Evolutionary learning of flexible neuro-fuzzy systems

In the paper the evolutionary strategy (mu, lambda) is applied for learning flexible neuro-fuzzy systems. In the process of evolution we determine: (i) fuzzy inference (Mamdani type or logical type - described by an S-implication), (ii) concrete fuzzy implication, if the logical type system is found in the process of evolution or concrete t-norm connecting antecedents and consequences, if the Mamdani type system is found in the process of evolution, (iii) concrete t-norm for aggregation of antecedents in each rule, (iv) concrete triangular norm describing aggregation operator, (v) shapes and parameters of fuzzy membership functions, (vi) weights describing importance of antecedents of rules and weights describing importance of rules, (vii) parameters of adjustable triangular norms. It should be noted that the crossover and mutation operators are chosen in a self-adaptive way. The method is tested using well known benchmarks.