Evolving Automatic Target Detection Algorithms by LogicallyCombining Decision Spaces

In this paper a novel approach to performing classification is presented. Discriminant functions are constructed by combining selected features from the feature set with simple mathematical functions such as . These discriminant functions are capable of forming nonlinear discontinuous hypersurfaces . For multimodal data more than one discriminant function may be combined with logical operators before classification is performed. An algorithm capable of making decisions as to whether a combination of discriminant functions is needed to classify a data sample, or whether a single discriminant function will suffice, is developed. The algorithms used to perform classification are not written by a human. The algorithms are learnt, or rather evolved, using Evolutionary Computing techniques.