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A genetic algorithm for designing microarray experiments

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Abstract

Heuristic techniques of optimization can be useful in designing complex experiments, such as microarray experiments. They have advantages over the traditional methods of optimization, particularly in situations where the search space is discrete. In this paper, a search procedure based on a genetic algorithm is proposed to find optimal (efficient) designs for both one- and multi-factor experiments. A genetic algorithm is a heuristic optimization method that exploits the biological evolution to obtain a solution of the problem. As an example, optimal designs for \(3\times 2\) factorial microarray experiments are presented for different numbers of arrays and for various sets of research questions. Comparisons between different operators of the genetic algorithm are performed by simulation studies.

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Acknowledgments

The authors wish to thank the editor and referees for their constructive suggestions that help to improve the organisation of the paper.

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Authors and Affiliations

  1. Institute of Statistical Research and Training (ISRT), University of Dhaka, Dhaka, 1000, Bangladesh

    A. H. M. Mahbub Latif

  2. Abteilung Medizinische Statistik, Georg-August-Universität Göttingen, 37073, Göttingen, Germany

    Edgar Brunner

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  1. A. H. M. Mahbub Latif
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  2. Edgar Brunner
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Correspondence to A. H. M. Mahbub Latif.

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Latif, A.H.M., Brunner, E. A genetic algorithm for designing microarray experiments. Comput Stat 31, 409–424 (2016). https://doi.org/10.1007/s00180-015-0618-2

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  • DOI: https://doi.org/10.1007/s00180-015-0618-2

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