This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

Evolutionary algorithms have been shown to be very effective in solving complex
optimization problems. This has driven the research community in the development of novel …

Per-instance algorithm selection seeks to recommend, for a given problem instance and a
given performance criterion, one or several suitable algorithms that are expected to perform …

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 …

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

Introducing new algorithmic ideas is a key part of the continuous improvement of existing
optimization algorithms. However, when introducing a new component into an existing …

This open access book provides a wealth of hands-on examples that illustrate how
hyperparameter tuning can be applied in practice and gives deep insights into the working …

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 …

The growing ubiquity of machine learning (ML) has led it to enter various areas of computer
science, including black-box optimization (BBO). Recent research is particularly concerned …

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