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 …

Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms
can provably outperform static settings in evolutionary algorithms for binary search spaces …

In classical runtime analysis it has been observed that certain working principles of an
evolutionary algorithm cannot be understood by only looking at the asymptotic order of the …

In the first and so far only mathematical runtime analysis of an estimation-of-distribution
algorithm (EDA) on a multimodal problem, Hasenöhrl and Sutton (GECCO 2018) showed for …

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 …

For the (1+(λ, λ)) genetic algorithm rigorous runtime analyses on unimodal fitness functions
have shown that it can be faster than classical evolutionary algorithms, though on these …

We prove that the compact genetic algorithm (cGA) with hypothetical population size [MATH
HERE] poly (n) with high probability finds the optimum of any n-dimensional jump function …

Abstract The (1+(λ, λ)) genetic algorithm is a younger evolutionary algorithm trying to profit
also from inferior solutions. Rigorous runtime analyses on unimodal fitness functions …

In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that
the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal …

In the first runtime analysis of an estimation-of-distribution algorithm (EDA) on the
multimodal jump function class, Hasenöhrl and Sutton (GECCO 2018) proved that the …