Result: Black-Box Optimization Benchmarking of NEWUOA compared to BIPOP-CMA-ES
Title:
Black-Box Optimization Benchmarking of NEWUOA compared to BIPOP-CMA-ES
Authors:
Contributors:
Machine Learning and Optimisation (TAO), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Saclay, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Source:
Genetic and Evolutionary Computation Conference 2010, Jul 2010, Portland, OR, United States
Publisher Information:
CCSD, 2010.
Publication Year:
2010
Collection:
collection:EC-PARIS
collection:CNRS
collection:INRIA
collection:UNIV-PSUD
collection:INRIA-SACLAY
collection:INRIA_TEST
collection:TESTALAIN1
collection:UMR8623
collection:INRIA2
collection:LRI-AO
collection:UNIV-PARIS-SACLAY
collection:UNIV-PSUD-SACLAY
collection:INRIA-300009
collection:CNRS
collection:INRIA
collection:UNIV-PSUD
collection:INRIA-SACLAY
collection:INRIA_TEST
collection:TESTALAIN1
collection:UMR8623
collection:INRIA2
collection:LRI-AO
collection:UNIV-PARIS-SACLAY
collection:UNIV-PSUD-SACLAY
collection:INRIA-300009
Subject Terms:
ACM: G.: Mathematics of Computing, G.1: NUMERICAL ANALYSIS, G.1.6: Optimization, G.1.6.2: Global optimization, G.1.6.11: Unconstrained optimization, ACM: F.: Theory of Computation, F.2: ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, F.2.1: Numerical Algorithms and Problems, [INFO.INFO-NA]Computer Science [cs], Numerical Analysis [cs.NA]
Subject Geographic:
Original Identifier:
HAL:
Document Type:
Conference
conferenceObject<br />Conference papers
Language:
English
Access URL:
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.inria.00473779v1
Database:
HAL
Further Information
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.