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Representation in the (Artificial) Immune System

McEwan, Chris; Hart, Emma

Authors

Chris McEwan



Abstract

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausible dynamical systems, with little overlap between. We propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction and demonstrate how a simplistic interpretation of Perelson’s shape-space formalism may have largely contributed to this dichotomy. In this paper, we motivate and derive an alternative representational abstraction. To do so we consider the validity of shape-space from both the biological and machine learning perspectives. We then take steps towards formally integrating these perspectives into a coherent computational model of notions such as life-long learning, degeneracy, constructive representations and contextual recognition—rhetoric that has long inspired work in AIS, while remaining largely devoid of operational definition.

Citation

McEwan, C., & Hart, E. (2009). Representation in the (Artificial) Immune System. Journal of Mathematical Modelling and Algorithms, 8, 125-149. https://doi.org/10.1007/s10852-009-9104-6

Journal Article Type Article
Acceptance Date Jan 15, 2009
Online Publication Date Feb 17, 2009
Publication Date 2009-06
Deposit Date Jan 29, 2010
Publicly Available Date May 16, 2017
Print ISSN 1570-1166
Electronic ISSN 1572-9214
Publisher BMC
Peer Reviewed Peer Reviewed
Volume 8
Pages 125-149
DOI https://doi.org/10.1007/s10852-009-9104-6
Keywords Artificial immune system;representation; shape-space - learning ;
Public URL http://researchrepository.napier.ac.uk/id/eprint/3478
Publisher URL http://www.springerlink.com/content/w9x47085555wn4k5/

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