Exploiting parallelism inherent in AIRS an artificial immune classifier

Andrew Watkins, Jon Timmis

Research output: Chapter in Book/Report/Conference proceedingChapter

68 Citations (Scopus)

Abstract

The mammalian immune system is a highly complex, inherently parallel, distributed system. The field of Artificial Immune Systems (AIS) has developed a wide variety of algorithms inspired by the immune system, few of which appear to capitalize on the parallel nature of the system from which inspiration was taken. The work in this paper presents the first steps at realizing a parallel artificial immune system for classification. A simple parallel version of the classification algorithm Artificial Immune Recognition System (AIRS) is presented. Initial results indicate that a decrease in overall runtime can be achieved through fairly naïve techniques. The need for more theoretical models of the behavior of the algorithm is discussed.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsGiuseppe Nicosia, Vincenzo Cutello, Peter J. Bentley, Jon Timmis
PublisherSpringer Nature
Pages427-438
Number of pages12
ISBN (Print)3540230971, 9783540230977
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3239
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • memory cell
  • message passing interface
  • parallel version
  • training instance
  • serial version

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