Comparison-Based Optimizers Need Comparison-Based Surrogates

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 1, 2
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : Taking inspiration from approximate ranking, this paper nvestigates the use of rank-based Support Vector Machine as surrogate model within CMA-ES, enforcing the invariance of the approach with respect to monotonous transformations of the fitness function. Whereas the choice of the SVM kernel is known to be a critical issue, the proposed approach uses the Covariance Matrix adapted by CMA-ES within a Gaussian kernel, ensuring the adaptation of the kernel to the currently explored region of the fitness landscape at almost no computational overhead. The empirical validation of the approach on standard benchmarks, comparatively to CMA-ES and recent surrogate-based CMA-ES, demonstrates the efficiency and scalability of the proposed approach.
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Communication dans un congrès
Parallel Problem Solving from Nature XI (PPSN 2010), Sep 2010, Krakow, Poland. 2010
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Contributeur : Loshchilov Ilya <>
Soumis le : lundi 21 juin 2010 - 16:02:50
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : mercredi 22 septembre 2010 - 18:13:14

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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Comparison-Based Optimizers Need Comparison-Based Surrogates. Parallel Problem Solving from Nature XI (PPSN 2010), Sep 2010, Krakow, Poland. 2010. 〈inria-00493921〉

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