Artificial evolution of fuzzy rule bases which represent time: A temporal fuzzy classifier system

Abstract

This paper reviews experience in using a temporal fuzzy classifier system which explicitly represents time in the classifier syntax by augmenting individual classifiers with temporal tags. The contribution of each activated classifier to the composite system output is modulated in time according to the parameters of the fuzzy temporal tag associated with that classifier. This feature allows the learning algorithm-in this case, the genetic algorithm-to explore and exploit temporal features of the environment in which the classifier system might be expected to operate. Experimental results in applying the fuzzy temporal classifier system to control of a time-delayed plant and to the distributed problem of adaptive routing in communication networks are presented.