Skip to main content
SpringerLink
Log in

Robust Parallel Genetic Algorithms with Re-initialisation

  • Conference paper
Parallel Problem Solving from Nature - PPSN VIII (PPSN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3242))

Included in the following conference series:

Abstract

The influence of different parallel genetic algorithm (PGA) architectures on the GA convergence properties is analysed. Next, two proposed versions of these PGA architectures are compared – homogenous and heterogeneous. Finally the effect of re-initialisation in some partial populations on the PGA convergence has been analysed. The proposed PGA modifications are useful mainly in case of non-smooth cost function optimisation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 74.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bäck T.: Selective pressure in evolutionary algorithms: A characterization of selection mechanisms. In: ICEC 1994, pp. 57–62 (1994)

    Google Scholar 

  2. Cantú-Paz, E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (1995)

    Google Scholar 

  3. Chipperfield, A.J., Fleming, P.J.: Parallel genetic algorithms: A survey. ACSE Research Report No.518, University of Sheffield (1994)

    Google Scholar 

  4. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Rawlins, G. (ed.) Foundation of Genetic Algorithms, Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  5. Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms, Concepts and Deigns. Springer, Heidelberg (2001)

    Google Scholar 

  6. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Berlin (1996)

    MATH  Google Scholar 

  7. Mitchell, M.: An introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    Google Scholar 

  8. Oei, C.K., Goldberg, D.E., Chang, S.J.: Tournament selection, niching, and the preservation of diversity. Technical Report 91011, University of Illinois Genetic algorithm laboratory (1991)

    Google Scholar 

  9. Schwefel, H.P.: Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Birkhäuser, Basel (1977)

    Google Scholar 

  10. Whitley, D.: The GENITOR Algorithm and Selection Pressure.: Why Rank-based Allocation of Reproductive Trials is Best. In: Proceedings of the Conf. of Genetic Algorithms, pp. 116–121. Morgan Kaufmann Publ., San Mateo (1989)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automatic Control Systems, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 812 19, Bratislava, Slovak Republic

    Ivan Sekaj

Authors
  1. Ivan Sekaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Xin Yao

  2. Automated Scheduling, Optimisation and Planning Group, School of Computer Science & IT, University of Nottingham, NG8 1BB, Nottingham, UK

    Edmund K. Burke

  3. Intelligent Systems Group Department of Computer Science and Artificial Intelligence, University of the Basque Country, Paseo Manuel de Lardizábal 1, 20018, San Sebastian, Donostia, Spain

    José A. Lozano

  4. University of the West of England, Bristol, United Kingdom

    Jim Smith

  5. GeNeura Team, Depto. Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain

    Juan Julián Merelo-Guervós

  6. School of Computer Science, The University of Birmingham, B15 1TT, Edgbaston, Birmingham, UK

    John A. Bullinaria

  7. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, Great Britain

    Jonathan E. Rowe

  8. School of Computer Science, University of Birmingham, United Kingdom

    Peter Tiňo

  9. School of Computer Science, The University of Birmingham, B15 2TT, Birmingham, UK

    Ata Kabán

  10. Faculty of Computer Science, Dortmund University of Technology, 44221, Dortmund, Germany

    Hans-Paul Schwefel

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sekaj, I. (2004). Robust Parallel Genetic Algorithms with Re-initialisation. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30217-9_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23092-2

  • Online ISBN: 978-3-540-30217-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation