  This work provides an efficient sampling method for the covariance matrix
adaptation evolution strategy (CMA-ES) in large-scale settings. In contract to
the Gaussian sampling in CMA-ES, the proposed method generates mutation vectors
from a mixture model, which facilitates exploiting the rich variable
correlations of the problem landscape within a limited time budget. We analyze
the probability distribution of this mixture model and show that it
approximates the Gaussian distribution of CMA-ES with a controllable accuracy.
We use this sampling method, coupled with a novel method for mutation strength
adaptation, to formulate the mixture model based evolution strategy (MMES) -- a
CMA-ES variant for large-scale optimization. The numerical simulations show
that, while significantly reducing the time complexity of CMA-ES, MMES
preserves the rotational invariance, is scalable to high dimensional problems,
and is competitive against the state-of-the-arts in performing global
optimization.
