Surrogate Assisted Feature Computation for Continuous Problems

Nacim Belkhir 1, 2 Johann Dréo 1 Pierre Savéant 1 Marc Schoenauer 3, 2, *
* Corresponding author
2 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 : A possible approach to Algorithm Selection and Configuration for continuous black box optimization problems relies on problem features, computed from a set of evaluated sample points. However, the computation of the features proposed in the literature require a rather large number of such sample points, unlikely to be practical for expensive real-world problems. On the other hand, surrogate models have been proposed to tackle the optimization of expensive objective function. It is proposed in this paper to use surrogate models to approximate the values of the features at reasonable computational cost. Two experimental studies are conducted, using the well-known BBOB framework as testbench. First, the effect of sub-sampling is analyzed. Then, a methodology to compute approximate values for the features using a surrogate model is proposed, and validated from the point of view of retrieving BBOB classes. It is shown that when only small computational budgets are available, using surrogate models as proxies to compute the features can be beneficial.
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Nacim Belkhir, Johann Dréo, Pierre Savéant, Marc Schoenauer. Surrogate Assisted Feature Computation for Continuous Problems. LION 10 Learning and Intelligent OptimizatioN Conference, May 2016, Ischia, Italy. To appear. ⟨hal-01303320v2⟩

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