Exploring the MLDA benchmark on the nevergrad platform
Jérémy Rapin, Marcus Gallagher, Pascal Kerschke, Mike Preuß, Olivier Teytaud · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2019
This work presents the integration of the recently released benchmark suite MLDA into Nevergrad, a likewise recently released platform for derivative-free optimization. Benchmarking evolutionary and other optimization methods on this collection enables us to learn how algorithms deal with problems that are often treated by means of standard methods like clustering or gradient descent. As available computation power nowadays allows for running much 'slower' methods without noticing a performance difference it is an open question which of these standard methods may be replaced by derivative-free and (in terms of quality) better performing optimization algorithms. Additionally, most MLDA problems are suitable for landscape analysis and other means of understanding problem difficulty or algorithm behavior, due to their tangible nature.