Open AccessParameter tuning of a genetic algorithm for finding central vertices in graphs
Author(s) -
Andrey Vlasov,
Andrew A. Khomchenko,
Alexey Faizliev,
С. В. Миронов,
Alexey Grigoriev
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1784/1/012009
Subject(s) - crossover , genetic algorithm , algorithm , population , computer science , mutation , process (computing) , mathematics , artificial intelligence , machine learning , chemistry , demography , sociology , gene , operating system , biochemistry
This paper studies a genetic algorithm for finding the central vertices in graphs. The algorithm uses a different approach to the solution method presentation and describes a new insight in the crossover process. Studies are conducted to find the optimal parameters of the genetic algorithm such us crossover probability, mutation probability and population size. Based on the results, it can be claimed that with the right parameters, our algorithm shows good running time results with high accuracy of the correct answers.