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An Entropy-Based Multi-population Genetic Algorithm and Its Application

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Advances in Intelligent Computing (ICIC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3644))

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Abstract

An improved genetic algorithm based on information entropy is presented in this paper. A new iteration scheme in conjunction with multi-population genetic strategy, entropy-based searching technique with narrowing down space and the quasi-exact penalty function is developed to solve nonlinear programming problems with equality and inequality constraints. A specific strategy of reserving the most fitness member with evolutionary historic information is effectively used to approximate the solution of the nonlinear programming problems to the global optimization. Numerical examples and an application in molecular docking demonstrate its accuracy and efficiency.

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References

  1. Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  2. Wu, J.-y., Wang, X.-c.: A Parellel Genetic Design Method with Coarse Grain. Chinese Journal of Computational Mechanics 19(2), 148–153 (2002) (in Chinese)

    Google Scholar 

  3. Li, X.-s.: A Quasi-exact Penalty Function Method for Nonlinear Program. Chinese Science Bulletin 36, 1451–1453 (1991)

    Google Scholar 

  4. Elements of Information Theory. Wiley, New York (1991c)

    Google Scholar 

  5. Charalambous, C.: Nonlinear Least Pth Optimization and Nonlinear Programming. Mathematics Programming 12, 195–225 (1977)

    Article  MATH  MathSciNet  Google Scholar 

  6. Bazaraa, M.S., Shetty, L.M.: Non-linear Programming: Theory and Algorithms. Wiley, New York (1993)

    Google Scholar 

  7. Floudas, C.A., Pardalos, P.M.: A Collection of Test Problems for Constrained Global Optimization Algorithms. LNCS, vol. 455. Springer, Heidelberg (1987)

    Google Scholar 

  8. He, X.-j., Sun, G.-z., Liu, G.: Random Perturbation Method of Genetic Algorithms. J. Wuhan Univ. (Nat. Sci. Ed.) 47, 285–288 (2001) (in Chinese)

    Google Scholar 

  9. Kubinyi, H.: Burger’s Medicinal Chemistry and Drug Discovery. In: Wolff, M.E. (ed.) Principles and Practice, 5th edn., vol. 1, pp. 497–571. John Wiley & Sons, Inc., New York (1995)

    Google Scholar 

  10. DOCK5.0.0. Demetri Moustakas. Kuntz Laboratory, UCSF, 4,15 (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, Changchun University, Changchun, 130022, China

    Chun-lian Li

  2. Institute of Special Education, Changchun University, Changchun, 130022, China

    Yu sun

  3. Institute of Material Medical Chinese Academy of Medical Sciences &, Peking Union Medical College, Beijing, 1000050, China

    Yan-shen Guo, Feng-ming Chu & Zong-ru Guo

Authors
  1. Chun-lian Li
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  2. Yu sun
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  3. Yan-shen Guo
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  4. Feng-ming Chu
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  5. Zong-ru Guo
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Editor information

Editors and Affiliations

  1. Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences,, China

    De-Shuang Huang

  2. School of Computer & Information Technology, Beijing Jiaotong University, 100044, Beijing, P.R. China

    Xiao-Ping Zhang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, P.O. Box, Singapore

    Guang-Bin Huang

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© 2005 Springer-Verlag Berlin Heidelberg

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Li, Cl., sun, Y., Guo, Ys., Chu, Fm., Guo, Zr. (2005). An Entropy-Based Multi-population Genetic Algorithm and Its Application. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_99

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  • DOI: https://doi.org/10.1007/11538059_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28226-6

  • Online ISBN: 978-3-540-31902-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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