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A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems

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  • Wolters, Mark A.

Abstract

The R function kofnGA conducts a genetic algorithm search for the best subset of k items from a set of n alternatives, given an objective function that measures the quality of a subset. The function fills a gap in the presently available subset selection software, which typically searches over a range of subset sizes, restricts the types of objective functions considered, or does not include freely available code. The new function is demonstrated on two types of problem where a fixed-size subset search is desirable: design of environmental monitoring networks, and D-optimal design of experiments. Additionally, the performance is evaluated on a class of constructed test problems with a novel design that is interesting in its own right.

Suggested Citation

  • Wolters, Mark A., 2015. "A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(c01).
    • Handle: RePEc:jss:jstsof:v:068:c01
    DOI: http://hdl.handle.net/10.18637/jss.v068.c01
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    Cited by:

    1. Mark A. Wolters & C. B. Dean, 2017. "Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 622-645, December.
    2. Cappozzo, Andrea & Greselin, Francesca & Murphy, Thomas Brendan, 2021. "Robust variable selection for model-based learning in presence of adulteration," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

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