Optimization of multi-extrema functions using genetic algorithm

Authors

  • І. М. Кравець Національний авіаційний університет

DOI:

https://doi.org/10.18372/2073-4751.2.7648

Abstract

Usage of genetic algorithm for solving optimization problems of functions with multiple extrema, and functions with non-linear not convex range restrictions. Results has shown that using genetic algorithm cannot guarantee finding the best solution though it gives one of optimal solutions with high probability. To improve optimization, it is necessary to perform detailed analysis of crossover and mutation operators for genetic algorithm, as increasing of population or generation numbers does not always provide desired results

Author Biography

І. М. Кравець, Національний авіаційний університет

Інститут комп'ютерних технологій

References

Жуков І.А., Кравець І.М. Розподілення навантаження баз даних в інформаційно-аналітичній системі // Проблеми інформатизації та управління: Зб. наук. пр. – К.: НАУ, 2007. Вип. 4(22). – С. 56–61.

Zhukov I.A., Kravets I.M. “Organization of distribution load database in the analysis and information system”, International scientific technical conference “DESSERT-2009”, Radioelectronic and computer system. Kharkiv, KhAI, 2009. – Vol. 5(39). – P. 25–30.

Zhukov I.A., Kravets I.M. “An algorithm of fragmentation optimization in distributed database”, Advanced computer systems and networks design and application: proceedings of the 4-th International conference ACSN-2009. – Lviv, 2009. – Р. 72–75.

Rosenbrock, H.H. "An Automatic Method for Finding the Greatest or Least Value of a Function.", Computer J. 3, 1960. – P. 175–184.

Darrell Whitley. “A genetic algorithm tutorial” // Statistics and Computing. – Springer Netherlands. Vol. 4(2) / June, 1994. – P. 65–85.

Sharapov R., Lapshin A. “Convergence of genetic lgorithms” // Pattern Recognition and Image Analysis. – MAIK Nauka/Interperiodica distributed exclusively by Springer Science+Business Media LLC. – Vol. 16(3) / July, 2006. – P. 392–397.

Bäck, T. “Evolutionary Algorithms in Theory and Practice” // Evolution Strategies, Evolutionary Programming, Genetic Algorithms. – New York, Oxford: Oxford University Press, 1996.

Pohlheim H. “Advanced Techniques for the Visualization of Evolutionary Algorithms”. Proceedings of 42. International Scientific Colloquium Ilmenau, 1997. – Vol. 3. – P. 60–68.

F. Herrera, M. Lozano, A.M. Sánchez. “Hybrid crossover operators for real-coded genetic algorithms: an experimental study” // Soft Computing – A Fusion of Foundations, Methodologies and Applications. – Springer Berlin / Heidelberg. – Vol. 9(4) / April, 2005. – P. 280–298.

Pohlheim H. GEATbx: Genetic and Evolutionary Algorithm Toolbox for use with Matlab. www.geatbx.com, 1994-2010.

Downloads

Published

2010-10-17

How to Cite

Кравець, І. М. (2010). Optimization of multi-extrema functions using genetic algorithm. Problems of Informatization and Management, 2(30), 95–99. https://doi.org/10.18372/2073-4751.2.7648

Issue

Vol. 2 No. 30 (2010)

Section

Статті

License

The scientific journal adheres to the principles of Open Access and provides free, immediate, and permanent access to all published materials without financial, technical, or legal barriers for readers.

All articles are published in Open Access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Copyright

Authors who publish their works in the journal:

  • retain the copyright to their publications;

  • grant the journal the right of first publication of the article;

  • agree to the distribution of their materials under the CC BY 4.0 license;

  • have the right to reuse, archive, and distribute their works (including in institutional and subject repositories), provided that proper reference is made to the original publication in the journal.