@article {pub3292,
	title = {Simplify Your Covariance Matrix Adaptation Evolution Strategy},
	author = {Hans-Georg Beyer AND Bernhard Sendhoff},
	year = {2017},
	month = {April},
	abstract = {The standard Covariance Matrix Adaptation Evolution
Strategy (CMA-ES) comprises two evolution paths, one
for the learning of the mutation strength and one for the rank-
1 update of the covariance matrix. In this paper is is shown
that one can approximately transform this algorithm in such
a manner that one of the evolution paths and the covariance
matrix itself disappear. That is, the covariance update and the
covariance matrix square root operations are no longer needed
in this novel so-called Matrix Adaptation (MA) ES. The MAES
performs nearly as well as the original CMA-ES. This
is shown by empirical investigations considering the evolution
dynamics and the empirical expected runtime on a set of standard
test functions. Furthermore, it is shown that the MA-ES can
be used as search engine in a Bi-Population (BiPop) ES. The
resulting BiPop-MA-ES is benchmarked using the BBOB COCO
framework and compared with the performance of the CMA-ESv3.61
production code. It is shown that this new BiPop-MA-ES
{\textendash} while algorithmically simpler {\textendash} performs nearly equally well
as the CMA-ES-v3.61 code.},
	publisher = {IEEE Press},
	journal = {IEEE Transactions on Evolutionary Computation}
}