Evolutionary generation of rule base in TSK fuzzy model for real estate appraisal

Abstract

Takagi-Sugeno-Kang-type fuzzy model to assist with real estate appraisals is described and optimized using evolutionary algorithms Two approaches were compared in the paper. The first one consisted in learning the rule base and the second one in combining learning the rule base and tuning the membership functions in one process. Five TSK-type fuzzy models comprising 3 or 4 input variables referring to the attributes of a property were evaluated. The evolutionary algorithms were based on Pittsburgh approach with the real coded chromosomes of constant length comprising whole rule base or both the rule base and all parameters of all membership functions. The experiments were conducted using training and testing sets prepared on the basis of actual 134 sales transactions made in one of Polish cities and located in a residential section.