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Abstract Context. The problem of automation synthesis of artificial neural networks for further use in diagnosing, forecasting and pattern recognition is solved. The object of the study was the process of synthesis of ANN using a modified genetic algorithm. Objective. The goals of the work are the reducing the synthesis time and improve the accuracy of the resulting neural network. Method. The method of synthesis of artificial neural networks on the basis of the modified genetic algorithm which can be implementing sequentially and parallel using MIMD – and SIMD-systems is proposed. The use of a high probability of mutation can increase diversity within the population and prevent premature convergence of the method. The choice of a new best specimen, as opposed to a complete restart of the algorithm, significantly saves system resources and ensures the exit from the area of local extrema. The use of new criteria for adaptive selection of mutations, firstly, does not limit the number of hidden neurons, and, secondly, prevents the immeasurable increase in the network. The use of uniform crossover significantly increases the efficiency, as well as allows emulating other crossover operators without problems. Moreover, the use of uniform crossover increases the flexibility of the genetic algorithm. The parallel approach significantly reduces the number of iterations and significantly speedup the synthesis of artificial neural networks. Results. The software which implements the proposed method of synthesis of artificial neural networks and allows to perform the synthesis of networks in sequentially and in parallel on the cores of the CPU or GPU. Conclusions. The experiments have confirmed the efficiency of the proposed method of synthesis of artificial neural networks and allow us to recommend it for use in practice in the processing of data sets for further diagnosis, prediction or pattern recognition. Prospects for further research may consist in the introduction of the possibility of using genetic information of several parents to form a new individual and modification of synthesis methods for recurrent network architectures for big data processing. Author Biographies S. D. Leoshchenko, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia Postgraduate student of the Department of Software Tools A. O. Oliinyk, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, PhD, Associate Professor, Associate Professor of the Department of Software Tools S. A. Subbotin, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia Dr. Sc., Professor, Head of the Department of Software Tools V. A. Lytvyn, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia Postgraduate student of the Department of Software Tools, V. V. Shkarupylo, National University of Life and Environmental Sciences PhD, Associate Professor, Associate Professor of Computer Systems and Networks References Mocanu D., Mocanu E., Stone P., Nguyen P., Gibescu M., Liotta A. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science, Nature Communications, 2018, No. 9, pp. 1–17. DOI:10.1038/s41467-018-04316-3.

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