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# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
from ...converter import Converter
from ...results_manager import ResultsManager
from ...optimizers.base_optimizer import get_n_inits
class BasePopulationOptimizer:
def __init__(
self,
search_space,
initialize={"grid": 4, "random": 2, "vertices": 4},
):
super().__init__()
self.conv = Converter(search_space)
self.results_mang = ResultsManager(self.conv)
self.initialize = initialize
self.eval_times = []
self.iter_times = []
def _iterations(self, positioners):
nth_iter = 0
for p in positioners:
nth_iter = nth_iter + len(p.pos_new_list)
return nth_iter
def _create_population(self, Optimizer):
if isinstance(self.population, int):
population = []
for pop_ in range(self.population):
population.append(
Optimizer(self.conv.search_space, rand_rest_p=self.rand_rest_p)
)
else:
population = self.population
n_inits = get_n_inits(self.initialize)
if n_inits < len(population):
print("\n Warning: Not enough initial positions for population size")
print(" Population size is reduced to", n_inits)
population = population[:n_inits]
return population
def finish_initialization(self):
pass
SimonBlanke /
Gradient-Free-Optimizers
