Experimental Comparisons of Derivative Free Optimization Algorithms

Anne Auger 1, 2 Nikolaus Hansen 2 Jorge Perez Zerpa 1 Raymond Ros 1 Marc Schoenauer 1, 2
1 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
2 MSR - INRIA - Microsoft Research - Inria Joint Centre
Abstract : In this paper, the performances of the quasi-Newton BFGS algorithm, the NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm Optimizers (PSO) 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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Submitted on : Sunday, April 4, 2010 - 2:42:18 PM
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  • HAL Id : inria-00397334, version 3
  • ARXIV : 1005.5631

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Anne Auger, Nikolaus Hansen, Jorge Perez Zerpa, Raymond Ros, Marc Schoenauer. Experimental Comparisons of Derivative Free Optimization Algorithms. 8th International Symposium on Experimental Algorithms, Jun 2009, Dortmund, Germany. ⟨inria-00397334v3⟩

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