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 Pittsburgtype 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.