Survey Methodology
A grouping genetic algorithm for joint stratification and sample allocation designs

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by Mervyn O’Luing, Steven Prestwich, and S. Armagan TarimNote 1

  • Release date: December 17, 2019
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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.

Key Words:      Grouping genetic algorithm; Optimal stratification; Sample allocation; R software.

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How to cite

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. Paper available at http://www.statcan.gc.ca/pub/12-001-x/2019003/article/00007-eng.htm.

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ISSN : 1492-0921

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Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

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Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2019

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Classical vs grouping genetic algorithms
Section 3. Comparing the genetic algorithms
Section 4. An improved Bethel implementation
Section 5. Conclusion and further work
Acknowledgements
References

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