The fuzzy cooperative genetic algorithm (FCoGA): the optimisation of a fuzzy model through incorporation of a cooperative coevolutionary method

Abstract

Genetic Algorithms (GA) have been widely used to represent parameters in a fuzzy system. However, when a fuzzy system is applied to a complex problem, GA tends to lose their effectiveness because of the representation complexity of the solution. In this paper, an improved method of fuzzy modelling called as Fuzzy Cooperative Genetic Algorithm (FCoGA) is introduced. Cooperative Coevolution (CC) is applied to the GA by subdividing the chromosome into three sub-chromosomes known as species, and thus reducing the representation complexity of the solution. Furthermore, two-level evaluations in the FCoGA, at the species level and cooperative chromosome level, are introduced to improve the performance. To measure the performance of FCoGA, two benchmark datasets namely Wisconsin Breast Cancer Diagnosis (WBCD) and Pima Indian Diabetes (PID) datasets have been used. The experimental results show that FCoGA slightly improves the accuracy rate and maintains comparable effectiveness with other existing study solutions.