Skip to main content

Advertisement

SpringerLink
Log in

Estimation of selection efficiency from the increment of adaptation

  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

A genetic algorithm is proposed that uses not the adaptation (fitness) itself but the increment of adaptation during selection. The algorithm proposed makes it possible to avoid the premature convergence of computations. The efficiency of the algorithm is experimentally estimated.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford Univ. Press, Oxford (1996).

    Google Scholar 

  2. J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence, Bradford Book Edition, London (1994).

  3. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Boston (1989).

    MATH  Google Scholar 

  4. M. Mitchell, An Introduction to Genetic Algorithms, MIT Press, Cambridge (1996).

    MATH  Google Scholar 

  5. D. Fogel, Evolutionary Computation toward a New Philosophy of Machine Intelligence, IEEE Press, New York (1995).

    Google Scholar 

  6. D. H. Wolpert and W. G. Macready, “No-Free-Lunch theorems for optimization,” IEEE Trans. Evolut. Comput., 1, No. 1, 67–82 (1997).

    Article  Google Scholar 

  7. X. Yao and Y. Lui, “Fast evolution strategies,” Control and Cybernetics, 26(3), 467–496 (1997).

    MATH  MathSciNet  Google Scholar 

  8. A. V. Homich and L. A. Zhukov, “Optimization based on evolution with selection according to the value of the increment of fitness: Modeling of nonequilibrium systems,” in: Trans. VIIth All-Russian. Seminar, IVM SO RAN, Krasnoyarsk (2004), pp. 180–183.

    Google Scholar 

  9. D. Yuret, “Dynamic hill climbing: Overcoming the limitations of optimization techniques,” in: Proc 2nd Turkish Symp. On AI and ANN, Bogazici Univ., Istambul, Turkey (1993), pp. 254–260 (http: //home.ku.edu.tr/∼dyuret/pub/aiexp94.html).

  10. L. D. Whitlay, “The genitor algorithm and selective pressure: Why rank based allocation of reproductive trials is best,” in: Proc. 3rd Intern. Conf. on Genetic Algorithms, Morgan-Kauffman, San Mateo (CA) (1989), pp. 35–41.

  11. L. Eshelman, “The CHC adaptive search algorithm,” in: G. J. E. Rawlins (ed.), Proc. 1th Workshop on Algorithms, Morgan Kauffman, San Mateo (CA) (1991), pp. 265–283.

  12. V. S. Gordon and D. Whitlay, “Serial and parallel genetic algorithms as function optimizers,” in: S. Forrest (ed.), Proc. 5th Intern. Conf. on Genetic Algorithms, Morgan Kauffman, San Mateo (CA) (1993), pp. 177–183.

Download references

Author information

Authors and Affiliations

  1. Siberian State Technological University, Krasnoyarsk, Russia

    A. V. Homich & L. A. Zhukov

Authors
  1. A. V. Homich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. A. Zhukov
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

__________

Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 70–75, May–June 2006.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Homich, A.V., Zhukov, L.A. Estimation of selection efficiency from the increment of adaptation. Cybern Syst Anal 42, 366–371 (2006). https://doi.org/10.1007/s10559-006-0073-8

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-006-0073-8

Keywords

Search

Navigation