Noisy Optimization Complexity Under Locality Assumption

Jérémie Decock 1, 2 Olivier Teytaud 1, 2
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : In spite of various recent publications on the subject, there are still gaps between upper and lower bounds in evolutionary optimization for noisy objective function. In this paper we reduce the gap, and get tight bounds within logarithmic factors in the case of small noise and no long-distance influence on the objective function.
Complete list of metadatas

Cited literature [33 references]  Display  Hide  Download

https://hal.inria.fr/hal-00755663
Contributor : Jérémie Decock <>
Submitted on : Sunday, April 7, 2013 - 1:52:25 AM
Last modification on : Monday, December 9, 2019 - 5:24:06 PM
Long-term archiving on: Monday, July 8, 2013 - 10:15:08 AM

Files

foga006-decock.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00755663, version 1

Collections

Citation

Jérémie Decock, Olivier Teytaud. Noisy Optimization Complexity Under Locality Assumption. FOGA - Foundations of Genetic Algorithms XII - 2013, Jan 2013, Adelaide, Australia. ⟨hal-00755663⟩

Share

Metrics

Record views

798

Files downloads

350