Binary-real coding of solutions in genetic algorithms

Authors

  • Александр Евгеньевич Мочалин Kyiv State Maritime Academy named after hetman Petro Konashevich-Sahaydachniy, str. Frunze 9, Kyiv, 04071, Ukraine https://orcid.org/0000-0002-1326-0181

DOI:

https://doi.org/10.15587/2312-8372.2015.44992

Keywords:

genetic algorithm, binary coding, real coding, optimization

Abstract

The problem of solutions coding in genetic algorithms was reviewed in this paper. The main classes of solutions coding are presented. Advantages and disadvantages of binary coding and real coding in genetic algorithms have been analyzed.

The main purpose of the research is to develop a new way of coding solutions in genetic algorithms. The method developed consists of two stages. Binary coding with the partition of solutions area into small intervals is applied during the first stage. Real coding is used at the second stage. This approach allows one to take advantages of both binary and real coding.

Comparative analysis of the efficiency of the new coding method in genetic algorithms for optimization of complex functions is carried out. Efficiency analysis has shown that the use of binary-real coding in genetic algorithms can solve the optimization problem with a quite high degree of accuracy at medium computational overhead.

Practical recommendations for using binary-real coding in various cases are presented.

Author Biography

Александр Евгеньевич Мочалин, Kyiv State Maritime Academy named after hetman Petro Konashevich-Sahaydachniy, str. Frunze 9, Kyiv, 04071

Candidate of Technical Science, Associate Professor

Department of information technology

References

  1. Gill, Ph. E., Murray, W., Wrigh, M. H. (1985). Practical Optimization. Translated from English. Moscow: Mir, 509.
  2. Kalitkin, N. N. (1978). Chislennye metody. M.: Nauka, 512.
  3. Gladkov, L. A., Kurejchik, V. V., Kurejchik, V. M. (2006). Geneticheskie algoritmy. M.: FIZMATLIT, 320.
  4. Whitley, D. (1994, June). A genetic algorithm tutorial. Statistics and Computing, Vol. 4, № 2, 65–85. doi:10.1007/bf00175354
  5. Holland, J. H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Cambridge: A Bradford Book, 211.
  6. Kurejchik, V. V., Sorokoletov, P. V. (2008). Konceptual'naja model' predstavlenija reshenij v geneticheskih algoritma. Izvestija JuFU, 9, 7–12.
  7. Raidl, G. R. (1999). Weight-codings in a genetic algorithm for the multiconstraint knapsack problem. Proceedings of the Congress on Evolutionary Computation, Vol. 1, 596–603. doi:10.1109/cec.1999.781987
  8. Kurejchik, V. V., Rodzin, S. I. (2010). O pravilah predstavlenija reshenij v jevoljucionny algoritmah. Izvestija JuFU, 7, 13–22.
  9. Belea, R., Beldiman, L. (2003). A new method of gene coding for a genetic algorithm designed for parametric optimization. The Annals of University “Dunarea de Jos” of Galati, 3, 66–71.
  10. Teneev, V. A. (2006). Primenenie geneticheskih algoritmov s veshhestvennym krossoverom dlja minimizacii funkcij bol'shoj razmernosti. Intellektual'nye sistemy v proizvodstve, 1 (7), 93–107.
  11. Chakraborty, U. K., Janikow, C. Z. (2003, November). An analysis of Gray versus binary encoding in genetic search. Information Sciences, Vol. 156, № 3-4, 253–269. doi:10.1016/s0020-0255(03)00178-6
  12. Wright, A. (1991). Genetic algorithms for real parameter optimization. Foundations of Genetic Algorithms, Vol. 1, 205–218. doi:10.1016/b978-0-08-050684-5.50016-1

Downloads

Published

2015-05-28

How to Cite

Мочалин, А. Е. (2015). Binary-real coding of solutions in genetic algorithms. Technology Audit and Production Reserves, 3(2(23), 41–45. https://doi.org/10.15587/2312-8372.2015.44992

Issue

Vol. 3 No. 2(23) (2015): Systems and control processes. Information technologies. Mathematical modeling. Information and control systems

Section

Information Technologies: Original Research

License

Copyright (c) 2016 Александр Евгеньевич Мочалин

Creative Commons License

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

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.