COMPARING EVOLUTIONARY ALGORITHMS ON TUNING THE PARAMETERS OF FUZZY WAVELET NEURAL NETWORK (RESEARCH NOTE)
P Arab Khedri, Mahdi Eftekhari, Ramin Maazallahi · 2013
In recent years Fuzzy Wavelet Neural Networks (FWNNs) have been used in many areas. Function approximation is an important application of FWNNs. One of the main problems in effective usage of FWNN is tuning of its parameters. In this paper several different evolutionary algorithms including Genetic Algorithm (GA), Gravitational Search Algorithm (GSA), Evolutionary Strategy (ES), Fast Evolutionary Strategy (FES) and variants of Differential Evolutionary algorithms (DE) are used for adjusting these parameters on five test functions. The obtained results are compared based on some measures by using multiple non-parametric statistical tests. The comparison reveals the superiority of some variants of DE in terms of convergence behavior and the ability of function approximation. Keywords– Fuzzy wavelet neural networks, function approximation, evolutionary algorithms, nonparametric statistical test