Solving optimization problems on cognitive models using genetic algorithms

Cognitive models are one of the effective tools for solving strategic management problems. The traditional use of cognitive models is limited to modeling problems - optimization problems on these models are not solved due to their non-polynomial complexity when it is impossible to use algorithms for rejecting solutions. The article estimates the computational complexity of optimization problems of the most general type and proposes to use real-coded genetic algorithms for their solution. The specifics of encoding information about the parameters of variants for solving the problem in the chromosomes of the genetic algorithm and the main features of the various stages of the algorithm are considered.