A grouping genetic algorithm for joint stratification and sample allocation designs

Show simple item record

dc.contributor.author O’Luing, Mervyn
dc.contributor.author Prestwich, Steven D.
dc.contributor.author Tarim, S. Armagan
dc.date.accessioned 2021-02-23T09:54:07Z
dc.date.available 2021-02-23T09:54:07Z
dc.date.issued 2019-12-17
dc.identifier.citation O’Luing, M., Prestwich, S. and Tarim, S.A. (2019). A grouping genetic algorithm for joint stratification and sample allocation designs. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 45, No. 3, pp. 513-531. Available at http://www.statcan.gc.ca/pub/12-001-x/2019003/article/00007-eng.htm en
dc.identifier.volume 45 en
dc.identifier.issued 3 en
dc.identifier.startpage 513 en
dc.identifier.endpage 531 en
dc.identifier.issn 1492-0921
dc.identifier.uri http://hdl.handle.net/10468/11091
dc.description.abstract Finding the optimal stratification and sample size in univariate and multivariate sample design is hard when the population frame is large. There are alternative ways of modelling and solving this problem, and one of the most natural uses genetic algorithms (GA) combined with the Bethel-Chromy evaluation algorithm. The GA iteratively searches for the minimum sample size necessary to meet precision constraints in partitionings of atomic strata created by the Cartesian product of auxiliary variables. We point out a drawback with classical GAs when applied to the grouping problem, and propose a new GA approach using “grouping” genetic operators instead of traditional operators. Experiments show a significant improvement in solution quality for similar computational effort. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Statistics Canada en
dc.relation.uri http://www.statcan.gc.ca/pub/12-001-x/2019003/article/00007-eng.htm
dc.rights © Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2019 All rights reserved. Use of this publication is governed by the Statistics Canada Open Licence Agreement. en
dc.rights.uri https://www.statcan.gc.ca/eng/reference/licence en
dc.subject Grouping genetic algorithm en
dc.subject Optimal stratification en
dc.subject Sample allocation en
dc.subject R software en
dc.title A grouping genetic algorithm for joint stratification and sample allocation designs en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Steven D. Prestwich, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: s.prestwich@cs.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2021-02-22T17:24:43Z
dc.description.version Published Version en
dc.internal.rssid 556121740
dc.description.status Peer reviewed en
dc.identifier.journaltitle Survey Methodology en
dc.internal.copyrightchecked Yes
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress s.prestwich@cs.ucc.ie en
dc.internal.IRISemailaddress mervyn.oluing@insight-centre.org en
dc.internal.IRISemailaddress armagan.tarim@ucc.ie en
dc.identifier.articleid 12-001-X en


Files in this item

This item appears in the following Collection(s)

Show simple item record

This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement