A metaheuristic is a collection of algorithmic concepts that can be used to define heuristic
methods applicable to a wide set of optimization problems for which exact/analytical …

Thirty years, 1993–2023, is a huge time frame in science. We address some major
developments in the field of evolutionary algorithms, with applications in parameter …

Large Language Models (LLMs) such as GPT-4 have demonstrated their ability to
understand natural language and generate complex code snippets. This paper introduces a …

Benchmarking and performance analysis play an important role in understanding the
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …

We present IOHexperimenter, the experimentation module of the IOHprofiler project.
IOHexperimenter aims at providing an easy-to-use and customizable toolbox for …

Benchmarking heuristic algorithms is vital to understand under which conditions and on
what kind of problems certain algorithms perform well. In most current research into heuristic …

New contributions in the field of iterative optimisation heuristics are often made in an
iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as …

Extending a recent suggestion to generate new instances for numerical black-box
optimization benchmarking by interpolating pairs of the well-established BBOB functions …

Efficient solving of an unseen optimization problem is related to appropriate selection of an
optimization algorithm and its hyper-parameters. For this purpose, automated algorithm …

Benchmarking plays a major role in the development and analysis of optimization
algorithms. As such, the way in which the used benchmark problems are defined …