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Deep neural networks enjoy high interest and have become the state-of-art methods in many fields of machine learning recently. Still, there is no easy way for a choice of network architecture. However, the choice of architecture can significantly influence the network performance. This work is the first step towards an automatic architecture design. We propose a genetic algorithm for an optimization of a network architecture. The algorithm is inspired by and designed directly for the Kerasdoi:10.15439/2017f241 dblp:conf/fedcsis/VidnerovaN17 fatcat:au5zjqebvvayrj2xsxhkfje6oq