A GA paradigm for learning fuzzy rules

In this paper, we describe a paradigm for learning fuzzy rules using genetic algorithms (GA). We formulate our problem of learning as follows: given a set of linguistic values that characterize the input and output state variables of the system in consideration, derive an n-rule fuzzy control algorithm. The value n represents a specified constraint of the GA in searching for a functional ruleset. The GA learning paradigm is powerful since it requires no prior knowledge about the system's behavior in order to formulate a set of functional control rules through adaptive learning. We present our simulation results using the classical inverted pendulum control problem to demonstrate the effectiveness of the GA learning scheme. Results have shown that the approach has great potential as a tool for the learning of fuzzy control rules, particularly in situations where the knowledge from a human expert is not easily accessible.