Parameter control is aimed at realizing performance gains through a dynamic choice of the
parameters which determine the behavior of the underlying optimization algorithm. In the …

The theory of evolutionary computation for discrete search spaces has made significant
progress since the early 2010s. This survey summarizes some of the most important recent …

This chapter collects several probabilistic tools that have proven to be useful in the analysis
of randomized search heuristics. This includes classic material such as the Markov …

The heavy-tailed mutation operator proposed in Doerr et al.(GECCO 2017), called fast
mutation to agree with the previously used language, so far was successfully used only in …

We propose a new way to self-adjust the mutation rate in population-based evolutionary
algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate …

Most evolutionary algorithms have multiple parameters and their values drastically affect the
performance. Due to the often complicated interplay of the parameters, setting these values …

It was recently observed that the (1+(λ, λ)) genetic algorithm can comparably easily escape
the local optimum of the jump functions benchmark. Consequently, this algorithm can …

A predominant topic in the theory of evolutionary algorithms and, more generally, theory of
randomized black-box optimization techniques is running-time analysis. Running-time …

Most evolutionary algorithms used in practice heavily employ crossover. In contrast, the
rigorous understanding of how crossover is beneficial is largely lagging behind. In this work …

The signature is a behavioral-based human characteristics that is extensively used as legal
evidence of identification on bank checks, credit cards, and wills. Developing a good offline …