METHOD OF DETERMINING THE STRUCTURE OF THE MODEL OF OPTIMAL COMPLEXITY - BOYKO, NATALIYA; BLAZHEVSRYY, STEPAN (Herald of Khmelnytskyi National University. Technical sciences, 2022)
Genetic algorithms belong to the class of heuristic algorithms. They provide a global solution to optimization problems and are a promising area in optimization and modelling. Genetic algorithm development is reproduced in more sophisticated evolutionary methods that use real numbers and statistics. One such method is the Group method of accounting for algorithms. The paper analyzes the principles of genetic algorithms their logic, compares their work with the Group method of argumentation, and explores building bridges. An overview of publications on this issue, which became the impetus for the development of this topic. Genetic algorithms and group argumentation are tools for many applications, but keep in mind that they have limitations: they give only evaluative answers and require little computational time, and you need refined input. Therefore, to improve the approach to implementing existing programs, it is necessary to conduct constant research and comparison. The genetic algorithm and the method of group use of arguments are considered. The principle of operation and construction of genetic algorithms is described. The focus of the process of group use of discussions and its range of algorithms is given. Structurally parametric identification and prediction of the method of group consideration of arguments are used. Self-organizations of models are considered to determine the structure of the model of optimal complexity. The genetic algorithm and group method of viewing statements for similarities and differences are considered. The advantages and disadvantages of research methods are revealed. The task of building a bridge is performed by a genetic algorithm, which is due to its analysis with the Group method of taking into account arguments and proving that the genetic algorithm is the best solution for this task.
Keywords: genetic algorithm; method of group use of arguments; data mining; neural networks; algorithm; population; fitness; crossover; mutation