Research Output

Representation in the (Artificial) Immune System.

  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.

  • Type:

    Article

  • Date:

    31 May 2009

  • Publication Status:

    Published

  • Publisher

    Springer-Verlag

  • DOI:

    10.1007/s10852-009-9104-6

  • ISSN:

    1570-1166

  • Library of Congress:

    QA75 Electronic computers. Computer science

Citation

McEwan, C. & Hart, E. (2009). Representation in the (Artificial) Immune System. Journal of Mathematical Modelling and Algorithms. 8, 125-149. doi:10.1007/s10852-009-9104-6. ISSN 1570-1166

Authors

Keywords

Artificial immune system;representation; shape-space - learning ;

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