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Volume 42 Issue 6
Jun.  2020
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Kai ZHANG, Bin Chen, Zhiwei Xu. A Multiobjective Evolution Strategy Algorithm for DNA Sequence Design[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1365-1373. doi: 10.11999/JEIT190869
Citation: Kai ZHANG, Bin Chen, Zhiwei Xu. A Multiobjective Evolution Strategy Algorithm for DNA Sequence Design[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1365-1373. doi: 10.11999/JEIT190869

A Multiobjective Evolution Strategy Algorithm for DNA Sequence Design

doi: 10.11999/JEIT190869
Funds:  The National Natural Science Foundation of China (61472293, 61702383, 61602328)
  • Received Date: 2019-11-01
  • Rev Recd Date: 2020-03-01
  • Available Online: 2020-04-09
  • Publish Date: 2020-06-22
  • It is important to design high-quality DNA sequences set, which can improve the reliability and efficiency of DNA computing. DNA sequence design problem is an multiobjective optimization problem that needs to satisfy multiple conflict objectives which are thermodynamic constraint, similarity constraint and GC content constraint simultaneously. A MultiObjective Evolutionary Strategy (MOES) is proposed to solve the DNA sequence design problem. The random base mutation operator is designed for exploration and exploitation the search space. The fitness function is improved for obtaining balanced similarity and H-measure objective functions. Some state-of-the-art approaches are chosen to evaluate the effectivity of proposed algorithm. The experiment results show that the proposed multiobjective evolution strategy algorithm obtains very promising DNA sequences and outperforms previous approaches.
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  • ADLEMAN L M. Molecular computation of solutions to combinatorial problems[J]. Science, 1994, 266(5187): 1021–1024. doi: 10.1126/science.7973651
    DE SILVA P Y and GANEGODA G U. New trends of digital data storage in DNA[J]. BioMed Research International, 2016: 8072463.
    BRAICH R S, CHELYAPOV N, JOHNSON C, et al. Solution of a 20-variable 3-SAT problem on a DNA computer[J]. Science, 2002, 296(5567): 499–502. doi: 10.1126/science.1069528
    ZIMMERMANN K H. Efficient DNA sticker algorithms for NP-complete graph problems[J]. Computer Physics Communications, 2002, 144(3): 297–309. doi: 10.1016/S0010-4655(02)00270-9
    XU Jin, QIANG Xiaoli, ZHANG Kai, et al. A DNA computing model for the graph vertex coloring problem based on a probe graph[J]. Engineering, 2018, 4(1): 61–77. doi: 10.1016/j.eng.2018.02.011
    CHAVES-GONZÁLEZ J M and VEGA-RODRÍGUEZ M A. A multiobjective approach based on the behavior of fireflies to generate reliable DNA sequences for molecular computing[J]. Applied Mathematics and Computation, 2014, 227: 291–308. doi: 10.1016/j.amc.2013.11.032
    PENG Ximei, ZHENG Xuedong, WANG Bin, et al. A micro-genetic algorithm for DNA encoding sequences design[C]. The 2nd International Conference on Control Science and Systems Engineering, Singapore, 2016: 10–14.
    YANG Gaijing, WANG Bin, ZHENG Xuedong, et al. IWO algorithm based on niche crowding for DNA sequence design[J]. Interdisciplinary Sciences: Computational Life Sciences, 2017, 9(3): 341–349. doi: 10.1007/s12539-016-0160-0
    WANG Yanfeng, SHEN Yongpeng, ZHANG Xuncai, et al. An improved non-dominated sorting genetic algorithm-Ⅱ (INSGA-Ⅱ) applied to the design of DNA codewords[J]. Mathematics and Computers in Simulation, 2018, 151: 131–139. doi: 10.1016/j.matcom.2018.03.011
    CHAVES-GONZÁLEZ J M and MARTÍNEZ-GIL J. An efficient design for a multi-objective evolutionary algorithm to generate DNA libraries suitable for computation[J]. Interdisciplinary Sciences: Computational Life Sciences, 2018, 11(3): 542–558.
    YANG Shuming, SHAO Dongguo, and LUO Yangjie. A novel evolution strategy for multiobjective optimization problem[J]. Applied Mathematics and Computation, 2005, 170(2): 850–873. doi: 10.1016/j.amc.2004.12.025
    ARNOLD D V and BEYER H G. Investigation of the (μ, λ)-ES in the presence of noise[C]. The 2001 Congress on Evolutionary Computation, Seoul, South Korea, 2001, 1: 332–339.
    EBENAU C, ROTTSCHÄFER J, and THIERAUF G. An advanced evolutionary strategy with an adaptive penalty function for mixed-discrete structural optimisation[J]. Advances in Engineering Software, 2005, 36(1): 29–38. doi: 10.1016/j.advengsoft.2003.10.008
    MEZURA-MONTES E and COELLO C A C. A simple multimembered evolution strategy to solve constrained optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2005, 9(1): 1–17. doi: 10.1109/TEVC.2004.836819
    XIAO J, XU Jin, CHEN Zhihua, et al. A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding[J]. Computers & Mathematics with Applications, 2009, 57(11/12): 1949–1958.
    CHAVES-GONZÁLEZ J M, VEGA-RODRÍGUEZ M A, and GRANADO-CRIADO J M. A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design[J]. Engineering Applications of Artificial Intelligence, 2013, 26(9): 2045–2057. doi: 10.1016/j.engappai.2013.04.011
    MUHAMMAD M S, SELVAN K V, MASRA S M W, et al. An improved binary particle swarm optimization algorithm for DNA encoding enhancement[C]. 2011 IEEE Symposium on Swarm Intelligence, Paris, France, 2011: 1–8.
    CHAVES-GONZÁLEZ J M and VEGA-RODRÍGUEZ M A. DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm[J]. Biosystems, 2014, 116: 49–64. doi: 10.1016/j.biosystems.2013.12.005
    BUI L T and ALAM S. Multi-Objective Optimization in Computational Intelligence: Theory and Practice[M]. Hershey: IGI Global, 2008.
    SHIN S Y, LEE I H, KIM D, et al. Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing[J]. IEEE Transactions on Evolutionary Computation, 2005, 9(2): 143–158. doi: 10.1109/TEVC.2005.844166
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