GENETIC ALGORITHMS IN OPTIMIZATION OF MULTIEXTREMUM FUNCTIONS WITH LARGE PARAMETERS NUMBER

Authors

  • O. Ye. Mochalin Kyiv State Maritime Academy named after hetman Petro Konashevich-Sahaydachniy, Kyiv, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2015-4-9

Keywords:

optimization, genetic algorithm, Soft Computing, genetic operators, coding of solutions.

Abstract

An optimization problem is formally formulated. The main advantages and disadvantages of classical optimization methods are considered
for this problem. Basic prerequisites for the emergence and history development of the instrument of genetic algorithms are highlighted. The current state of the bibliography which is dedicated to the use of search genetic algorithms is analyzed. The basic ideas and underlying principles of genetic algorithms functioning are considered. A review of the most commonly used genetic operators: crossover and mutation, is made. The basic steps of classical genetic algorithm operation are analyzed in detail. The coding problem of solutions in the chromosomes and the selection of individual pairs for crossbreeding are considered. Some common selection strategies are presented as well. The basic benefits of the binary coding of solutions in the chromosomes that using Gray code are formulated. The recommendations are also given on the using of solutions real coding in different situations. The two main classes of parallel genetic algorithms: «islands» and «master – slave», are described. The example of using a genetic algorithm to optimize multiextremal function depending on a large number of parameters is showed. Experimental data are presented which confirm the benefits of graphic processors using in parallel implementation of genetic algorithm. The expediency recommendations of the use of genetic algorithms in different situations are set out.

References

Гладков Л. А. Генетические алгоритмы / Л. А. Гладков, В. В. Курейчик, В. М. Курейчик. – М. : ФИЗМАТЛИТ, 2006. – 320 с. 2. Калиткин Н. Н. Численные методы / Н. Н. Калиткин. – М. : Наука, 1978.– 512 с. 3. Holland J. H. Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence / J. H. Holland. – Cambridge : A Bradford Book, 1992. – 211 p. 4. Goldberg D. Genetic Algorithms in Search, Optimization, and Machine Learning / D. Goldberg. – Boston : Addison-Wesley Professional, 1989. – 432 p. 5. Скобцов Ю. А. Основы эволюционных вычислений / Ю. А. Скобцов. – Донецк : ДонНТУ, 2008. – 326 с. 6. Soft computing: new trends and application / [L. Fortuna, G. Rizzotto, M. Lavorgna, G. Nunnari and other]. – Heidelberg : Springer, 2001. – 267 p. 7. Whitley D. A genetic algorithm tutorial / D. Whitley // Statistics and Computing. – 1994. – Vol. 4. – P. 65–85. DOI: 10.1007/ BF00175354. 8. Дарвин Ч. Происхождение видов путем естественного отбора / Ч. Дарвин. – Спб. : Наука, 2001.– 568 с. 9. Панченко Т. В. Генетические алгоритмы / Т. В. Панчено. – Астрахань : Астраханский университет, 2007. – 87 с. 10. Хэмминг Р. В. Теория кодирования и теория информации / Р. В. Хэмминг. – М. : Радио и связь, 1983. – 176 с. 11. Выбор размера популяции для генетического алгоритма [Электронный документ]. – Режим доступа: http://habrahabr.ru/post/114910/. – 25.12.2014 г. – Загл. с экрана. 12. Иванов Д. Е. Масштабируемый параллельный генетический алгоритм построения идентифицирующих последовательностей для современных многоядерных вычислительных систем / Д. Е. Иванов // Управляющие системы и машины. – Киев, 2011. – № 1. – С. 25–32. 13. Мочалин А. Е. Использование параллельных генетических алгоритмов в методе обработки трассерных изображений BORF / А. Е. Мочалин // Вісник черкаського університету. – Черкаси, 2013. – № 38(291). – С. 58–64.

Downloads

Published

2015-02-23

How to Cite

Mochalin, O. Y. (2015). GENETIC ALGORITHMS IN OPTIMIZATION OF MULTIEXTREMUM FUNCTIONS WITH LARGE PARAMETERS NUMBER. Radio Electronics, Computer Science, Control, (4). https://doi.org/10.15588/1607-3274-2015-4-9

Issue

No. 4 (2015): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

License

Copyright (c) 2016 O. Ye. Mochalin

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons Licensing Notifications in the Copyright Notices

The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.

The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.

The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.