MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

Aymeric Blot (1, 2, 3, 4) , Holger Hoos (4) , Laetitia Jourdan (2, 1) , Marie-Éléonore Marmion (2, 1) , Heike Trautmann (5)

1 Université de Lille
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
3 ENS Rennes - École normale supérieure - Rennes
4 UBC - University of British Columbia
5 WWU - Westfälische Wilhelms-Universität Münster = University of Münster

Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator.

Mots clés: Local Search Algorithms; Algorithm Configuration; Multi-objective Optimisation; Parameter Tuning

Domaines: Recherche opérationnelle [math.OC]

Aymeric Blot, Holger Hoos, Laetitia Jourdan, Marie-Éléonore Marmion, Heike Trautmann. MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. Learning and Intelligent Optimization, May 2016, Ischia, Italy. pp.32-47, ⟨10.1007/978-3-319-50349-3_3⟩. ⟨hal-01370392⟩