Application of Artificial Neural Networks in Genetic Algorithm Control Problems
Д.А. Петросов
Abstract
In contemporary intelligent decision support systems, there is still a problem associated with increasing the performance speed of the structural-parametric synthesis of large discrete systems with a given behavior based on genetic algorithms. Currently, there are two main research areas that are designed for mathematical or hardware performance speed improvement. One way to improve hardware performance speed is the use of parallel computing, which includes general-purpose computing on graphics processing units (GPGPU). This article deals with the possibility of improving the performance speed of intelligent systems using the mathematical tool of artificial neural networks by introducing a control module of the genetic algorithm directly when performing the synthesis of solutions. Control of the structural-parametric synthesis process is achieved by predicting and evaluating the state of the genetic algorithm (convergence, attenuation, finding the population in local extremes) using artificial neural networks. This allows changing the operating parameters directly in the course of decision synthesis, changing their destructive ability relative to the binary string, which leads to a change in the trajectory of the population in the decision space, and as a result, should help to improve the performance speed of intelligent decision support systems.