The article discusses the concept of choosing the sequence of control actions in order to minimize the possibility of the system state transition to an adverse one. For this purpose, the bionic model based on the synthesis of information approach, neural networks and a genetic algorithm is developed. The functionality of each of the model elements and their interaction are presented in this paper. Special attention is paid to neuroevolutionary interaction. At the same time, information about control actions is encapsulated in the gene, which allowed increasing the functionality of the algorithm due to multidimensional data representation. The article describes the principle of data representation in bionic models, which differs from the existing ones by the possibility of explicit or implicit representation of the control action in the chromosome. In the explicit representation one neural network is formed, it describes the effect of any of the control actions involved in the training. An implicit view creates a set of models, each of which describes the effect of only one control action. A brief description of the software implemented in the Python programming language is provided.
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