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 …

Just over a decade ago, the first comprehensive review on the state of benchmarking in
Genetic Programming (GP) analyzed the mismatch between the problems that are used to …

The domain of an optimization problem is seen as one of its most important characteristics.
In particular, the distinction between continuous and discrete optimization is rather impactful …

We present a first proof-of-concept use-case that demonstrates the efficiency of interfacing
the algorithm framework ParadisEO with the automated algorithm configuration tool irace …

The recently proposed MA-BBOB function generator provides a way to create numerical
black-box benchmark problems based on the well-established BBOB suite. Initial studies on …

Dynamic Algorithm Configuration (DAC) tackles the question of how to automatically learn
policies to control parameters of algorithms in a data-driven fashion. This question has …

Applying optimisation techniques such as evolutionary computation to real-world tasks often
requires significant adaptation. However, specific application domains do not typically …

Artificial benchmark datasets are common in both numerical and discrete optimisation
domains. Existing benchmarks cover a broad range of classes of optimisation, but as a …

Decomposition methods are valuable approaches to support the development of divide-and-
conquer metaheuristics. When the problem structure is unknown, such as in black-box …

Given that real-world multi-objective optimization problems are generally constructed by
combining individual functions to be optimized, it seems sensible that benchmark functions …