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Hierarchy and Convergance of Immune Networks: Basic Ideas and Premilinary Results

de Castro, Leandro N. and Timmis, Jon (2002) Hierarchy and Convergance of Immune Networks: Basic Ideas and Premilinary Results. In: Timmis, Jon and Bentley, Peter J., eds. 1st Internatonal Conference on Artificial Immune Systems. Unversity of Kent, Canterbury, Kent, pp. 231-240. ISBN 1-902671-32-5. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:13725)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

Abstract

aiNet is an artificial immune network model originally developed to perform automatic data compression. Combined with graph theoretical and statistical clustering techniques, aiNet is a powerful data clustering and classification tool. However, the original aiNet model suffers from the lack of a well-defined stopping criterion and an ad hoc approach to parameter initialization, prior to the training process. This paper has two main goals. First, by assessing convergence criteria employed in a class of artificial neural networks, a suitable stopping criterion can be created for aiNet. Secondly, the paper demonstrates that through the use of a cooling schedule for some of these user-defined parameters, it is not only possible to reduce the importance of their initial values, but also this leads to possible derivation of a hierarchical tree of immune networks. Due to the very limited space available, only the basic ideas of a novel convergence criterion, and an approach to develop a tree of aiNets will be presented, together with an illustrative example.

Item Type: Book section
Uncontrolled keywords: artificial immune systems, clonal selection, immune network
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 17:59 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13725 (The current URI for this page, for reference purposes)
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