Clonal selection algorithm explained

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

• Artificial immune system
• Biologically inspired computing
• Computational immunology
• Computational intelligence
• Evolutionary computation
• Immunocomputing
• Natural computation
• Swarm intelligence

External links

• Clonal Selection Pseudo code on AISWeb
• CLONALG in Matlab developed by Leandro de Castro and Fernando Von Zuben
• 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)
• AIRS in C++ developed by Andrew Watkins
• BCA in C++ developed by Johnny Kelsey

Notes and References

1. Web site: Clonal Selection Algorithm. Brownlee. Jason. Clonal Selection Algorithm.
2. de Castro . L. N. . Von Zuben, F. J. . [ftp://ftp.dca.fee.unicamp.br/pub/docs/vonzuben/lnunes/ieee_tec01.pdf Learning and Optimization Using the Clonal Selection Principle ]. IEEE Transactions on Evolutionary Computation . 6 . 3 . 2002 . 239–251 . 10.1109/tevc.2002.1011539.
3. Watkins . Andrew . Timmis, Jon . Boggess, Lois . Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm . Genetic Programming and Evolvable Machines . 5 . 3 . 2004 . 291–317 . 10.1023/B:GENP.0000030197.83685.94 . 10.1.1.58.1410 . 13661336 . 2008-11-27 . https://web.archive.org/web/20090108045124/http://www.cse.msstate.edu/%7Eandrew/research/publications/airs.pdf . 2009-01-08 . dead .
4. Kelsey . Johnny . Timmis, Jon . Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation . Genetic and Evolutionary Computation (GECCO 2003) . 2003 . 202 . 10.1007/3-540-45105-6_26 . 10.1.1.422.515 .