O. I. Chumachenko, S. V. Shymkov, A. T. Kot


This paper focuses on the process of adjusting weights and biases of feed-forward ANN during their training process. A new algorithm for tuning artificial neural networks parameters has been proposed to overcome some limitations of existing optimization algorithms and to improve the training process of neural networks. This proposed algorithm combines the benefits of genetic algorithm and gradient-based optimization algorithms to improve the speed of training artificial neural networks and to increase the prediction accuracy of resulting network. The results of artificial neural networks training for classification task using two-level algorithm are presented and compared in performance with various gradient-based optimization algorithms.


Neural networks; parametric tuning; training; optimization; genetic algorithms


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