  Estimation-of-distribution algorithms (EDAs) are general metaheuristics used
in optimization that represent a more recent alternative to classical
approaches like evolutionary algorithms. In a nutshell, EDAs typically do not
directly evolve populations of search points but build probabilistic models of
promising solutions by repeatedly sampling and selecting points from the
underlying search space. Recently, there has been made significant progress in
the theoretical understanding of EDAs. This article provides an up-to-date
overview of the most commonly analyzed EDAs and the most recent theoretical
results in this area. In particular, emphasis is put on the runtime analysis of
simple univariate EDAs, including a description of typical benchmark functions
and tools for the analysis. Along the way, open problems and directions for
future research are described.
