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
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 : 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.
Type de document :
Communication dans un congrès
11th International Conference on Parallel Problem Solving From Nature - PPSN, Sep 2010, Krakow, Poland. 2010
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https://hal.inria.fr/inria-00493005
Contributeur : Álvaro Fialho <>
Soumis le : dimanche 12 septembre 2010 - 16:44:03
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
Document(s) archivé(s) le : lundi 13 décembre 2010 - 02:30:42

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  • HAL Id : inria-00493005, version 2

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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. 2010. 〈inria-00493005v2〉

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