Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation

Jon Timmis*, Camilla Edmonds, Johnny Kelsey

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

40 Citations (Scopus)

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 acknowledge 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.

Original languageEnglish
Title of host publicationProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Pages1044-1051
Number of pages8
Publication statusPublished - 2004
EventProceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States of America
Duration: 19 Jun 200423 Jun 2004

Publication series

NameProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Volume1

Conference

ConferenceProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Country/TerritoryUnited States of America
CityPortland, OR
Period19 Jun 200423 Jun 2004

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