Skip to Main content Skip to Navigation
Conference papers

Black-Box Optimization Benchmarking of NEWUOA compared to BIPOP-CMA-ES

Raymond Ros 1 Nikolaus Hansen 1
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
Abstract : In this paper, the performances of the NEW Unconstrained Optimization Algorithm (NEWUOA) on some noiseless functions are compared to those of the BI-POPulation Covariance Matrix Adaptation-Evolution Strategy (BIPOP-CMA-ES). The two algorithms were benchmarked on the BBOB 2009 noiseless function testbed. The comparison shows that NEWUOA outperforms BIPOP-CMA-ES on some functions like the Sphere or the Rosenbrock functions. Also the independent restart procedure used for NEWUOA allows it to perform better than BIPOP-CMA-ES on the Gallagher functions. Nevertheless, BIPOP-CMA-ES is faster and has a better success probability than NEWUOA in reaching target function values smaller than one on all other functions.
Document type :
Conference papers
Complete list of metadata
Contributor : Raymond Ros Connect in order to contact the contributor
Submitted on : Friday, April 16, 2010 - 4:58:49 PM
Last modification on : Thursday, July 8, 2021 - 3:47:55 AM
Long-term archiving on: : Tuesday, September 28, 2010 - 11:46:18 AM


Files produced by the author(s)


  • HAL Id : inria-00473779, version 1



Raymond Ros, Nikolaus Hansen. Black-Box Optimization Benchmarking of NEWUOA compared to BIPOP-CMA-ES. Genetic and Evolutionary Computation Conference 2010, Jul 2010, Portland, OR, United States. ⟨inria-00473779⟩



Les métriques sont temporairement indisponibles