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Application of genetic algorithms to the construction of exact D-optimal designs

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  • Grace Montepiedra

Abstract

This paper studies the application of genetic algorithms to the construction of exact D-optimal experimental designs. The concept of genetic algorithms is introduced in the general context of the problem of finding optimal designs. The algorithm is then applied specifically to finding exact D-optimal designs for three different types of model. The performance of genetic algorithms is compared with that of the modified Fedorov algorithm in terms of computing time and relative efficiency. Finally, potential applications of genetic algorithms to other optimality criteria and to other types of model are discussed, along with some open problems for possible future research.

Suggested Citation

  • Grace Montepiedra, 1998. "Application of genetic algorithms to the construction of exact D-optimal designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(6), pages 817-826.
    • Handle: RePEc:taf:japsta:v:25:y:1998:i:6:p:817-826
    DOI: 10.1080/02664769822800
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    References listed on IDEAS

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    1. Sung Jung, Joo & Jin Yum, Bong, 1996. "Construction of exact D-optimal designs by tabu search," Computational Statistics & Data Analysis, Elsevier, vol. 21(2), pages 181-191, February.
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    Cited by:

    1. Karvanen, Juha & Kulathinal, Sangita & Gasbarra, Dario, 2009. "Optimal designs to select individuals for genotyping conditional on observed binary or survival outcomes and non-genetic covariates," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1782-1793, March.
    2. Harman, Radoslav & Filová, Lenka, 2014. "Computing efficient exact designs of experiments using integer quadratic programming," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1159-1167.
    3. CUERVO, Daniel Palhazi & GOOS, Peter & SÖRENSEN, Kenneth, 2013. "An iterated local search algorithm for the construction of large scale D-optimal experimental designs," Working Papers 2013006, University of Antwerp, Faculty of Business and Economics.
    4. Masoudi, Ehsan & Holling, Heinz & Wong, Weng Kee, 2017. "Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 330-345.

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