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ARTICLE
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
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.
Citations