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Niche Genetic Algorithm for Solving Multiplicity Problems in Genetic Association Studies

Fu-I Chou1, Wen-Hsien Ho2,3, Chiu-Hung Chen4,*
1 Department of Automation Engineering, National Formosa University, Taiwan, R.O.C.
2 Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan, R.O.C.
3 Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Kaohsiung, Taiwan, R.O.C.
4 Department of Mechanical and Computer-Aided Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, Taichung, Taiwan 407, R.O.C.
* Corresponding Authors: Chiu-Hung Chen, ;

Intelligent Automation & Soft Computing 2020, 26(3), 501-512. https://doi.org/10.32604/iasc.2020.013926

Abstract

This paper proposes a novel genetic algorithm (GA) that embeds a niche competition strategy (NCS) in the evolutionary flow to solve the combinational optimization problems that involve multiple loci in the search space. Unlike other niche-information based algorithms, the proposed NCSGA does not need prior knowledge to design niche parameters in the niching phase. To verify the solution capability of the new method, benchmark studies on both the travelling salesman problem (TSP) and the airline recovery scheduling problem were first made. Then, the proposed method was used to solve single nucleotide polymorphism (SNP) barcodes generation problems in a genetic association study. Experiments showed that the proposed NCS-based solver substantially improves solution quality by maintaining multiple optima.

Keywords

genetic algorithm, combinational optimization, travelling salesman problem, genetic association, single nucleotide polymorphism.

Cite This Article

F. Chou, W. Ho and C. Chen, "Niche genetic algorithm for solving multiplicity problems in genetic association studies," Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 501–512, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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