Cybernetics And Systems Analysis logo
Editorial Board Announcements Abstracts Authors Archive
Cybernetics And Systems Analysis
International Theoretical Science Journal
UDC 004.023
V.P. Shylo1, M.M. Glybovets2, N.M. Gulayeva3, K.V. Nikishchikhina4


1 V.M. Glushkov Institute of Cybernetics, National Academy
of Sciences of Ukraine, Kyiv, Ukraine

v.shylo@gmail.com

2 National University of Kyiv-Mohyla Academy, Kyiv, Ukraine

glib@ukma.kiev.ua

3 National University of Kyiv-Mohyla Academy, Kyiv, Ukraine

ngulayeva@yahoo.com

4 National University of Kyiv-Mohyla Academy, Kyiv, Ukraine

kateryna.nikishchikhina@gmail.com

TOURNAMENT CROWDING GENETIC ALGORITHMS BASED ON GAUSS MUTATION

Abstract. To solve multimodal optimization problems, a new niching genetic algorithm named tournament crowding genetic algorithm based on Gauss mutation is proposed. A comparative analysis of this algorithm to other crowding algorithms and to parallel hill-climbing algorithm has shown the advantages of the proposed algorithm in many cases. The FPR criterion to estimate the distribution of population elements is proposed and it is shown that computation of this criterion is advisable to estimate algorithms solving multimodal problems of finding global and local maxima.

Keywords: multimodal optimization problem, niching genetic algorithms, crowding algorithms, parallel hill-climbing algorithm, fake peak ratio.



FULL TEXT

REFERENCES

  1. Glybovets M.M., Gulayeva N.M. Evolutionary multimodal optimization. In: Optimization Methods and Applications: in Honor of Ivan V. Sergienko’s 80th Birthday. Butenko S., Pardalos P.M., Shylo V. (Eds.). Springer International Publishing, 2017. P. 129–173.

  2. Preuss M. Multimodal optimization by means of evolutionary algorithms. Berlin: Springer, 2015. 175 p.

  3. Reeves C. (ed.). Modern heuristic techniques for combinatorial problems. New York: Halsted Press, 1993.

  4. Aickelin U. An indirect genetic algorithm for set covering problems. J. of the Oper. Res. Society. 2002. Vol. 53. P. 1118–1126.

  5. Sergienko I.V., Shilo V.P. Discrete optimization problems: problems, solution methods, research [in Russian]. Kiev:Nauk. dumka, 2003. 264 p.

  6. Glybovets M.M., Gulaeva N.M. Evolutionary algorithms. Textbook [in Ukrainian]. Kyiv: NaUKMA, 2013. 828 p.

  7. Mahfoud S. Niching method for genetic algorithms. Ph.D. thesis. Urbana, 1995. 251 p.

  8. Singh G., Deb K. Comparison of multi-modal optimization algorithms based on evolutionary algorithms. Proc. 8th Ann. Conf. on Genetic and Evolutionary Computation (GECCO’06) (July 8–12, 2006, Seattle, Washington, USA). Seattle, 2006. P. 1305–1312.

  9. Friedrich T., Oliveto P.S., Sudholt D., Witt C. Analysis of diversity-preserving nechanisms for global exploration. Evolutionary Computation. 2009. Vol. 17, N 4. P. 455–476. https://doi.org/10.1162/evco.2009.17.4.17401.

  10. Osuna E. C., Sudholt D. Runtime analysis of crowding mechanisms for multimodal optimization. IEEE Trans. Evol. Comp. 2019. https://doi.org/10.1109/TEVC.2019.2914606

  11. Mengshoel O., Goldberg D. Probabilistic crowding: deterministic crowding with probabilistic replacement. Proc. the Genetic and Evolutionary Computation Conference (GECCO-99) (13–17 July, 1999, Orlando, FL, USA). Orlando, 1999. P. 409–416.
© 2020 Kibernetika.org. All rights reserved.