ABSTRACT
Fuzzy evaluation techniques accommodate imprecision, uncertainty, or partial truth, based on fuzzy theory concepts (Zimmerman, 1993; Vasant, 2013). In addition, these techniques can also handle real-world problems with multiple criteria. An important research direction is hybridizing efficient evolutionary approaches with fuzzy evaluation concepts (Mutingi and Mbohwa, 2014). The goal is to develop hybrid fuzzy evolutionary algorithms to provide optimal or near-optimal solutions within a reasonable computation time.
