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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(1)
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))
sigma = 32.768 * 2 / 5 # 1/5 of the domain width
optimizer = CMA(mean=mean, sigma=sigma, bounds=bounds, seed=0)
# Multiplier for increasing population size before each restart.
inc_popsize = 2
print(" g f(x1,x2) x1 x2 ")
print("=== ========== ====== ======")
for generation in range(200):
solutions = []
for _ in range(optimizer.population_size):
x = optimizer.ask()
value = ackley(x[0], x[1])
solutions.append((x, value))
print(f"{generation:3d} {value:10.5f} {x[0]:6.2f} {x[1]:6.2f}")
optimizer.tell(solutions)
if optimizer.should_stop():
seed += 1
popsize = optimizer.population_size * inc_popsize
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))
if __name__ == "__main__":
main()
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