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

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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Contributor : Aymeric Blot Connect in order to contact the contributor
Submitted on : Thursday, September 22, 2016 - 2:31:14 PM
Last modification on : Friday, December 18, 2020 - 5:30:03 PM


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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⟩



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