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import math
import numpy as np
from cmaes import CMA
def ackley(x1, x2):
return (
-20 * math.exp(-0.2 * math.sqrt(0.5 * (x1**2 + x2**2)))
- math.exp(0.5 * (math.cos(2 * math.pi * x1) + math.cos(2 * math.pi * x2)))
+ math.e
+ 20
)
def main():
seed = 0
rng = np.random.RandomState(0)
bounds = np.array([[-32.768, 32.768], [-32.768, 32.768]])
lower_bounds, upper_bounds = bounds[:, 0], bounds[:, 1]
mean = lower_bounds + (rng.rand(2) * (upper_bounds - lower_bounds))
sigma0 = 32.768 * 2 / 5 # 1/5 of the domain width
sigma = sigma0
optimizer = CMA(mean=mean, sigma=sigma, bounds=bounds, seed=0)
n_restarts = 0 # A small restart doesn't count in the n_restarts
small_n_eval, large_n_eval = 0, 0
popsize0 = optimizer.population_size
inc_popsize = 2
# Initial run is with "normal" population size; it is
# the large population before first doubling, but its
# budget accounting is the same as in case of small
# population.
poptype = "small"
while n_restarts <= 5:
solutions = []
for _ in range(optimizer.population_size):
x = optimizer.ask()
value = ackley(x[0], x[1])
solutions.append((x, value))
# print("{:10.5f} {:6.2f} {:6.2f}".format(value, x[0], x[1]))
optimizer.tell(solutions)
if optimizer.should_stop():
seed += 1
n_eval = optimizer.population_size * optimizer.generation
if poptype == "small":
small_n_eval += n_eval
else: # poptype == "large"
large_n_eval += n_eval
if small_n_eval < large_n_eval:
poptype = "small"
popsize_multiplier = inc_popsize**n_restarts
popsize = math.floor(
popsize0 * popsize_multiplier ** (rng.uniform() ** 2)
)
sigma = sigma0 * 10 ** (-2 * rng.uniform())
else:
poptype = "large"
n_restarts += 1
popsize = popsize0 * (inc_popsize**n_restarts)
sigma = sigma0
mean = lower_bounds + (rng.rand(2) * (upper_bounds - lower_bounds))
optimizer = CMA(
mean=mean,
sigma=sigma,
bounds=bounds,
seed=seed,
population_size=popsize,
)
print("Restart CMA-ES with popsize={} ({})".format(popsize, poptype))
if __name__ == "__main__":
main()
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