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International Conference on Parallel Problem Solving from Nature

PPSN 1998: Parallel Problem Solving from Nature — PPSN V pp 712–721Cite as

Immune network dynamics for inductive problem solving

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

Abstract

This paper develops an inductive computation algorithm upon biological mechanisms discovered by the immunology. We build an evolutionary search algorithm based on a model of the immune network dynamics. According to it, the concentration of lymphocyte clone-like solutions is determined by the degree of recognition of antigens, as well as the extent of behavioral interaction with other members of the population. The antigen-like examples also change their concentration to gear up solutions matching slightly covered examples. These dynamic features are incorporated in the fitness function of the immune algorithm in order to achieve high diversity and efficient search navigation. Empirical evidence for the superiority of this immune version before the simple genetic algorithm on automata induction tasks are presented.

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References

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Author information

Authors and Affiliations

  1. Information Technologies Lab, New Bulgarian University, 1113, Sofia, Bulgaria

    Vanio Slavov

  2. Department of Computer Science, American University in Bulgaria, 2700, Blagoevgrad, Bulgaria

    Nikolay I. Nikolaev

Authors
  1. Vanio Slavov
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  2. Nikolay I. Nikolaev
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Slavov, V., Nikolaev, N.I. (1998). Immune network dynamics for inductive problem solving. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056913

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  • DOI: https://doi.org/10.1007/BFb0056913

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

  • eBook Packages: Springer Book Archive

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