Nothing Special   »   [go: up one dir, main page]

Skip to main content

Optimising an Evolutionary Algorithm for Scheduling

  • Conference paper
  • First Online:
Real-World Applications of Evolutionary Computing (EvoWorkshops 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1803))

Included in the following conference series:

  • 584 Accesses

Abstract

This paper examines two techniques for setting the parameters of an evolutionary Algorithm (EA). The example EA used for test purposes undertakes a simple scheduling problem. An initial version of the EA was tested utilising a set of parameters that were decided by basic experimentation. Two subsequent versions were compared with the initial version, the first of these adjusted the parameters at run time, the second used a set of parameters decided on by running a meta-EA. The authors have been able to conclude that the usage of a meta-EA allows an efficient set of parameters to be derived for the problem EA.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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.

Similar content being viewed by others

References

  1. Production Scheduling and Rescheduling with genetic algorithms. Bierwirth C, Mattfeld D. Evolutionary Computation volume 7, No 1. MIT Press 1999.

    Google Scholar 

  2. Co-Evolving Draughts Strategies with Differential Evolution, Chap 9 pp 147–158 in NewIdeas in Optimiszation, Corne D, Dorigo M, Glover F Eds. McGraw-Hill 1999.

    Google Scholar 

  3. Building and Optimising a Scheduling GA. BSc Honours dissertation. Urquhart, N Chisholm, K. (supervisor). Napier University, Edinburgh 1998.

    Google Scholar 

  4. Machine Learning Using a Genetic Algorithm to Optimise a Draughts Program Board Evaluation Function. Chisholm K.J, Bradbeer P.V.G.. Proceedings of IEEE ICEC’97, Indianapolis, USA, 1997.

    Google Scholar 

  5. An introduction to Genetic Algorithms, Mitchell, M. MIT press 1996.

    Google Scholar 

  6. Extensions to a Memetic Timetabling System. Paechter B, Norman M, Luchian H. Practice and theory of Automated Timetabling, Burke and Ross Eds. Springer Verlag 1996.

    Google Scholar 

  7. Evolutionary Computation, Fogel D B. IEEE Press 1995.

    Google Scholar 

  8. Specialised Recombinative Operators for Timetabling Problems, Burke E, Elliman D, Weare R. Proceeding of Evolutionary Computing AISB Workshop Sheffield UK April 1995 ed Fogarty, T. Springer-Verlag 1995.

    Google Scholar 

  9. Optimising a Presentation Timetable Using Evolutionary Algorithms. Paechter B. Lecture Notes In Computer Science No 864, Springer-Verlag 1994.

    Google Scholar 

  10. Two solutions to the General Timetable Problem Using Evolutionary Methods, Paechter B, Cumming A, Luchian H, Petriuc, M. Proceedings of the IEEE World Congress on Computational Intelligence, June 1994.

    Google Scholar 

  11. A Case for Lamarckian Evolution, Ackley D H, Littman M L. Artificial Life III, Langton C ed. Addison-Wesley 1994.

    Google Scholar 

  12. Genetic Algorithms in Search Optimisation and Machine Learning, Goldberg D. Addison-Wesley 1989.

    Google Scholar 

  13. Adapting Operator Probabilities in Genetic Algorithms, Davis L. Proceedings of the third International Conference on Genetic Algorithms. Schaffer J. ed. Morgan Kaufmann 1989.

    Google Scholar 

  14. Optimisation & Control Parameters for Genetic Algorithms, Grefenstette J. IEEE Transactions on Systems, Man and Cybernetics 1986.

    Google Scholar 

  15. Fast Practical Evolutionary Timetabling. Corne D, Ross P, Fang H.. Lecture Notes In Computer Science No 864, Springer-Verlag 1994.

    Google Scholar 

  16. Adapting operator settings in Genetic Algorithms. Tuson A, Ross P. Evolutionary Computation Vol 6 No2. Massachusetts Institute of Technology.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Urquhart, N., Chisholm, K., Paechter, B. (2000). Optimising an Evolutionary Algorithm for Scheduling. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_30

Download citation

  • DOI: https://doi.org/10.1007/3-540-45561-2_30

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67353-8

  • Online ISBN: 978-3-540-45561-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics