Clonal selection algorithm

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 .

Techniques

• CLONALG: The CLONal selection ALGorithm.
• AIRS: The Artificial Immune Recognition System.
• BCA: The B-Cell Algorithm

See also

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

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
• AIRS in Java developed by Jason Brownlee as a plug-in for WEKA
• BCA in C++ developed by Johnny Kelsey