The article discusses the use of heuristic algorithms for optimization problems. The algorithms for stochastic
optimization are described, which constitute the main properties of the metaheuristic and its classes. Evolutionary
algorithms are described in general terms. In particular, the main steps and properties of genetic algorithms are presented.
The main goal of this article is to solve the vehicle routing problem using a metaheuristic algorithm. The vehicle
routing problem is a complex combinatorial NP-complete optimization problem. It is shown that the metaheuristic
approach to solving the problem allows one to obtain a suboptimal solution without examining the entire space of possible
solutions. The genetic algorithm belongs to the group of evolutionary algorithms. The definitions are briefly given to the
terms characteristic of the genetic algorithm: gene, chromosome, personality (descendant), population, descendant
operators, crossing, mutation, crossover. Application of the theory of finite automata in a genetic algorithm is described.
The terminology and scheme of the genetic algorithm for solving various problems are proposed.