The work deals with the development of the genetic algorithm, which designs the structure and learning of the neural networks. The fitness function also includes the number of hidden neurons, and thus we obtain the most optimal structure, which is reachable. The own versions of the operators are presented, which manage the entire process of evolution. The result of the work is a library for evolutionary design of neural networks. Moreover, graphical interface for setting parameters and displaying the results was created. In the experimental part the design is compared with other systems and algorithms. Finally, results are reviewed and the process for the following development of the system is outlined
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