Skip to main content

Model Driven Rapid Prototyping of Heuristic Optimization Algorithms

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5717)

Abstract

In this paper the authors describe a model driven approach for the development of heuristic optimization algorithms. Based on a generic algorithm model, several operators are presented which can be used as algorithm building blocks. In combination with a graphical user interface, this approach provides an interactive and declarative way of engineering complex optimization heuristics. By this means, it also enables users with little programming experience to develop, tune, test, and analyze heuristic optimization techniques.

Keywords

  • Reduction Operator
  • Selection Operator
  • Heuristic Optimization
  • Operator Graph
  • Combine Operator

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The work described in this paper was done within HEUREKA!, the Josef Ressel centre for heuristic optimization sponsored by the Austrian Research Promotion Agency (FFG). Visit http://heureka.heuristiclab.com for more details.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-04772-5_94
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-04772-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Collet, P., Lutton, E., Schoenauer, M., Louchet, J.: Take it EASEA. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 891–901. Springer, Heidelberg (2000)

    CrossRef  Google Scholar 

  2. Doerner, K.F., Gendreau, M., Greistorfer, P., Gutjahr, W., Hartl, R.F., Reimann, M. (eds.): Metaheuristics: Progress in Complex Systems Optimization. Operations Research/Computer Science Interfaces Series. Springer, Heidelberg (2007)

    Google Scholar 

  3. Wagner, S.: Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. PhD thesis, Johannes Kepler University, Linz, Austria (2009)

    Google Scholar 

  4. Wagner, S., Kronberger, G., Beham, A., Winkler, S., Affenzeller, M.: Modeling of heuristic optimization algorithms. In: Bruzzone, A., Longo, F., Piera, M.A., Aguilar, R.M., Frydman, C. (eds.) Proceedings of the 20th European Modeling and Simulation Symposium, pp. 106–111. DIPTEM University of Genova (2008)

    Google Scholar 

  5. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wagner, S., Kronberger, G., Beham, A., Winkler, S., Affenzeller, M. (2009). Model Driven Rapid Prototyping of Heuristic Optimization Algorithms. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2009. EUROCAST 2009. Lecture Notes in Computer Science, vol 5717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04772-5_94

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04772-5_94

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04771-8

  • Online ISBN: 978-3-642-04772-5

  • eBook Packages: Computer ScienceComputer Science (R0)