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 …

Very recently, the first mathematical runtime analyses of the multiobjective evolutionary
optimizer nondominated sorting genetic algorithm II (NSGA-II) have been conducted. We …

We present a day-ahead scheduling strategy for an Energy Storage System (ESS) in a
microgrid using two algorithms-Genetic Algorithm (GA) and Particle Swarm Optimization …

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 …

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 …

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 …

Two mechanisms have recently been proposed that can significantly speed up finding
distant improving solutions via mutation, namely using a random mutation rate drawn from a …

One of the first and easy to use techniques for proving run time bounds for evolutionary
algorithms is the so-called method of fitness levels by Wegener. It uses a partition of the …

While the theoretical analysis of evolutionary algorithms (EAs) has made significant
progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic …

Choosing a suitable algorithm from the myriads of different search heuristics is difficult when
faced with a novel optimization problem. In this work, we argue that the purely academic …