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*[http://www.artificial-immune-systems.org/algorithms.shtml#clonal-alg Clonal Selection Pseudo code] on AISWeb
*[http://www.artificial-immune-systems.org/algorithms.shtml#clonal-alg Clonal Selection Pseudo code] on AISWeb
*[https://web.archive.org/web/20090711163953/http://www.dca.fee.unicamp.br/~lnunes/manual.html CLONALG in Matlab] developed by Leandro de Castro and Fernando Von Zuben
*[https://web.archive.org/web/20090711163953/http://www.dca.fee.unicamp.br/~lnunes/manual.html CLONALG in Matlab] developed by Leandro de Castro and Fernando Von Zuben
*[http://optalgtoolkit.sourceforge.net Optimization Algorithm Toolkit] in Java developed by Jason Brownlee which includes the following clonal selection algorithms: Adaptive Clonal Selection (ACS), Optimization Immune Algorithm (opt-IMMALG), Optimization Immune Algorithm (opt-IA), Clonal Selection Algorithm (CLONALG, CLONALG1, CLONALG2), B-Cell Algorithm (BCA), Cloning, Information Gain, Aging (CLIGA), Immunological Algorithm (IA)
*[https://web.archive.org/web/20081224175602/http://optalgtoolkit.sourceforge.net/ Optimization Algorithm Toolkit] in Java developed by Jason Brownlee which includes the following clonal selection algorithms: Adaptive Clonal Selection (ACS), Optimization Immune Algorithm (opt-IMMALG), Optimization Immune Algorithm (opt-IA), Clonal Selection Algorithm (CLONALG, CLONALG1, CLONALG2), B-Cell Algorithm (BCA), Cloning, Information Gain, Aging (CLIGA), Immunological Algorithm (IA)
*[http://www.artificial-immune-systems.org/code/airs/airs_andrew.tar.gz AIRS in C++]{{dead link|date=August 2017 |bot=InternetArchiveBot |fix-attempted=yes }} developed by Andrew Watkins
*[http://www.artificial-immune-systems.org/code/airs/airs_andrew.tar.gz AIRS in C++]{{dead link|date=August 2017 |bot=InternetArchiveBot |fix-attempted=yes }} developed by Andrew Watkins
*[http://www.artificial-immune-systems.org/code/bca/BCA.tar.gz BCA in C++]{{dead link|date=August 2017 |bot=InternetArchiveBot |fix-attempted=yes }} developed by Johnny Kelsey
*[http://www.artificial-immune-systems.org/code/bca/BCA.tar.gz BCA in C++]{{dead link|date=August 2017 |bot=InternetArchiveBot |fix-attempted=yes }} developed by Johnny Kelsey

Revision as of 12:13, 24 July 2020

In artificial immune systems, clonal selection algorithms are a class of algorithms inspired by the clonal selection theory of acquired immunity that explains how B and T lymphocytes improve their response to antigens over time called affinity maturation. These algorithms focus on the Darwinian attributes of the theory where selection is inspired by the affinity of antigen-antibody interactions, reproduction is inspired by cell division, and variation is inspired by somatic hypermutation. Clonal selection algorithms are most commonly applied to optimization and pattern recognition domains, some of which resemble parallel hill climbing and the genetic algorithm without the recombination operator[1].

Techniques

  • CLONALG: The CLONal selection ALGorithm[2]
  • AIRS: The Artificial Immune Recognition System[3]
  • BCA: The B-Cell Algorithm[4]

See also

Notes

  1. ^ Brownlee, Jason. "Clonal Selection Algorithm". Clonal Selection Algorithm.
  2. ^ de Castro, L. N.; Von Zuben, F. J. (2002). "Learning and Optimization Using the Clonal Selection Principle" (PDF). IEEE Transactions on Evolutionary Computation. 6 (3): 239–251. doi:10.1109/tevc.2002.1011539.
  3. ^ Watkins, Andrew; Timmis, Jon; Boggess, Lois (2004). "Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm" (PDF). Genetic Programming and Evolvable Machines. 5 (3): 291–317. CiteSeerX 10.1.1.58.1410. doi:10.1023/B:GENP.0000030197.83685.94. Archived from the original (PDF) on 2009-01-08. Retrieved 2008-11-27.
  4. ^ Kelsey, Johnny; Timmis, Jon (2003). "Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation". Genetic and Evolutionary Computation (GECCO 2003). p. 202. {{cite conference}}: Unknown parameter |booktitle= ignored (|book-title= suggested) (help)