Research Output

On AIRS and clonal selection for machine learning.

  Many recent advances have been made in understanding the functional implications of the global topological properties of biological networks through the application of complex network theory, particularly in the area of small-world and scale-free topologies. Computational studies which attempt to understand the structure–function relationship usually proceed by defining a representation of cells and an affinity measure to describe their interactions. We show that this necessarily restricts the topology of the networks that can arise—furthermore, we show that although simple topologies can be produced via representation and affinity measures common in the literature, it is unclear how to select measures which result in complex topologies, for example, exhibiting scale-free functionality. In this paper, we introduce the concept of the potential network as a method in which abstract network topologies can be directly studied, bypassing any definition of shape-space and affinity function. We illustrate the benefit of the approach by studying the evolution of idiotypic networks on a selection of scale-free and regular topologies, finding that a key immunological property—tolerance—is promoted by bi-partite and heterogeneous topologies. The approach, however, is applicable to the study of any network and thus has implications for both immunology and artificial immune systems.

  • Date:

    01 January 2009

  • Publication Status:


  • Publisher


  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science


McEwan, C., & Hart, E. (2008). On AIRS and clonal selection for machine learning. In Artificial Immune Systems, 67-79. doi:10.1007/978-3-642-03246-2_11



Artificial immune recognition system; AIRS; learning algorithm; radial basis functions; clonal selection; iterative descent algorithms;

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