Optimal Design of Type-2 Fuzzy Membership Functions Using Genetic Algorithms in a Partitioned Search Space

In this paper we describe an evolutionary method for the optimization of type-2 fuzzy systems based on the level of uncertainty. The proposed evolutionary method produces the best fuzzy inference systems (based on the memberships functions) for particular applications. The optimization of membership functions of the type-2 fuzzy systems is based on the level of uncertainty considering three different cases to reduce the complexity problem of searching the solution space.