  The theory of evolutionary computation for discrete search spaces has made
significant progress in the last ten years. This survey summarizes some of the
most important recent results in this research area. It discusses fine-grained
models of runtime analysis of evolutionary algorithms, highlights recent
theoretical insights on parameter tuning and parameter control, and summarizes
the latest advances for stochastic and dynamic problems. We regard how
evolutionary algorithms optimize submodular functions and we give an overview
over the large body of recent results on estimation of distribution algorithms.
Finally, we present the state of the art of drift analysis, one of the most
powerful analysis technique developed in this field.
