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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from gradient_free_optimizers import TabuOptimizer
from ._base_test import _base_test
n_iter = 33
opt = TabuOptimizer
def test_epsilon():
for epsilon in [0.00001, 100]:
opt_para = {"epsilon": epsilon}
_base_test(opt, n_iter, opt_para=opt_para)
def test_n_neighbours():
for n_neighbours in [1, 100]:
opt_para = {"n_neighbours": n_neighbours}
def test_tabu_factor():
for tabu_factor in [1, 3, 20, 50, 100]:
opt_para = {"tabu_factor": tabu_factor}
SimonBlanke /
Gradient-Free-Optimizers
