A grouping genetic algorithm for joint stratification and sample allocation designs

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O'Luing, Mervyn
Prestwich, Steven D.
Tarim, S. Armagan
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Statistics Canada
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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.
Grouping genetic algorithm , Optimal stratification , Sample allocation , R software
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