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Adaptive Dynamic Clone Selection Algorithms

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Rough Sets and Current Trends in Computing (RSCTC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3066))

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Abstract

Based on the Antibody Clonal Selection Theory of immunology, a novel artificial immune system algorithm, adaptive dynamic clone select algorithm, is put forward. The new algorithm is intended to integrate the local searching with the global and the probability evolution searching with the stochastic searching. Compared with the improved genetic algorithm and other clonal selection algorithms, the new algorithm prevents prematurity more effectively and has high convergence speed. Numeric experiments of function optimization indicate that the new algorithm is effective and useful.

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References

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Author information

Authors and Affiliations

  1. Institute of Intelligent Information Processing, Xidian University, 710071, Xi’an, China

    Haifeng Du, Licheng Jiao, Maoguo Gong & Ruochen Liu

Authors
  1. Haifeng Du
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  2. Licheng Jiao
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  3. Maoguo Gong
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  4. Ruochen Liu
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Editor information

Editors and Affiliations

  1. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  2. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

  3. The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden

    Jan Komorowski

  4. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymała-Busse

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© 2004 Springer-Verlag Berlin Heidelberg

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Du, H., Jiao, L., Gong, M., Liu, R. (2004). Adaptive Dynamic Clone Selection Algorithms. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_97

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  • DOI: https://doi.org/10.1007/978-3-540-25929-9_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22117-3

  • Online ISBN: 978-3-540-25929-9

  • eBook Packages: Springer Book Archive

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