Abstract
This work addresses the development of a hybrid approach to solving threeperson polymatrix games (hexamatrix games). On the one hand, this approach is based on the reduction of the game to a nonconvex optimization problem and the Global Search Theory proposed by A.S. Strekalovsky for solving nonconvex optimization problems with (d.c.) functions representable as a difference of two convex functions. On the other hand, to increase the efficiency of one of the key stages of the global search — constructing an approximation of the level surface of a convex function that generates the basic nonconvexity in the problem under study — operators of genetic algorithms are used. The results of the first computational experiment are presented.