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Deep Learning of Neural Networks Using Genetic Algorithms

The technologies of artificial intelligence (AI) are aimed at creating a "thinking machine", that is, a computer system with human-like intelligence. One of the current directions of intellectualization is the use of neural networks with the implementation of their deep learning. The paper analyzes modern approaches to learning neural networks and investigates the possibility of using genetic algorithms to solve the problems of deep learning of neural networks. The purpose of the paper is to develop the scientific and methodological foundations of learning neural networks using genetic algorithms. To achieve the goal, the following tasks were solved: the justification of the approach to learning neural networks using genetic algorithms was carried out and the task of optimizing the learning of neural networks using a genetic algorithm was solved using the example of forecasting the time series of the environmental temperature by the method of shortest descent. A biotechnical complex exposed to external disturbances (external temperature) was chosen as the object on that relevant research was conducted.