Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks

Amine Aziz-Alaoui (1) , Carola Doerr (2, 3, 4) , Johann Dreo (4)

1 ISAE-SUPAERO - Institut Supérieur de l'Aéronautique et de l'Espace
2 CNRS - Centre National de la Recherche Scientifique
3 RO - Recherche Opérationnelle
4 SU - Sorbonne Université

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 and the experimental platform IOHprofiler. By combing these three tools, we obtain a powerful benchmarking environment that allows us to systematically analyze large classes of algorithms on complex benchmark problems. Key advantages of our pipeline are fast evaluation times, the possibility to generate rich data sets to support the analysis of the algorithms, and a standardized interface that can be used to benchmark very broad classes of sampling-based optimization heuristics. In addition to enabling systematic algorithm configuration studies, our approach paves a way for assessing the contribution of new ideas in interplay with already existing operatorsa promising avenue for our research domain, which at present may have a too strong focus on comparing entire algorithm instances.

Domaines: Réseau de neurones [cs.NE]

Amine Aziz-Alaoui, Carola Doerr, Johann Dreo. Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks. Genetic and Evolutionary Computation Conference (GECCO 2021), Jul 2021, Lille, France. ⟨10.1145/3449726.3463155⟩. ⟨hal-03233932⟩