Active Covariance Matrix Adaptation
Active covariance matrix adaptation is a mechanism for accelerating the
convergence of covariance matrix adaptation evolution strategies (CMA-ES).
While "conventional" CMA-ES update their mutation distribution by
enlarging variances in successful search directions, active covariance
matrix adaptation additionally actively decreases variances in especially
unsuccessful directions. This is especially useful for optimisation
problems that contain high-dimensional valleys, where the eigenvalue
spectrum of the local Hessian is dominated by a small number of relatively
large values. We have developed active variants of both
(μ/μ,λ)-CMA-ES and (1+1)-CMA-ES.
G. Jastrebski and D. V. Arnold
evolution strategies through active covariance matrix
IEEE Congress on Evolutionary Computation, Vancouver, 2006.
D. V. Arnold and N. Hansen
matrix adaptation for the (1+1)-CMA-ES
Genetic and Evolutionary Computation Conference, Portland, OR, 2010.
This research is supported through grants from the Natural Sciences and
Engineering Research Council of Canada (NSERC) and the Canada Foundation
for Innovation (CFI).