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Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation

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Book cover Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

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Abstract

When considering function optimisation, there is a trade off between quality of solutions and the number of evaluations it takes to find that solution. Hybrid genetic algorithms have been widely used for function optimisation and have been shown to perform extremely well on these tasks. This paper presents a novel algorithm inspired by the mammalian immune system, combined with a unique mutation mechanism. Results are presented for the optimisation of twelve functions, ranging in dimensionality from one to twenty. Results show that the immune inspired algorithm performs significantly fewer evaluations when compared to a hybrid genetic algorithm, whilst not sacrificing quality of the solution obtained.

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Authors and Affiliations

  1. Computing Laboratory, University of Kent, Canterbury, Kent, CT2 7NF, UK

    Johnny Kelsey & Jon Timmis

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  1. Johnny Kelsey
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  2. Jon Timmis
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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Kelsey, J., Timmis, J. (2003). Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_26

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  • DOI: https://doi.org/10.1007/3-540-45105-6_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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