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
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 : 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.
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François-Michel de Rainville, Michèle Sebag, Christian Gagné, Marc Schoenauer, Denis Laurendeau. Sustainable Cooperative Coevolution with a Multi-Armed Bandit. Proc. 15th Genetic and Evolutionary Computation COnference - ACM-GECCO, ACM SIGEVO, Jul 2013, Amsterdam, Netherlands. pp.1517-1524. ⟨hal-01084312⟩

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