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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)

Language: English
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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: (The current URI for this page, for reference purposes)
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