_new)
for nth_iter in range(len(init_positions), n_iter):
pos_new = opt.iterate(nth_iter)
SimonBlanke /
Gradient-Free-Optimizers
