About the influence of features of fitness-functions on the convergence of the genetic algorithm

I.O. Lukianov, F.A. Lytvynenko

Abstract


The adaptive capabilities of a parallel version of a multipopulation genetic algorithm are considered depending on the characteristics of certain classes of fitness-functions. Ways are proposed to increase the rate of convergence to the optimal solution based on effective control of algorithm parameters and strategies for the exchange of chromosome-solutions between populations. The results of computer experiments with the optimization of fitness-functions with various ratios of insignificant and significant factors are presented. The dependence of the convergence rate of the algorithm in the presence of a random effect on the values of fitness-functions is studied.

Problems in programming 2020; 2-3: 362-367


Keywords


multipopulation genetic algorithm; insignificant factors; computational model

References


Pepelyaev V. On evolutionary approaches to the optimization of simulation modeling. Computer Mathematics. 2005. N 1. P. 48–54.

Pepelyaev V. On the planning of optimization-simulation experiments. Cybernetics and system analysis. 2006. N 6. P. 112–125.

CrossRef

Pepelyaev V., Cherny Y. On the possibilities of using genetic algorithms in optimization and simulation experiments. Theory of Optimal Solutions. 2019. P. 100–109.

Lukianov I., Lytvynenko F., Krykovliuk O. Features of the implementation of a parallel version of the multipopulation genetic algorithm. Computer math. 2018. N 2. P. 21–29.

Lukianov I., Lytvynenko F., Krykovliuk O. On increasing the efficiency of a parallel version of a multipopulation genetic algorithm. Theory of optimal solutions. 2019. N 18. P. 116–122.

Lytvynenko F., Lukianov I., Krykovliuk O. Using the diversity of the initial population in a multipopulation genetic algorithm. Computer Mathematics. 2019. N 1. P. 116–123.

Lukianov I., Lytvynenko F., Koval V. On the choice of the size of the initial population for the parallel version of the multipopulation genetic algorithm. Collection of materials of the IntSol-2019 conference “Decision Making Theory”. 2019. P. 95–96.




DOI: https://doi.org/10.15407/pp2020.02-03.362

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