• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • LAMSADE (UMR CNRS 7243)
  • LAMSADE : Publications
  • View Item
  •   BIRD Home
  • LAMSADE (UMR CNRS 7243)
  • LAMSADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail

Asymptotic convergence rates for averaging strategies

Meunier, Laurent; Legheraba, Iskander; Chevaleyre, Yann; Teytaud, Olivier (2021), Asymptotic convergence rates for averaging strategies, in Finck, Steffen, Foundations of Genetic Algorithms, ACM - Association for Computing Machinery : New York, NY, p. 1–11. 10.1145/3450218.3477302

View/Open
2108.04707.pdf (1.189Mb)
Type
Communication / Conférence
Date
2021
Conference title
FOGA '21: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
Conference date
2021-09
Conference country
Austria
Book title
Foundations of Genetic Algorithms
Book author
Finck, Steffen
Publisher
ACM - Association for Computing Machinery
Published in
New York, NY
ISBN
978-1-4503-8352-3
Pages
1–11
Publication identifier
10.1145/3450218.3477302
Metadata
Show full item record
Author(s)
Meunier, Laurent
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Legheraba, Iskander
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Chevaleyre, Yann
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Teytaud, Olivier
Abstract (EN)
Parallel black box optimization consists in estimating the optimum of a function using λ parallel evaluations of f. Averaging the μ best individuals among the λ evaluations is known to provide better estimates of the optimum of a function than just picking up the best. In continuous domains, this averaging is typically just based on (possibly weighted) arithmetic means. Previous theoretical results were based on quadratic objective functions. In this paper, we extend the results to a wide class of functions, containing three times continuously differentiable functions with unique optimum. We prove formal rate of convergences and show they are indeed better than pure random search asymptotically in λ. We validate our theoretical findings with experiments on some standard black box functions.
Subjects / Keywords
Black-Box; Randomized Search Heuristics; Design of Experiments; Parallel Optimization; Evolutionary Computation

Related items

Showing items related by title and author.

  • Thumbnail
    On Averaging the Best Samples in Evolutionary Computation 
    Meunier, Laurent; Chevaleyre, Yann; Rapin, J.; Royer, Clément; Teytaud, O. (2020) Communication / Conférence
  • Thumbnail
    On Averaging the Best Samples in Evolutionary Computation 
    Meunier, Laurent; Chevaleyre, Yann; Rapin, J.; Royer, Clément; Teytaud, O. (2020) Communication / Conférence
  • Thumbnail
    Mixed Nash Equilibria in the Adversarial Examples Game 
    Meunier, Laurent; Scetbon, Meyer; Pinot, Rafael; Atif, Jamal; Chevaleyre, Yann (2021) Document de travail / Working paper
  • Thumbnail
    Rate of convergence to an asymptotic profile for the self-similar fragmentation and growth-fragmentation equations 
    Mischler, Stéphane; Cañizo, José Alfredo; Caceres, Maria J. (2011) Article accepté pour publication ou publié
  • Thumbnail
    Advocating for Multiple Defense Strategies against Adversarial Examples 
    Araujo, Alexandre; Meunier, Laurent; Pinot, Rafael; Negrevergne, Benjamin (2020) Communication / Conférence
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo