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Research on the Evolutionary Strategy Based on AIS and Its Application on Numerical Integration

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Intelligent Computing and Information Science (ICICIS 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 135))

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Abstract

Based on the features of artificial immune system, a new evolutionary strategy is proposed in order to calculate the numerical integration of functions. This evolutionary strategy includes the mechanisms of swarm searching and constructing the fitness function. Finally, numerical examples are given for verifying the effectiveness of evolutionary strategy. The results show that the performance of evolutionary strategy is satisfactory and more accurate than traditional methods of numerical integration, such as trapezoid formula and Simpson formula.

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References

  1. Qi, A., Du, C.: Immune System and Nonlinear Modeling. Shanghai Scientific and Technological Education Publishing House, ShangHai (1998)

    Google Scholar 

  2. Dasgupta, D.: Artificial Immune System and Their Applications. Springer, Heidelberg (1999)

    Book  MATH  Google Scholar 

  3. Ding, Y., Ren, L.: Artificial Immune Systems: Theory and Applications. Pattern Recognition and Artificial Intelligence 13(1), 52–59 (2000)

    MathSciNet  Google Scholar 

  4. Sasaki, M., Kawafuku, M., Takahashi, K.: An immune feedback mechanism based adaptive learning of neural network controller. In: 6th International Conference on Neural Information Processing, vol. 2, pp. 502–507. IEEE Computer Society Press, Los Alamitos (1999)

    Google Scholar 

  5. Gao, J.: The Application of the Immune Algorithm for Power Network Planning. System Engineering-Theory & Practice (5), 119–123 (2001)

    Google Scholar 

  6. Shao, X., Chen, Z., Lin, X.: A Novel Algorithm for Fitting Analytical Signals-an Immune Algorithm. Chinese Journal of Analytical Chemistry 28(2), 152–155 (2000)

    Google Scholar 

  7. Timmis, J., Neal, M., Hunt, J.: Data analysis using artificial immune systems, cluster analysis and Kohonen networks:some comparisons. In: IEEE SMC 1999 Conference Proceedings, vol. 3, pp. 922–927. Institute of Electrical and Electronics Engineers, Incorporated (1999)

    Google Scholar 

  8. Ke, S.: Advanced Mathematics. Beijing University of Aeronautics & Astronautics Press, Beijing (2007)

    Google Scholar 

  9. Jiao, L., Du, H.: Development and Prospect of the Artificial Immune System. ACTA Electronica Sinica 31(10), 1540–1548 (2003)

    Google Scholar 

  10. Burden, R.L., Faires, J.K.: Numerical Analysis, 7th edn., pp. 190–212. Brooks/Cole, Thomson Learning, Inc. (2001)

    Google Scholar 

  11. Nie, L.: Artificial Fish Swarm Algorithm and its Application. Guangxi University for Nationalities, Guangxi

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Enginerring, Henan University of Urban Construction, Henan, Pingdingshan, 467001, China

    Li Bei

Authors
  1. Li Bei
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Editor information

Editors and Affiliations

  1. The Key Laboratory of Manufacture and Test, Chongqing University of Technology, 400054, Chongqing, P. R. China

    Ran Chen

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© 2011 Springer-Verlag Berlin Heidelberg

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Bei, L. (2011). Research on the Evolutionary Strategy Based on AIS and Its Application on Numerical Integration. In: Chen, R. (eds) Intelligent Computing and Information Science. ICICIS 2011. Communications in Computer and Information Science, vol 135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18134-4_29

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  • DOI: https://doi.org/10.1007/978-3-642-18134-4_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18133-7

  • Online ISBN: 978-3-642-18134-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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