Efficient Knowledge-Based Cultural Differential Evolution for Neural Fuzzy Inference Systems

This study proposes a knowledge-based cultural differential evolution (KCDE) for neural fuzzy inference systems (NFIS). The cultural algorithms involve acquiring the belief space from the evolving population space and then exploiting that information to guide the search. The proposed KCDE method adopts the mutation strategies of differential evolution as knowledge sources to influence a population space. These knowledge sources including normative knowledge, situational knowledge, domain knowledge, history knowledge, and topographic knowledge are integrated in the proposed method for optimizing parameters of the NFIS model. Experimental results have demonstrated that the proposed NFIS-KCDE method performs well in nonlinear system control problems.