**1. Introduction**

Currently, the technology of the neuroevolutionary synthesis of management and decision-making models is being intensively developed, which is considered as a promising means of implementing intelligent algorithms for analyzing information and management under conflict and uncertainty in real time [1–5]. The effectiveness of the neuroevolutionary approach for solving this class of problems is determined by the ability to take into account uncertain factors, such as conflict uncertainty, the multicriteria of management goals, and the uncertainty of environmental conditions. In this context, it is expedient to formalize the task of training an artificial neural network (ANN) in the form of a multicriteria optimization problem under uncertainty (MCOU). In [6,7], a coevolutionary technology for solving the MCOU problem was developed, which, as the results of computational experiments show, has an extremely high computational complexity. In [8–12], it was shown that a promising area of research is the parallel implementation of computing technology based on graphics processors (GPUs). In this article, a parallel GPU implementation of a coevolutionary technology for solving the MCOU problem is proposed.

In Section 2, the formulation of the ANN training problem is formulated in the form of an MCOU problem, where the principle of vector minimax is applied for its solution. Section 3 presents a parallel GPU-based implementation of the MCOU hierarchical evolutionary algorithm software. Section 4 presents the results of a computational experiment on a test problem.
