In the last three decades, the field of computational intelligence has seen a profusion of
population‐based metaheuristics applied to a variety of problems, where they achieved …

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

One hope of using non-elitism in evolutionary computation is that it aids leaving local
optima. We perform a rigorous runtime analysis of a basic non-elitist evolutionary algorithm …

The benefits of using crossover in crossing fitness gaps have been studied extensively in
evolutionary computation. Recent runtime results show that majority-vote crossover is …

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 …

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 …

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 …

Ant colony optimizers have been successfully used as general-purpose optimization
heuristics. Due to the complicated nature of the random processes that describe the runs of …

Jump functions are the most studied non-unimodal benchmark in the theory of evolutionary
algorithms (EAs). They have significantly improved our understanding of how EAs escape …

The benefits of using crossover in crossing fitness gaps have been studied extensively in
evolutionary computation. Recent runtime results show that majority-vote crossover is …