Variance reduction in population-based optimization: Application to unit commitment

Jean Joseph CHRISTOPHE, Jérémie DECOCK, Jialin LIU*, Olivier TEYTAUD

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

2 Citations (Scopus)

Abstract

We consider noisy optimization and some traditional variance reduction techniques aimed at improving the convergence rate, namely (i) common random numbers (CRN), which is relevant for population-based noisy optimization and (ii) stratified sampling, which is relevant for most noisy optimization problems. We present artificial models of noise for which common random numbers are very efficient, and artificial models of noise for which common random numbers are detrimental. We then experiment on a desperately expensive unit commitment problem. As expected, stratified sampling is never detrimental. Nonetheless, in practice, common random numbers provided, by far, most of the improvement.

Original languageEnglish
Title of host publicationArtificial Evolution : 12th International Conference, Evolution Artificielle, EA 2015, Revised Selected Papers
EditorsStéphane BONNEVAY, Pierrick LEGRAND, Nicolas MONMARCHÉ, Evelyne LUTTON, Marc SCHOENAUER
PublisherSpringer-Verlag Italia Srl
Pages219-233
Number of pages15
ISBN (Print)9783319314709
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event12th International Conference on Evolution Artificielle, EA 2015 - Lyon, France
Duration: 26 Oct 201528 Oct 2015

Publication series

NameLecture Notes in Computer Science
Volume9554
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
PublisherSrpinger
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029

Conference

Conference12th International Conference on Evolution Artificielle, EA 2015
Country/TerritoryFrance
CityLyon
Period26/10/1528/10/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

  • Common random numbers
  • Noisy optimization
  • Stratified sampling
  • Variance reduction

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