Performing classification with an environment manipulating mutable automata (EMMA)

In this paper a novel approach to performing classification is presented, hypersurface discriminant functions are evolved using genetic programming. These discriminant functions reside in the states of finite state automata which have the ability to reason and logically combine the hypersurfaces to generate a complex decision space. An object may be classified by one or many of the discriminant functions, this is decided by the automata. During the evolution of this symbiotic architecture, feature selection for each of the discriminant functions is achieved implicitly, a task which is normally performed before a classification algorithm is trained. Since each discriminant function has different features, and objects may be classified with one or more discriminant functions, no two objects from the same class need be classified using the same features. Instead, the most appropriate features for a given object are used