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

Jérémie DECOCK; Jialin LIU; Olivier TEYTAUD

doi:10.1145/2739482.2764638

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

Jérémie DECOCK, Jialin LIU, Olivier TEYTAUD

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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 nonetheless provided, by far, most of the improvement.

Original language	English
Title of host publication	GECCO 2015 : Companion Publication of the 2015 Genetic and Evolutionary Computation Conference
Editors	Sara SILVA
Publisher	Association for Computing Machinery, Inc
Pages	1377-1378
Number of pages	2
ISBN (Electronic)	9781450334884
DOIs	https://doi.org/10.1145/2739482.2764638
Publication status	Published - 11 Jul 2015
Externally published	Yes
Event	17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain Duration: 11 Jul 2015 → 15 Jul 2015

Conference

Conference	17th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/Territory	Spain
City	Madrid
Period	11/07/15 → 15/07/15

Keywords

Common random numbers
Noisy optimization
Stratified sampling
Variance reduction

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10.1145/2739482.2764638

Cite this

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title = "Variance reduction in population-based optimization: Application to unit commitment",

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 nonetheless provided, by far, most of the improvement.",

keywords = "Common random numbers, Noisy optimization, Stratified sampling, Variance reduction",

author = "J{\'e}r{\'e}mie DECOCK and Jialin LIU and Olivier TEYTAUD",

year = "2015",

month = jul,

day = "11",

doi = "10.1145/2739482.2764638",

language = "English",

pages = "1377--1378",

editor = "Sara SILVA",

booktitle = "GECCO 2015 : Companion Publication of the 2015 Genetic and Evolutionary Computation Conference",

publisher = "Association for Computing Machinery, Inc",

note = "17th Genetic and Evolutionary Computation Conference, GECCO 2015 ; Conference date: 11-07-2015 Through 15-07-2015",

}

DECOCK, J, LIU, J & TEYTAUD, O 2015, Variance reduction in population-based optimization: Application to unit commitment. in S SILVA (ed.), GECCO 2015 : Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. Association for Computing Machinery, Inc, pp. 1377-1378, 17th Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11/07/15. https://doi.org/10.1145/2739482.2764638

Variance reduction in population-based optimization: Application to unit commitment. / DECOCK, Jérémie; LIU, Jialin; TEYTAUD, Olivier.
GECCO 2015 : Companion Publication of the 2015 Genetic and Evolutionary Computation Conference. ed. / Sara SILVA. Association for Computing Machinery, Inc, 2015. p. 1377-1378.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - Variance reduction in population-based optimization: Application to unit commitment

AU - DECOCK, Jérémie

AU - LIU, Jialin

AU - TEYTAUD, Olivier

PY - 2015/7/11

Y1 - 2015/7/11

N2 - 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 nonetheless provided, by far, most of the improvement.

AB - 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 nonetheless provided, by far, most of the improvement.

KW - Common random numbers

KW - Noisy optimization

KW - Stratified sampling

KW - Variance reduction

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U2 - 10.1145/2739482.2764638

DO - 10.1145/2739482.2764638

M3 - Conference paper (refereed)

AN - SCOPUS:84959422155

SP - 1377

EP - 1378

BT - GECCO 2015 : Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

A2 - SILVA, Sara

PB - Association for Computing Machinery, Inc

T2 - 17th Genetic and Evolutionary Computation Conference, GECCO 2015

Y2 - 11 July 2015 through 15 July 2015

ER -

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

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Keywords

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