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.
The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.