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

Anne Auger 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
Abstract : 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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://hal.inria.fr/inria-00430515
Contributor : Anne Auger <>
Submitted on : Sunday, November 8, 2009 - 1:39:52 PM
Last modification on : Thursday, April 5, 2018 - 12:30:12 PM
Long-term archiving on : Thursday, June 17, 2010 - 7:44:48 PM

File

wk2037-auger.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00430515, version 1

Collections

Citation

Anne Auger. Benchmarking the (1+1) Evolution Strategy with One-Fifth Success Rule on the BBOB-2009 Function Testbed. ACM-GECCO Genetic and Evolutionary Computation Conference, Jul 2009, Montreal, Canada. ⟨inria-00430515⟩

Share

Metrics

Record views

1104

Files downloads

2333