Multidimensional and multideme genetic algorithms for the construction of fuzzy systems Original Research Article

In this paper, a real-coded genetic algorithm (GA) is proposed capable of simultaneously optimizing the structure of a system (number of inputs, membership functions and rules) and tuning the parameters that define the fuzzy system. A multideme GA system is used in which various fuzzy systems with different numbers of input variables and with different structures are jointly optimized. Communication between the different demes is established by the migration of individuals presenting a difference in the dimensionality of the input space of a particular variable. We also propose coding by means of multidimensional matrices of the fuzzy rules such that the neighbourhood properties are not destroyed by forcing it into a linear chromosome. The effectiveness of the proposed approach is verified by a variety of simulation examples and is compared with other fuzzy, neuro-fuzzy and fuzzy–genetic approaches in terms of the root-mean-square error (RMSE).