Metaheuristics and machine learning for joint stratification and sample allocation in survey design

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dc.contributor.advisorPrestwich, Steve
dc.contributor.advisorTarim, Armagan
dc.contributor.authorO'Luing, Mervyn
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-06-06T12:43:54Z
dc.date.available2023-06-06T12:43:54Z
dc.date.issued2022-01
dc.date.submitted2022-01
dc.description.abstractIn this thesis, we propose a number of metaheuristics and machine learning techniques to solve the joint stratification and sample allocation problem. Finding the optimal solution to this problem is hard when the sampling frame is large, and the evaluation algorithm is computationally burdensome. To advance the research in this area, we explore and evaluate different algorithmic methods of modelling and solving this problem. Firstly, we propose a new genetic algorithm approach using "grouping" genetic operators instead of traditional operators. Experiments show a significant improvement in solution quality for similar computational effort. Next, we combine the capability of a simulated annealing algorithm to escape from local minima with delta evaluation to exploit the similarity between consecutive solutions and thereby reduce evaluation time. Comparisons with two recent algorithms show the simulated annealing algorithm attaining comparable solution qualities in less computation time. Then, we consider the combination of the k-means and clustering algorithms with a hill climbing algorithm in stages and report the solution costs, evaluation times and training times. The multi-stage combinations generally compare well with recent algorithms, and provide the survey designer with a greater choice of algorithms to choose from. Finally, we combine the explorative properties of an estimation of distribution algorithm (EDA) to model the probabilities of an atomic stratum belonging to different strata with the exploitative search properties of a simulated annealing algorithm to create a hybrid estimation of distribution algorithm (HEDA). Results of comparisons with the best solution qualities from our earlier experiments show that the HEDA finds better solution qualities, but requires a longer total execution time than alternative approaches we considered.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Luing, M. 2022. Metaheuristics and machine learning for joint stratification and sample allocation in survey design. PhD Thesis, University College Cork.en
dc.identifier.endpage214en
dc.identifier.urihttps://hdl.handle.net/10468/14544
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 2/12/RC/2289-P2s/IE/INSIGHT Phase 2/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2022, Mervyn O'Luing.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectMetaheuristicsen
dc.subjectMachine learningen
dc.subjectStratification and sample allocationen
dc.subjectSurvey designen
dc.titleMetaheuristics and machine learning for joint stratification and sample allocation in survey designen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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