Immunology as a Metaphor for Computational ... - Napier University

Chapter 2. Background 28desired properties. Each of the models described operates in a binary antigen universe,and all the models exhibit a common subset of the features of the natural immunesystem, namely they perform recognition via probabilistic detection of pathogens, arecapable of maintaining diversity, are able to learn the structure of the antigenic universeto which they are exposed, and to some extent are able to perform feature extraction.All the models draw inspiration from at least some features observed in the real system,for example the matching functions employed by Potter, Hightower, Forrest and Smithare all based on actual immunological observations.None of the EA-based models explicitly makes use of the concept of memory detectors,although in the work reviewed none of the system had been applied to problemsin which the environment is dynamic, hence the need to use memory detectors isperhaps unnecessary. On the contrary, in all the systems just described detectors areevolved to meet a specific goal, and once attained, the evolution is stopped. Thus, itcould be argued that the memory detectors are merely the set of detectors or librariesthat result from the evolution process. However, if detectors were required to be generatedcontinuously as in ARTIS, an evolutionary approach could run into problems, dueto the time-scales required to perform the evolution. There are two other key featuresof the natural system not exhibited by any of the EA models — self-regulation and costimulation.By definition, an EA must have a fitness function controlling evolution,and hence this can be considered analogous to having a central control function. Coevolution,or the presence of a 2nd signal confirming the nature of the detection, is notincorporated into any of these models. Nevertheless, the EA seems to provide a sensiblestarting point for an artificial immune system, certainly in a binary universe, as itdoes provide a feasible method of searching the detector-space for suitable detectors,rather than randomly generating them as in ARTIS.2.3 Network Models for Machine LearningA number of implementations of artificial immune systems rely on the immune networkmetaphor. As previously mentioned, the network model of the immune systemis disputed by some theoretical immunologists, never the less, significant progress has