Feature Based Algorithm Configuration: A Case Study with Differential Evolution

Nacim Belkhir 1, 2 Johann Dréo 2 Pierre Savéant 2 Marc Schoenauer 1, *
* Corresponding author
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Algorithm Configuration is still an intricate problem especially in the continuous black box optimization domain. This paper empirically investigates the relationship between continuous problem features (measuring different problem characteristics) and the best parameter configuration of a given stochastic algorithm over a bench of test functions — namely here, the original version of Differential Evolution over the BBOB test bench. This is achieved by learning an empirical performance model from the problem features and the algorithm parameters. This performance model can then be used to compute an empirical optimal parameter configuration from features values. The results show that reasonable performance models can indeed be learned, resulting in a better parameter configuration than a static parameter setting optimized for robustness over the test bench.
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Nacim Belkhir, Johann Dréo, Pierre Savéant, Marc Schoenauer. Feature Based Algorithm Configuration: A Case Study with Differential Evolution. Parallel Problem Solving from Nature – PPSN XIV, Sep 2016, Edinburgh, France. pp.156-165, ⟨10.1007/978-3-319-45823-6_15⟩. ⟨hal-01359539⟩

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