Control Systems and Computers, N2, 2018, Article 7


Upr. sist. maš., 2018, Issue 2 (274), pp. 68-79.

UDK 6:004.8

O.GMoroz, junior research scientist, Departament for Technologies of Inductive Modelling, International Research and Training Center for Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine,


Introduction. Most of the existed traditional methods of optimization and identification models of complex systems are not effective in solving the problems of finding a globally optimal solution for the undifferentiated, multimodal, nonlinear, and multi-objective tasks. This leads to the development of approximate methods, in particular the meta-heuristic global search methods such as the genetic algorithm. The effectiveness of their application is confirmed by the numerous successful practical implementations.

Purpose. The purpose of the research is to examine more comprehensively the theoretical and practical aspects of the genetic algorithms and their capabilities for solving optimization and system identification problems.

Methods. The goal of this article is achieved by presenting a comprehensive survey of the main publications in the area of genetic algorithms theory and their application to the complex optimization tasks.

Results. The theoretical and applied aspects of genetic algorithms are considered in detail. Some examples of modern global optimization and model identification problems successfully solved by genetic algorithms are presented.

Conclusion. The genetic algorithms are a powerful tool for solving various complex global optimizations and modeling tasks that are characterized by incompleteness of input information, multi-objectiveness, large dimensionality, nonlinearity, lack of analytical description of objective function etc. The effectiveness of GA depends on its type, the choice of genetic operators, encoding method of potential solutions, etc. The theoretical aspects are well developed for simple GAs. Genetic algorithms have a great perspective and require further improvements, developments and more general theoretical justification.

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Keywords: global optimization, genetic algorithm, system identification.


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Received 28.03.2018