The paper investigates ways to determine the population size in a genetic algorithm and studies the relationship between the number of individuals and the speed of the algorithm. Methods for determining the optimal number of individuals in a population by different methods are described: depending on the size of the chromosomes, for a tree-like type of chromosomes, in the presence of a noise factor and by the method of a neighboring element with a maximum and minimum boundary. The data obtained by performing each method differ from each other, for this reason, an assessment was made in order to verify the accuracy of theoretical data by comparing them with experimental ones. To conduct experiments, a program was developed on the Unity graphics platform with the ability to change the number of individuals in the population. After receiving the results, the experimental data were compared with the data obtained on the basis of methods for determining the population size in the genetic algorithm from the first part of the work. The experiment showed that the optimal population size lies in the range of 100-160 individuals. With a decrease in their number, the execution time of the task begins to increase significantly, and with an increase beyond the calculated limit, the reduction in execution time does not correspond to the computing resources expended. The experimental data obtained themselves have the smallest error with the method used by the tree representation of chromosomes. The results of the study can be used to select the size of the population during training in order to achieve a better ratio of computing power to learning speed, and a method defined in the course of work can help in theoretical calculations
