Apart from few exceptions, the mathematical runtime analysis of evolutionary algorithms is
mostly concerned with expected runtimes, occasionally augmented by tail bounds. In this …

Runtime analysis aims at contributing to our understanding of evolutionary algorithms
through mathematical analyses of their runtimes. In the context of discrete optimization …

Apart from few exceptions, the mathematical runtime analysis of evolutionary algorithms is
mostly concerned with expected runtimes. In this work, we argue that stochastic domination …

Randomized search heuristics are frequently applied to NP-hard combinatorial optimization
problems. The runtime analysis of randomized search heuristics has contributed …

Clustering is a fundamental problem in many areas, which aims to partition a given data set
into groups based on some distance measure, such that the data points in the same group …

Algorithm configurators are automated methods to optimise the parameters of an algorithm
for a class of problems. We evaluate the performance of a simple random local search …

Theoretical and empirical research on evolutionary computation methods complement each
other by providing two fundamentally different approaches towards a better understanding of …

Understanding which function classes are easy and which are hard for a given algorithm is a
fundamental question for the analysis and design of bio-inspired search heuristics. A natural …

In studying randomized search heuristics, a frequent quantity of interest is the first time a
(real-valued) stochastic process obtains (or passes) a certain value. The processes under …

When stochastic search heuristics are used for optimisation they are often stopped after
some time has passed and the best search point they have found at this point is used as the …