Automated benchmarking environments aim to support researchers in understanding how
different algorithms perform on different types of optimization problems. Such comparisons …

Oftentimes the characteristics of real-world engineering optimization problems are not well
understood. In this paper, we introduce an approach for characterizing highly nonlinear and …

Recent theoretical studies have shown that self-adjusting mechanisms can provably
outperform the best static parameters in evolutionary algorithms on discrete problems …

Within the optimization community, the question of how to generate new optimization
problems has been gaining traction in recent years. Within topics such as instance space …

Solving real-world engineering optimization problems, such as automotive crashworthiness
optimization, is extremely challenging, because the problem characteristics are oftentimes …

This study critically examines the Raven Roost Optimization (RRO) algorithm within the
broader context of nature-inspired metaheuristics, challenging its novelty and efficacy in the …

In landscape-aware algorithm selection problem, the effectiveness of feature-based
predictive models strongly depends on the representativeness of training data for practical …

Metaheuristics employ a variety of different components using a wide array of operators to
execute their search. This determines their intensification, diversification and all other …

Evolutionary algorithms (EAs) are general-purpose optimisers that come with several
parameters like the sizes of parent and offspring populations or the mutation rate. It is well …