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In the paper an application of differential evolution algorithm to training of artificial neural networks is presented. The adaptive selection of control parameters has been introduced in the algorithm; due to this property only one parameter is set at the start of proposed algorithm. The artificial neural networks to classification of parity-p problem have been trained using proposed algorithm. Results obtained using proposed algorithm have been compared to the results obtained using other evolutionary method, and gradient training methods such as: error back-propagation, and Levenberg-Marquardt method. It has been shown in this paper that application of differential evolution algorithm to artificial neural networks training can be an alternative to other training methods.