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

Aymeric Blot,H. Hoos,Laetitia Vermeulen-Jourdan,Marie-Éléonore Kessaci-Marmion,H. Trautmann

Published 2016 in Learning and Intelligent Optimization

ABSTRACT

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.

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