  Various variants of the well known Covariance Matrix Adaptation Evolution
Strategy (CMA-ES) have been proposed recently, which improve the empirical
performance of the original algorithm by structural modifications. However, in
practice it is often unclear which variation is best suited to the specific
optimization problem at hand. As one approach to tackle this issue, algorithmic
mechanisms attached to CMA-ES variants are considered and extracted as
functional \emph{modules}, allowing for combinations of them. This leads to a
configuration space over ES structures, which enables the exploration of
algorithm structures and paves the way toward novel algorithm generation.
Specifically, eleven modules are incorporated in this framework with two or
three alternative configurations for each module, resulting in $4\,608$
algorithms. A self-adaptive Genetic Algorithm (GA) is used to efficiently
evolve effective ES-structures for given classes of optimization problems,
outperforming any classical CMA-ES variants from literature. The proposed
approach is evaluated on noiseless functions from BBOB suite. Furthermore, such
an observation is again confirmed on different function groups and
dimensionality, indicating the feasibility of ES configuration on real-world
problem classes.
