A Modified Genetic Algorithm for Training Adaptive Fuzzy Systems
Dia I. Abualnadi, Jamal S. Rahhal · Intelligent Automation & Soft Computing · 2008
Abstract Adaptive Fuzzy Logic Systems trained by genetic evolution of their pazameters are presented in this work. This technique is based on the aggregation of pazameter perturbations. Neither the evaluation function nor the membership functions, have to be differentiable as required in most optimization techniques. In the classical Genetic Algorithms, the solution space of each pazameter should be specified in the genetic search. The proposed technique does not specify the solution space of the pazameters of the fuzzy logic system. It specifies the ranges of the perturbations of the pazameters which will aggregate to fmd the optimum parameters for the Fuzzy Logic System. Computer simulation showed that the proposed technique reached an optimal solution for the Adaptive Fuzzy Logic parameters with a higher convergence rate than that of the classical GA.