Genetic algorithms (GA) are an effective tool for solving various problem-oriented tasks. GA-based software and tool complexes are widely used in research and design of reconfigurable hardware. The methods of improving GA are adaptive genetic operators, methods of selecting chromosomes and calculating the fitness function. The purpose of the study is to substantiate the methodology of structural-parametric synthesis to improve the efficiency of GA implementation. The analysis of methods for optimizing GA parameters, as well as a structural analysis of their hardware and software implementation, allowed us to substantiate the methodology for improving GA. The approaches to adaptive modification of GA parameters are systematized, on the basis of which new technical solutions for controllers for neural network control have been developed. Based on the analysis of adaptive GA optimization methods, the scheme of a multifunctional controller is substantiated, which additionally includes blocks for counting iterations and optimizing the parameters of genetic procedures (RU patent. 2843987), which improves the quality of control. It is shown that the epoch number and the duration of the algorithm implementation can be used as control parameters of the GA adaptation process. In order to avoid falling into local extremes, the use of an indicator approaching the extreme and the current TF values of individual individuals is justified in order to heuristically modify the parameters. The proposed system classification allows us to build a family of more productive technical solutions for controllers that implement the principle of adaptive modification, as well as hybrid optimization options.