A comment on opt-AiNET: An immune network algorithm for optimisation

Jon Timmis*, Camilla Edmonds

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

64 Citations (Scopus)

Abstract

Verifying the published results of algorithms is part of the usual research process. This helps to both validate the existing literature, but also quite often allows for new insights and augmentations of current systems in a methodological manner. This is very pertinent in emerging new areas such as Artificial Immune Systems, where it is essential that any algorithm is well understood and investigated. The work presented in this paper results from an investigation into the opt-aiNET algorithm, a well-known immune inspired algorithm for function optimisation. Using the original source code developed for opt-aiNET, this paper identifies two minor errors within the code, propose a slight augmentation of the algorithm to automate the process of peak identification: all of which affect the performance of the algorithm. Results are presented for testing of the existing algorithm and in addition, for a slightly modified version, which takes into account some of the issues discovered during the investigations.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsKalyanmoy Deb, Riccardo Poli, Owen Holland, Kalyanmoy Banzhaf, Hans-Georg Beyer, Edmund Burke, Paul Darwen, Dipankar Dasgupta, Dario Floreano, James Foster, Mark Harman, Pier Luca Lanzi, Lee Spector, Andrea G. B. Tettamanzi, Dirk Thierens, Andrew M. Tyrrell
PublisherSpringer Nature
Pages308-317
Number of pages10
ISBN (Print)3540223444, 9783540223443
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3102
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • candidate solution
  • average fitness
  • artificial immune system
  • network cell
  • immune network

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