Efficient reinforcement hybrid evolutionary learning for recurrent wavelet-based neuro-fuzzy systems

Abstract

This paper proposes a recurrent wavelet-based neuro-fuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA), performs the structure/parameter learning for dynamically constructing the RWNFS. That, is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. An illustrative example was conducted to show the performance and applicability of the proposed R-HELA method.