Motif detection inspired by immune memory

Wilson, William; Birkin, Phil; Aickelin, Uwe

doi:10.1057/jors.2010.81

Cited by 2 publications

(1 citation statement)

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“…Immune memory models frequently accompany clonal selection approaches, but the underlying immunology is rather unclear, even regard-ing the existence of such cells. This has made the development of specific models very difficult but possible, as demonstrated by Wilson et al [63] with the motif-tracking algorithm.…”

Section: Clonal Selection Approachesmentioning

confidence: 99%

Artificial Immune Systems

Greensmith

Whitbrook

Aickelin

2010

International Series in Operations Research &Amp; Management Science

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The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.

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Section: Clonal Selection Approachesmentioning

confidence: 99%