Multidimensional and multideme genetic algorithms for the construction of fuzzy systems

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 de®ne the fuzzy system. A multideme GA system is used in which various fuzzy systems with di€erent numbers of input variables and with di€erent structures are jointly optimized. Communication between the different demes is established by the migration of individuals presenting a di€erence 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 e€ectiveness of the proposed approach is veri®ed 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).