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

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.