The combination of wavelet theory and neural networks has lead to the development of wavelet networks (wavenets). Wavenets are feed-forward neural networks which used wavelets as activation functions, and the basis used in wavenet has been called wavelons. Wavenets are successfully used in identification problems. The strength of wavenets lies in their capabilities of catching essential features in “frequency-rich” signals. In wavenet, both the translation and the dilation of the wavelets (wavelons) are optimized beside the weights. The wavenet algorithm consist of two processes: the selfconstruction of networks and the minimization of errors. In the first process, the network structure is determined by using wavelet analysis. In the second process, the approximation errors are minimized. The wavenets with different types of frame wavelet function are integrated for their simplicity, availability, and capability of constructing unknown nonlinear function. Thus, wavelet can identify the localization of unknown function at any level. In addition, genetic algorithms (GAs) are used successfully in training wavenets since GAs reaches quickly the region of the optimal solution. Tests show that GA obtains best weight vector and produces a lower sum square error in a short period of time.