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Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for Function Optimisation

Timmis, Jon and Edmonds, Camilla and Kelsey, Johnny (2004) Assessing the Performance of Two Immune Inspired Algorithms and a Hybrid Genetic Algorithm for Function Optimisation. In: Proceedings of the 2004 Congress on Evolutionary Computation. IEEE, pp. 1044-1051. ISBN 0-7803-8515-2. (doi:10.1109/CEC.2004.1330977) (KAR id:14130)

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http://dx.doi.org/10.1109/CEC.2004.1330977

Abstract

Do artificial immune systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to assess the performance and viability of AIS. The investigation employs standard benchmark functions, and demonstrates that for these functions the opt-aiNET algorithm, when compared to the B-cell algorithm and hybrid GA, on average, takes longer to find the solution, without necessarily a better quality solution. Reasons for these differences are proposed and it is acknowledged that this is preliminary empirical work. It is felt that a more theoretical approach may well be required to ascertain real performance and applicability issues.

Item Type: Book section
DOI/Identification number: 10.1109/CEC.2004.1330977
Uncontrolled keywords: artificial immune systems, 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 18:02 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14130 (The current URI for this page, for reference purposes)
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