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Conference papers

Comparison-based Adaptive Strategy Selection with Bandits in Differential Evolution

Álvaro Fialho 1 Raymond Ros 2 Marc Schoenauer 1, 2, 3 Michèle Sebag 1, 2, 3
2 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 : Differential Evolution is a popular powerful optimization algorithm for continuous problems. Part of its efficiency comes from the availability of several mutation strategies that can (and must) be chosen in a problem-dependent way. However, such flexibility also makes DE difficult to be automatically used in a new context. F-AUC-Bandit is a comparison-based Adaptive Operator Selection method that has been proposed in the GA framework. It is used here for the on-line control of DE mutation strategy, thus preserving DE invariance w.r.t. monotonous transformations of the objective function. The approach is comparatively assessed on the BBOB test suite, demonstrating significant improvement on baseline and other Adaptive Strategy Selection approaches, while presenting a very low sensitivity to hyper-parameter setting.
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Contributor : Álvaro Fialho Connect in order to contact the contributor
Submitted on : Sunday, September 12, 2010 - 4:44:03 PM
Last modification on : Thursday, July 8, 2021 - 3:47:59 AM
Long-term archiving on: : Monday, December 13, 2010 - 2:30:42 AM


  • HAL Id : inria-00493005, version 2



Álvaro Fialho, Raymond Ros, Marc Schoenauer, Michèle Sebag. Comparison-based Adaptive Strategy Selection with Bandits in Differential Evolution. 11th International Conference on Parallel Problem Solving From Nature - PPSN, Sep 2010, Krakow, Poland. ⟨inria-00493005v2⟩



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