A genetic algorithm approach to optimal stratified sampling

Patrick R. McMullen, M. David Albritton

Research output: Contribution to conferencePaper

Abstract

A technique is presented to assist in the design of stratified random sampling plans via a genetic algorithm approach. When limited resources are available, researchers must often be thrifty in their attempt to find a minimized variance estimate of the mean, subject to the cost constraints of collecting data. The research presented here exploits the artificial intelligence technique of genetic algorithms to find stratified sampling plans, and preliminary results are near-optimal.

Original languageEnglish (US)
Pages1663-1668
Number of pages6
StatePublished - Dec 1 2003
Externally publishedYes
Event34th Annual Meeting of the Decision Sciences Institute - Washington, DC, United States
Duration: Nov 22 2003Nov 25 2003

Other

Other34th Annual Meeting of the Decision Sciences Institute
CountryUnited States
CityWashington, DC
Period11/22/0311/25/03

Keywords

  • Heuristics
  • Mathematical Programming/Optimization
  • Simulation

ASJC Scopus subject areas

  • Management Information Systems
  • Hardware and Architecture

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  • Cite this

    McMullen, P. R., & David Albritton, M. (2003). A genetic algorithm approach to optimal stratified sampling. 1663-1668. Paper presented at 34th Annual Meeting of the Decision Sciences Institute, Washington, DC, United States.