Representation in the (Artificial) Immune System


DOI: 10.1007/s10852-009-9104-6

Cite this article as:
McEwan, C. & Hart, E. J Math Model Algor (2009) 8: 125. doi:10.1007/s10852-009-9104-6


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.


Artificial immune system Representation Shape-space Learning 

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Napier UniversityEdinburghUK