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Empirical comparisons of several derivative free optimization algorithms

Anne Auger 1, 2 Nikolaus Hansen 2, 1 Jorge Perez Zerpa 2 Raymond Ros 2 Marc Schoenauer 2, 1
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 : In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimization algorithm, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and a Particle Swarm Optimization (PSO) algorithm are compared experimentally on benchmark functions reflecting important challenges encountered in real-world optimization problems. Dependence of the performances in the conditioning of the problem and rotational invariance of the algorithms are in particular investigated.
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Anne Auger, Nikolaus Hansen, Jorge Perez Zerpa, Raymond Ros, Marc Schoenauer. Empirical comparisons of several derivative free optimization algorithms. 9e Colloque national en calcul des structures, CSMA, May 2009, Giens, France. ⟨hal-01408402⟩

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