Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
The affirmation of self: a new perspective on the immune system
Artificial Life
A comparative analysis of artificial immune network models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Tolerance vs intolerance: how affinity defines topology in an idiotypic network
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Not all balls are round: an investigation of alternative recognition-region shapes
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Application areas of AIS: the past, the present and the future
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Immune Systems and Computation: An Interdisciplinary Adventure
UC '08 Proceedings of the 7th international conference on Unconventional Computing
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The idiotypic network has a long and chequered history in both theoretical immunology and Artificial Immune Systems. In terms of the latter, the drive for engineering applications has led to a diluted interpretation of the immunological models. Research inspired by theoretical immunology has produced compelling models of self-organised tolerance and immunity, but currently fail to have any practical engineering applicability. In this paper, we briefly discuss the engineering applicability of "self-affirming" idiotypic networks, leading to a suggestion that the "Third Generation" network models represent a way forward in this respect. Results obtained by implementing and extending a discrete model of this type of network suggest that the extended prototype is capable of two context-dependent modes of immune response, readily applicable to unsupervised machine-learning.