Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation

  • Johnny Kelsey
  • Jon Timmis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)


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.


Local Search Mutation Operator Clonal Selection Hybrid Genetic Algorithm Immune Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Johnny Kelsey
    • 1
  • Jon Timmis
    • 1
  1. 1.Computing LaboratoryUniversity of KentCanterbury, KentUK

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