an approach to reducing overfitting in fcm with evolutionary optimization

fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the fuzzy model. two new cost functions are developed to set the parameters of fcm algorithm properly and the two evolutionary optimization algorithms, i.e. the multi-objective simulated annealing and the multi-objective imperialist competitive algorithm, are employed to optimize the parameters of fcm according to the proposed cost functions. the multi-objective imperialist competitive algorithm is the proposed algorithm.