Sustainable Cooperative Coevolution with a Multi-Armed Bandit

François-Michel De Rainville 1 Michèle Sebag 2, 3 Christian Gagné 1 Marc Schoenauer 3, 2 Denis Laurendeau 1
3 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 : This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach re-lies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different popula-tions at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems.
Type de document :
Communication dans un congrès
Christian Blum and Enrique Alba. Proc. 15th Genetic and Evolutionary Computation COnference - ACM-GECCO, Jul 2013, Amsterdam, Netherlands. ACM Press, pp.1517-1524, 2013
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Soumis le : mercredi 19 novembre 2014 - 00:57:09
Dernière modification le : jeudi 5 avril 2018 - 12:30:12
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François-Michel De Rainville, Michèle Sebag, Christian Gagné, Marc Schoenauer, Denis Laurendeau. Sustainable Cooperative Coevolution with a Multi-Armed Bandit. Christian Blum and Enrique Alba. Proc. 15th Genetic and Evolutionary Computation COnference - ACM-GECCO, Jul 2013, Amsterdam, Netherlands. ACM Press, pp.1517-1524, 2013. 〈hal-01084312〉

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