Accuracy-based neuro and neuro-fuzzy classifier systems
Larry Bull, Toby O’Hara · 2002
Learning Classifier Systems traditionally use a binary representation with wildcards added to allow for generalizations over the problem encoding. However, the simple scheme can be limiting in complex domains. In this paper we present results from the use of neural network-based representation schemes within the accuracy-based XCS. Here each rule's condition and action are represented by a small neural network, evolved through the actions of the genetic algorithm. After describing the changes required to the standard production system functionality, optimal performance is presented using multi-layered perceptrons to represent the individual rules. Results from the use of fuzzy logic through radial basis fuction networks are then presented. In particular, the new representation scheme is shown to produce systems where outputs are a function of the inputs.