• Benchmarking the (1+1) Evoluti...

Result: Benchmarking the (1+1) Evolution Strategy with One-Fifth Success Rule on the BBOB-2009 Function Testbed

Title:
Benchmarking the (1+1) Evolution Strategy with One-Fifth Success Rule on the BBOB-2009 Function Testbed
Authors:
Auger, Anne
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), Microsoft Research - Inria Joint Centre (MSR - INRIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Microsoft Research Laboratory Cambridge-Microsoft Corporation [Redmond, Wash.]
Source:
ACM-GECCO Genetic and Evolutionary Computation Conference, Jul 2009, Montreal, Canada
Publisher Information:
CCSD, 2009.
Publication Year:
2009
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-ETATSUNIS
collection:INRIA-ROYAUMEUNI
Subject Terms:
ACM: G.: Mathematics of Computing, G.1: NUMERICAL ANALYSIS, G.1.6: 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:
Montreal, Canada
Original Identifier:
HAL:
Document Type:
Conference conferenceObject<br />Conference papers
Language:
English
Access URL:
https://inria.hal.science/inria-00430515
https://inria.hal.science/inria-00430515v1/document
https://inria.hal.science/inria-00430515v1/file/wk2037-auger.pdf
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.inria.00430515v1
Database:
HAL

Further Information

In this paper, we benchmark the (1+1) Evolution Strategy (ES) with one-fifth success rule which is one of the first and simplest adaptive search algorithms proposed for optimization. The benchmarking is conducted on the noise-free BBOB-2009 testbed. We implement a restart version of the algorithm and conduct for each run $10^{6}$ times the dimension of the search space function evaluations.