Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata
Contributor : Loshchilov Ilya Connect in order to contact the contributor
Submitted on : Monday, June 21, 2010 - 4:02:50 PM
Last modification on : Thursday, July 8, 2021 - 3:47:54 AM
Long-term archiving on: : Wednesday, September 22, 2010 - 6:13:14 PM


Files produced by the author(s)


  • HAL Id : inria-00493921, version 1



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. ⟨inria-00493921⟩



Les métriques sont temporairement indisponibles