Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
Konstantin Dietrich, Diederick Vermetten, Carola Doerr, Pascal Kerschke · Proceedings of the Genetic and Evolutionary Computation Conference · 2024
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 this generator highlighted its ability to smoothly transition between the component functions, both from a low-level landscape feature perspective, as well as with regard to algorithm performance. This suggests that MA-BBOB-generated functions can be an ideal testbed for automated machine learning methods, such as automated algorithm selection (AAS).