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 …

For genetic algorithms (GAs) using a bit-string representation of length n, the general
recommendation is to take 1/n as mutation rate. In this work, we discuss whether this is …

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 …

Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

Understanding how the time complexity of evolutionary algorithms (EAs) depend on their
parameter settings and characteristics of fitness landscapes is a fundamental problem in …

Abstract The (1+(λ, λ))(1+(λ, λ)) genetic algorithm proposed in Doerr et al.(Theor Comput Sci
567: 87–104, 2015) is one of the few examples for which a super-constant speed-up of the …

It is an ongoing debate whether and how comma selection in evolutionary algorithms helps
to escape local optima. We propose a new benchmark function to investigate the benefits of …

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 …