Multi-Objective Optimization of Complex Objects in Neural Network Models

Abstract

Background/Objectives: The study considers the modeling technique that applies artificial neural networks analyzing their types and functional principles. Methods: A comparative analysis of the existing methods of structural and parametric synthesis of artificial neural networks has been carried out; the practicability of applying evolutionary approach to solve this problem has been justified. Findings: The multi-objective optimization of the structure of a neural network model has been formalized, given its computational complexity. The genetic algorithm has been adjusted to solve the problems of unconditional optimization of the parameters of the neural network and of selecting its effective structure in multi-objective setting. The results of solving the practical problem prove that the application of the suggested approach can help alleviate the computational complexity of the obtained structures of artificial neural networks. Applications/Improvements: The results of the study make it possible for a decision maker to select neural network model among multiple options, given the required precision and the available computational resources.