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CMA-ES

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CMA-ES#

Type of optimizer algorithm: continuous inputs

About#

Covariance Matrix Adaptation - Evolutionary Strategy (CMA-ES) maintains the mean \(\boldsymbol{\mu}\) and the covariance \(\boldsymbol{\Sigma}\) of a Normal distribution, updating it using a subset of the best-performing members at each iteration [Hansen and Ostermeier, 1996].

For an introduction to evolutionary strategies we recommend this blogpost by David Ha.

In our implementation, we use pycma.

How to run#

from poli_baselines.solvers import CMA_ES
from poli.objective_repository import ToyContinuousBlackBox

n_dimensions = 3
population_size = 10

f = ToyContinuousBlackBox(
    n_dimensions=n_dimensions,
)

x0 = np.random.normal(size=(population_size, n_dimensions))
y0 = f(x0)

initial_mean = np.random.normal(size=n_dimensions)
solver = CMA_ES(
    black_box=f,
    x0=x0,
    y0=y0,
    initial_mean=initial_mean,
    initial_sigma=1.0,
    population_size=population_size,
)

solver.solve(max_iter=50)
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