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A New Detector Set Generating Algorithm in the Negative Selection Model

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

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Abstract

In order to improve the generating e.ciency of the detector set, a new detection rule called edit distance rule is presented in this paper, based on the negative selection model of Arti.cial Immune System (AIS). Under this rule, edit distance is adopted to measure the similarity between self strings and randomly generated strings. Then a new detector generating algorithm used the new rule is discussed. It is necessary to use the Trie data structure to store the strings in the self set in this new algorithm. Finally, the advantages of the algorithm are given through the theoretical analysis.

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Author information

Authors and Affiliations

  1. Network Center of Taiyuan Univ of Tech, Taiyuan, Shanxi, 030024, China

    Xinhua Ren & Xiufeng Zhang

  2. College of Computer Science and technology & Software of Taiyuan Univ of Tech, Taiyuan, Shanxi, 030024, China

    Yuanyuan Li

Authors
  1. Xinhua Ren
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  2. Xiufeng Zhang
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  3. Yuanyuan Li
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Ren, X., Zhang, X., Li, Y. (2005). A New Detector Set Generating Algorithm in the Negative Selection Model. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_108

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  • DOI: https://doi.org/10.1007/11539117_108

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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