Immune inspired somatic contiguous hypermutation for function optimisation

Johnny Kelsey*, Jon Timmis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

133 Citations (SciVal)

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.

Original languageEnglish
Pages (from-to)207-218
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2723
DOIs
Publication statusPublished - 2003

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