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Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation
J. Kelsey and J. Timmis
In E. Cantu-Paz and et al., editors, Genetic and Evolutionary Computation Conference - GECCO 2003, volume 2723 of Lecture Notes in Computer Science, pages 182-196, Chicago. USA., July 2003. Springer-Verlag.Abstract
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.
Bibtex Record
@inproceedings{1638,
author = {J. Kelsey and J. Timmis},
title = {Immune {I}nspired {S}omatic {C}ontiguous {H}ypermutation for {F}unction {O}ptimisation},
month = {July},
year = {2003},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2003/1638},
editor = {E. Cantu-Paz and et al.},
publication_type = {inproceedings},
submission_id = {17008_1056697974},
ISBN = {3-540-40602-6},
booktitle = {Genetic and Evolutionary Computation Conference - GECCO 2003},
volume = {2723},
series = {Lecture Notes in Computer Science},
address = {Chicago. USA.},
publisher = {Springer-Verlag},
refereed = {yes},
}