The CMA Evolution Strategy: A Comparing Review
                 Nikolaus Hansen

 Derived from the concept of self-adaptation in evolution strategies,
 the CMA (Covariance Matrix Adaptation) adapts the covariance matrix
 of a multi-variate normal search distribution.  The CMA was
 originally designed to perform well with small populations. In this
 review, the argument starts out with large population sizes,
 reflecting recent extensions of the CMA algorithm.  Commonalities and
 differences to continuous Estimation of Distribution Algorithms are
 analyzed.  The aspects of reliability of the estimation, overall step
 size control, and independence from the coordinate system
 (invariance) become particularly important in small populations
 sizes.  Consequently, performing the adaptation task with small
 populations is more intricate.

In Lozano et al, Towards a new evolutionary computation.  Advances on
    estimation of distribution algorithms, pp. 75--102, Springer, 2006.

Erratum: "..., we focus on algorithms with a multivariate normal
  search distribution, where the covariance matrix of the distribution
  \emph{is not restricted to a priori}, e.g., not a diagonal matrix."
  The emphasized phrase must read "is not restricted a priori".