Skip to main content
Log in

Comparison of Certain Evolutionary Algorithms

  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

A simple evolutionary algorithm is theoretically compared with other methods of this class for a situation in which the operator of transition to new solutions satisfies the so-called monotonicity condition. This algorithm under the monotonicity condition is optimal in the class of evolutionary algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. Goldberg, D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Reading: Addison Wesley, 1989.

    Google Scholar 

  2. Reeves, C.R., Genetic Algorithms for the Operations Researcher, INF J. Comput., 1997, vol. 9, no. 3, pp. 231–250.

    Google Scholar 

  3. Rechenberg, I., Evolutionsstrategie: optimerung technischer Systeme nach Prinzipen der Biologischen Evolution, Stuttgart: Formann-Holzboog Verlag, 1973.

    Google Scholar 

  4. Koza, J.R., Genetic Programming II: Automatic Discovery of Reusable Programs (Complex Adaptive Systems), Cambrige: MIT Press, 1994.

    Google Scholar 

  5. Darwin, C.R., On the Origin of Species, London: Cloves, 1860.

    Google Scholar 

  6. Beyer, H.-G., The Theory of Evolution Strategies. Natural Computing Series, Heidelberg: Springer, 2001.

    Google Scholar 

  7. Reeves, C.R. and Rowe, J.E., Genetic Algorithms: Principles and Perspectives, Norwell: Kluwer, 2002.

    Google Scholar 

  8. Vose, M.D., The Simple Genetic Algorithm: Foundations and Theory, Cambridge: MIT Press, 1999.

    Google Scholar 

  9. Rastrigin, L.A., Statisticheskie metody poiska (Statistical Search Methods), Moscow: Nauka, 1968.

    Google Scholar 

  10. Johnson, D.S., Aragon, C.R., McGeoch, L.A., and Schevon, C., Optimization by Simulated Annealing: An Experimental Evaluation. Part I. Graph Partitioning, Oper. Res., 1989, vol. 37, no. 6, pp. 865–892.

    Google Scholar 

  11. Borisovsky, P.A. and Eremeev, A.V., A Study on Performance of the (1+1)-Evolutionary Algorithm, in Foundations of Genetic Algorithms 7, De Jong, K., Poli R., and Rowe. J., Eds., San Francisco: Morgan Kaufmann, 2003.

    Google Scholar 

  12. Motwani, R. and Raghavan, P., Randomized Algorithms, Cambrige: Cambridge Univ. Press, 1995.

    Google Scholar 

  13. Rudolph, G., Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon, Fundamenta Inf., 1998, vol. 35, no. 1-4, pp. 67–89.

    Google Scholar 

  14. Ermeev, A.V., Genetic Algorithms for Covering Problems, Diskret. Analiz Issl. Operatsii, 2000, vol. 7, no. 1, pp. 47–60.

    Google Scholar 

  15. Eremeev, A.V., Modeling and Analysis of Genetic Algorithms with Tournament Selection, in Proc. Artificial Evolution Conference (AE'99), Fonlupt, C., et al., Eds., Dunkerque: Springer Verlag, 2000, vol. 1829, pp. 84–95.

    Google Scholar 

  16. Aldous, D. and Vazirani, U.U., "Go with the Winners" Algorithms, Proc. IEEE Sympos, Foundations Comput. Sci., 1994, pp. 492–501.

  17. Balas, E., A Sharp Bound on the Ratio Between Optimal Integer and Fractional Covers, Math. Oper. Res., 1984, vol. 9, no. 1, pp. 1–5.

    Google Scholar 

  18. Borisovsky, P.A. and Zavolovskaya, M.S., Experimental Comparison of Two Evolutionary Algorithms for the Independent Set Problem, in Application of Evolutionary Computers, Proc. EvoWorkshops 2000, Cagnoni, S., et al., Eds., Essex: Springer Verlag, 2003, vol. 2611, pp. 154–164.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Borisovskii, P.A., Eremeev, A.V. Comparison of Certain Evolutionary Algorithms. Automation and Remote Control 65, 357–362 (2004). https://doi.org/10.1023/B:AURC.0000019365.10288.58

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:AURC.0000019365.10288.58

Keywords

Navigation