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DNA Origami Based Computing Model for the Satisfiability Problem

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 951))

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Abstract

The satisfiability problem (SAT) is one of the NP-complete problems in the fields of theoretical computer and artificial intelligence, is the core of NP-complete problems. Compared with traditional DNA self-assembly, DNA origami is a new method of DNA self-assembly. We first give a description and the status quo of study of the satisfiability problem, briefly introduce the principle of DNA origami, propose the computing model based on DNA origami to solve the satisfiability problem, and solve an instance with 3 variables, 3 clauses to illustrate the feasibility of the algorithm. The proposed model only uses gel electrophoresis to search the solution to the problem, which is the most reliable biological operation known to date, therefore the proposed model is feasible. At present, the reported results concerning using origami to solve the NP-complete problem is relatively few. Our method is a new attempt to solve the NP- complete problem using biological DNA molecules.

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References

  1. Neidell, N.S.: Amplitude variation with offset. Lead. Edge 5(3), 47–51 (1986)

    Article  Google Scholar 

  2. Li, S.P.: AVO seismic parameter inversion method and its application. China University of Petroleum (2009). (in Chinese)

    Google Scholar 

  3. Chen, J.J.: Inversion method of the three AVO parameters. China University of Petroleum (2007). (in Chinese)

    Google Scholar 

  4. Wang, L.P.: Prestack AVO non-linear inversion of intelligent optimization algorithm. China University of Geosciences (2015). (in Chinese)

    Google Scholar 

  5. Berg, E.: Simple convergent genetic algorithm for inversion of multiparameter data. In: SEG Technical Program Expanded Abstracts 1993, pp. 1126–1128. Society of Exploration Geophysicists (1990)

    Google Scholar 

  6. Porsani, M.J.: A combined genetic and linear inversion algorithm for seismic waveform inversion. In: SEG Technical Program Expanded Abstracts 1993, pp. 692–695. Society of Exploration Geophysicists (1993)

    Google Scholar 

  7. Mallick, S.: Model-based inversion of amplitude-variations-with-offset data using a genetic algorithm. Geophysics 60(4), 939–954 (1995)

    Article  Google Scholar 

  8. Priezzhev, I.I., Shmaryan, L.E., Bejarano, G.: Nonlinear multitrace seismic inversion using neural network and genetic algorithm. In: 3rd EAGE St. Petersburg International Conference and Exhibition on Geosciences-geosciences: From New Ideas to New Discoveries (2008)

    Google Scholar 

  9. Soupios, P., Akca, I., Mpogiatzis, P.: Applications of hybrid genetic algorithms in seismic tomography. J. Appl. Geophys. 75(3), 479–489 (2011)

    Article  Google Scholar 

  10. Bai, J., Xu, Z., Xiao, Y., Xie, T.: Nonlinear hybrid optimization algorithm for seismic impedance inversion. In: Beijing International Geophysical Conference & Exposition 21–24, Beijing, China (2014)

    Google Scholar 

  11. Agarwal, A., Sain, K., Shalivahan, S.: Traveltime and constrained AVO inversion using FDR PSO. In: SEG Technical Program Expanded Abstracts 2016, pp. 577–581. Society of Exploration Geophysicists (2016)

    Google Scholar 

  12. Sun, S.Z.: PSO non-linear pre-stack inversion method and the application in reservoir prediction. In: SEG Technical Program Expanded Abstracts 2012, pp. 1–5. Society of Exploration Geophysicists (2012)

    Google Scholar 

  13. Sun, S.Z., Liu, L.: A numerical study on non-linear AVO inversion using chaotic quantum particle swarm optimization. J. Seismic Explor. 23(4), 379–392 (2014)

    Google Scholar 

  14. Zhou, Y., Nie, Z., Jia, Z.: An improved differential evolution algorithm for nonlinear inversion of earthquake dislocation. Geodesy Geodyn. 5(4), 49–56 (2014)

    Article  Google Scholar 

  15. Gao, Z., Pan, Z., Gao, J.: Multimutation differential evolution algorithm and its application to seismic inversion. IEEE Trans. Geosci. Remote Sens. 54(6), 3626–3636 (2016)

    Article  Google Scholar 

  16. Yin, X.Y., Kong, S.S., Zhang, F.C.: Prestack AVO inversion based on differential evolution algorithm. Oil Geophys. Prospect. 48(4), 591–596 (2013)

    Google Scholar 

  17. Wu, Q.H., Wang, L.P., Zhu, Z.X.: Research of pre-stack AVO elastic parameter inversion problem based on hybrid genetic algorithm. Cluster Comput. 20(4), 3173–3783 (2017)

    Article  Google Scholar 

  18. Wu, Q., Zhu, Z.X., Yan, X.S.: Research on the parameter inversion problem of prestack seismic data based on improved differential evolution algorithm. Cluster Comput. 20(4), 2881–2890 (2017)

    Article  Google Scholar 

  19. De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)

    Article  Google Scholar 

  20. Gong, M., Jiao, L., Zhang, L.: Baldwinian learning in clonal selection algorithm for optimization. Inf. Sci. 180(8), 1218–1236 (2010)

    Article  Google Scholar 

  21. Feng, J., Jiao, L.C., Zhang, X.: Bag-of-visual-words based on clonal selection algorithm for SAR image classification. IEEE Geosci. Remote Sens. Lett. 8(4), 691–695 (2011)

    Article  Google Scholar 

  22. Karoum, B., Elbenani, Y.B.: A clonal selection algorithm for the generalized cell formation problem considering machine reliability and alternative routings. Prod. Eng. 2017(15), 1–12 (2017)

    Google Scholar 

  23. Rao, B.S., Vaisakh, K.: Multi-objective adaptive clonal selection algorithm for solving optimal power flow problem with load uncertainty. Int. J. Bio-Inspired Comput. 8(2), 67 (2016)

    Article  Google Scholar 

  24. Swain, R.K., Barisal, A.K., Hota, P.K.: Short-term hydrothermal scheduling using clonal selection algorithm. Int. J. Electr. Power Energy Syst. 33(3), 647–656 (2011)

    Article  Google Scholar 

  25. Chitsaz, H., Amjady, N., Zareipour, H.: Wind power forecast using wavelet neural network trained by improved clonal selection algorithm. Energy Convers. Manage. 89, 588–598 (2015)

    Article  Google Scholar 

  26. Sindhuja, L.S., Padmavathi, G.: Replica node detection using enhanced single Hop detection with clonal selection algorithm in mobile wireless sensor networks. Hindawi Publishing Corp (2016)

    Google Scholar 

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Acknowledgement

The author sincerely thanks for the encouragement and advice given by the DNA computing research group, and thanks Professor Yin for his guidance. This research is funded by the National Natural Science Foundation of China; 61672001, 61702008.

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Authors and Affiliations

  1. School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, China

    Zhenqin Yang, Zhixiang Yin & Jing Yang

  2. School of Electronic and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, China

    Jianzhong Cui

Authors
  1. Zhenqin Yang
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  2. Zhixiang Yin
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  3. Jianzhong Cui
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  4. Jing Yang
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Corresponding author

Correspondence to Zhenqin Yang .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Yang, Z., Yin, Z., Cui, J., Yang, J. (2018). DNA Origami Based Computing Model for the Satisfiability Problem. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_14

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2825-1

  • Online ISBN: 978-981-13-2826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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