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Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation

Kelsey, Johnny and Timmis, Jon (2003) Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation. In: Cantu-Paz, Erick, ed. Genetic and Evolutionary Computation — GECCO 2003 Genetic and Evolutionary Computation Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 207-218. ISBN 978-3-540-40602-0. E-ISBN 978-3-540-45105-1. (doi:10.1007/3-540-45105-6_26) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:13942)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/3-540-45105-6_26

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.

Item Type: Book section
DOI/Identification number: 10.1007/3-540-45105-6_26
Uncontrolled keywords: artificial immune systems, clonal selection, optimisation
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:00 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13942 (The current URI for this page, for reference purposes)
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