Artificial immune recognition system (AIRS): An immune-inspired supervised learning algorithm

Andrew Watkins*, Jon Timmis, Lois Boggess

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

325 Citations (SciVal)


This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS.

Original languageEnglish
Pages (from-to)291-317
Number of pages27
JournalGenetic Programming and Evolvable Machines
Issue number3
Publication statusPublished - Sept 2004


  • Artificial immune systems
  • Classification
  • Neural networks
  • Supervised learning


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