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Evolutionary Model for Sequence Generation

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Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues (ICIC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4681))

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Abstract

DNA computing is being applied to solve problems in combinatorial optimization, logic and Boolean circuits. Breakthrough solutions in combinatorial optimization are the most impressive area of success but, in order to solve combinatorial optimization problems, problems related to the reliability of biological operators, stable DNA expressions, processing speed, expandability and the universality of evaluation criteria must be solved. This study implements a DNA sequence generation system that minimizes errors using DNA coding based on evolutionary models and performs simulation using biological experiment operators. The usefulness of this system is evaluated by applying the Hamiltonian Path Problem (HPP) in the form of a genetic algorithm. The proposed system generates sequences with minimal errors, as compared to existing systems, and identifies optimal solutions for combinatorial optimization problems in significantly reduced processing times.

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References

  1. Kashiwamura, S., Kameda, A., Yamamoto, M., Ohuchi, A.: General Protocol for Evaluating the Degree of Occurrence of Mis-hybridization. In: Proceedings of the Ninth International Symposium on Artificial Life and Robotics, AROB 9th ’04, pp. 303-308 (2004)

    Google Scholar 

  2. Shin, S.Y., Lee, I.H., Kim, D.M., Zhang, B.T.: Multiobjective Evolutionary Optimization of DNA Sequences for Reliable DNA Computing. IEEE Transactions on Evolutionary Computation 9(2), 143–158 (2005)

    Article  Google Scholar 

  3. Faulhammer, D., Cukras, A.R., Lipton, R.J., Landweber, L.F.: Molecular Computation: RNA Solutions to Chess Problems. Proceedings of the National Academy of Science, U.S.A. 97, 1385–1389 (2000)

    Article  Google Scholar 

  4. Tuplan, D.C., Hoose, H., Condon, A.: Stochastic Local Search Algorithms for DNA Word Design. In: Proceedings of the 8th International. Workshop on DNA Based Compuing, pp. 229–241 (2002)

    Google Scholar 

  5. Andronescu, M., Dees, D., Slaybaugh, L., Zhao, Y., Condon, A., Cohen, B., Skiena, S.: Algorithms for Testing That DNA Word Designs Avoid Unwanted Secondary Structure. In: Proc. 8th Int. Workshop DNA Based Compuing, pp. 182–195 (2002)

    Google Scholar 

  6. Feldkamp, U., Saghafi, S., Banzhaf, W., Rauhe, H.: DNA Sequence Generator - A Program for the Construction of DNA Sequences. In: Proc. 7th Int. Workshop DNA Based Computing, pp. 179–188 (2001)

    Google Scholar 

  7. Kameda, A., Yamamoto, M., Uejima, H., Hagiya, M., Sakamoto, K., Ohuchi, A.: Hairpin-based State Machine and Conformational Addressing: Design and Experiment. Natural Computing 4(2), 103–126 (2005)

    Article  Google Scholar 

  8. Max, H.G., Russell, J.D.: Codeword Design and Information Encoding in DNA Ensembles. Natural Computing 3(3), 253–292 (2004)

    MATH  Google Scholar 

  9. Adleman, L.M.: Molecular Computation of Solutions to Combinatorial Problems. Science 266, 1021–1024 (1994)

    Article  Google Scholar 

  10. Ansari, N.: Computational Intelligence for Optimization. Kluwer Academic Publishers, Dordrecht (1997)

    MATH  Google Scholar 

  11. Yoshikawa, T., Furuhashi, T., Uchidawa, Y.: Acquisition of Fuzzy Rules of Constructing Intelligent Systems Using Genetic Algorithm Based on DNA Coding Method. In: Proceedings of International Joint Conference of   CFSA/IFIS/SOFT’95 on Fuzzy Theory and Applications (1995)

    Google Scholar 

  12. Kashiwamura, S., Kameda, A., Yamamoto, M., Ohuchi, A.: Two-step Search for DNA Sequence Design. In: Proceedings of the 2003 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 03), pp. 1815–1818 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics and Physics, Anhui University of Science and Technology, Anhui Huainan (232001), China

    Zhi-xiang Yin, Jin Yang, Jian-zhong Cui & Jiaxiu Zhang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, Huibei Wuhan (430074), China

    Zhi-xiang Yin

Authors
  1. Zhi-xiang Yin
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  2. Jin Yang
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  3. Jian-zhong Cui
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  4. Jiaxiu Zhang
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Editor information

De-Shuang Huang Laurent Heutte Marco Loog

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© 2007 Springer Berlin Heidelberg

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Yin, Zx., Yang, J., Cui, Jz., Zhang, J. (2007). Evolutionary Model for Sequence Generation. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_2

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  • DOI: https://doi.org/10.1007/978-3-540-74171-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74170-1

  • Online ISBN: 978-3-540-74171-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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