A Multiobjective Evolution Strategy Algorithm for DNA Sequence Design

Kai ZHANG; Bin Chen; Zhiwei Xu

doi:10.11999/JEIT190869

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

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A Multiobjective Evolution Strategy Algorithm for DNA Sequence Design

doi: 10.11999/JEIT190869

Kai ZHANG^{1, 2},
Bin Chen¹,
Zhiwei Xu^{1
,
,}

1.
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
2.
Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China)

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

Abstract

Abstract

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.
- DNA Computing,
- DNA sequence design,
- Multiobjective evolutionary algorithm,
- Evolution strategy

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References(20)

References

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