Negative Selection Algorithms: from the Thymus to V-detector

Ji, Zhou. Ph.D. The University of Memphis. August, 2006. Negative selection algorithms: from the thymus to V-detector. Major Professor: Dipankar Dasgupta, Ph.D. Artificial Immune Systems (AIS) is a research area of developing computational methods inspired by biological immune systems. The approach of negative selection algorithms (NSA) is one of the major models of AIS. This dissertation does a comprehensive survey of NSA and highlights the key components that define a negative selection algorithm. It demonstrates that the so-called ‘negative selection algorithms’ have been a very broad interpretation compared with its biological archetype and differ from one another in strategy, applicability and implementation. This work proposed a new negative selection algorithm called V-detector. It has several important features that alleviate some difficulties in negative selection algorithms. (1) Statistical techniques are integrated in the detector generation process to estimate the detector coverage. (2) Detectors with variable coverage are used in a highly efficient manner to achieve maximum coverage. (3) A boundary-aware algorithm is proposed to interpret the training data set as a whole, instead of considering them as independent points. It shows that negative selection’s certain learning property cannot be replaced by straightforward positive selection. (4) The main components of V-detector can be customized for different data/detector representations and detector generation mechanisms. This generic characteristic could connect the gap between different negative selection algorithms.