Performing automatic target detection with evolvable finite state automata

摘要

In this paper, a novel approach for performing classification is presented. Discriminant functions are constructed by combining selected features from the feature set with simple mathematical functions such as +, −, ×, ÷ , max, min. These discriminant functions are capable of forming non-linear 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.