MODEL OF THE PROCESS OF MANAGING A GENETIC ALGORITHM USING AN ARTIFICIAL NEURAL NETWORK BECAUSE OF STRUCTURAL-PARAMETRIC SYNTHESIS OF LARGE DISCRETE SYSTEMS

In intelligent decision support systems aimed at solving the problems of structurally parametric synthesis of models of large discrete systems with a given behavior, based on genetic algorithms, it is often required to increase speed using not only hardware, but also mathematical ones. In this paper, we consider the processes that arise when using an evolutionary procedure consisting of four genetic algorithms adapted to the task of synthesizing under the control of an artificial neural network. Each model that is part of the decision-making block fulfills its function in the task of structural-parametric synthesis of simulation models of large discrete systems. That is, it searches for solutions based on: models of elements that make up the synthesized object; interelement connections; initial parameters of the functioning of the elements; parameters of the elements of the synthesized system, which can change in the synthesized model during its operation. As a control, the use of an artificial neural network is considered, which makes adjustments to the functioning parameters of the operators of the genetic algorithm and (or) the connection of various combinations of evolutionary procedures depending on the convergence of the evolutionary procedure. When creating a process model, modern methodologies IDEF0 and IDEF3 were used, aimed at solving problems of system analysis.

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