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Abstract
The purpose of this work is to summarize the results of research concerning the application of genetic algorithms, since in solving problems of complex systems optimization situations often make it difficult or impossible to use classical methods. To solve this problem, research is carried out on the functions of Akli, Rastrigin, Shekel, complaints handling functions and Rosenbrock functions. The studies are conducted on three starting point scattering algorithms: LPτ sequence, UDC sequences and universal random variation. As a result of the analysis, the option of initialization, selection, recombination, mutation and coding of this algorithm according to given test functions for the data of the scatter of initial points is chosen. The effective parameters of the genetic algorithm according to the results of research are established.
Highlights
To date, the genetic algorithm for global optimization [1 - 4] has many options for the algorithm parameters
The genetic algorithm for global optimization [1 - 4] has many options for the algorithm parameters. When applying this algorithm in practice, we would like to know which options to use in the first place, i.e. what options of parameters give the greatest efficiency, more precisely, what combination of them gives the greatest efficiency on test functions
Studies were conducted on Ackley, Rastrigin, Shekel, Grivanka and Rosenbrock functions [2, 5, 6]
Summary
The genetic algorithm for global optimization [1 - 4] has many options for the algorithm parameters When applying this algorithm in practice, we would like to know which options to use in the first place, i.e. what options of parameters give the greatest efficiency, more precisely, what combination of them gives the greatest efficiency on test functions. LP sequence [3, 7], UDC sequence, uniform random scatter - very interesting and effective scatter algorithms of initial points. Recent studies in this area were carried out in the works of the scientist [4, 8, 9]
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