Hybrid self-configuring evolutionary algorithm for automated design of fuzzy logic rule base

In this paper a method for fuzzy logic systems design, which implements the latest developments in this field, is presented. The main evolutionary algorithm uses the Pittsburg-type approach, and the Michigan-type one is used as a mutation operator. A self-configuring technique is used to adjust the algorithm parameters based on their success rates. The novelty here is the algorithm's ability to adjust the probability using either the genetic or heuristic method for the incorporation of a new rule in the rule base. Previously, this was done voluntarily. It is demonstrated that this new algorithm's flexibility does not decrease its performance although it makes it fully automated.