EvoNF: A Framework for Optimization of Fuzzy Inference Systems Using
  Neural Network Learning and Evolutionary Computation
  Several adaptation techniques have been investigated to optimize fuzzy
inference systems. Neural network learning algorithms have been used to
determine the parameters of fuzzy inference system. Such models are often
called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model
there is no guarantee that the neural network learning algorithm converges and
the tuning of fuzzy inference system will be successful. Success of
evolutionary search procedures for optimization of fuzzy inference system is
well proven and established in many application areas. In this paper, we will
explore how the optimization of fuzzy inference systems could be further
improved using a meta-heuristic approach combining neural network learning and
evolutionary computation. The proposed technique could be considered as a
methodology to integrate neural networks, fuzzy inference systems and
evolutionary search procedures. We present the theoretical frameworks and some
experimental results to demonstrate the efficiency of the proposed technique.
