Advertisement

Automatic Algorithm Selection for the Quadratic Assignment Problem Using Fitness Landscape Analysis

  • Erik Pitzer
  • Andreas Beham
  • Michael Affenzeller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7832)

Abstract

In the last few years, fitness landscape analysis has seen an increase in interest due to the availability of large problem collections and research groups focusing on the development of a wide array of different optimization algorithms for diverse tasks. Instead of being able to rely on a single trusted method that is tuned and tweaked to the application more and more, new problems are investigated, where little or no experience has been collected. In an attempt to provide a more general criterion for algorithm and parameter selection other than “it works better than something else we tried”, sophisticated problem analysis and classification schemes are employed. In this work, we combine several of these analysis methods and evaluate the suitability of fitness landscape analysis for the task of algorithm selection.

Keywords

Fitness Landscape Analysis Problem Understanding Quadratic Assignment Problem Robust Taboo Search Variable Neighborhood Search 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Weinberger, E.D.: Local properties of kauffman’s n-k model, a tuneably rugged energy landscape. Physical Review A 44(10), 6399–6413 (1991)CrossRefGoogle Scholar
  2. 2.
    Jones, T.: Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico, Albuquerque, New Mexico (1995)Google Scholar
  3. 3.
    Pitzer, E., Affenzeller, M.: A Comprehensive Survey on Fitness Landscape Analysis. In: Fodor, J., Klempous, R., Suárez Araujo, C.P. (eds.) Recent Advances in Intelligent Engineering Systems. SCI, vol. 378, pp. 161–191. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Koopmans, T.C., Beckmann, M.: Assignment problems and the location of economic activities. Econometrica 25(1), 53–76 (1957)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Taillard, E.D.: Robust taboo search for the quadratic assignment problem. Parallel Computing 17, 443–455 (1991)MathSciNetCrossRefGoogle Scholar
  6. 6.
    James, T., Rego, C., Glover, F.: Multistart tabu search and diversification strategies for the quadratic assignment problem. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 39(3), 579–596 (2009)CrossRefGoogle Scholar
  7. 7.
    Burkard, R.E., Karisch, S.E., Rendl, F.: QAPLIB - A quadratic assignment problem library. Journal of Global Optimization 10(4), 391–403 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Glover, F.: Tabu search – part I. ORSA Journal on Computing 1(3), 190–206 (1989)zbMATHCrossRefGoogle Scholar
  9. 9.
    Hansen, P., Mladenovic, N., Perez, J.: Variable neighbourhood search: methods and applications. Annals of Operations Research 175, 367–407 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Merz, P., Freisleben, B.: Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Transactions on Evolutionary Computation 4(4), 337–352 (2000)CrossRefGoogle Scholar
  11. 11.
    Weinberger, E.: Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological Cybernetics 63(5), 325–336 (1990)zbMATHCrossRefGoogle Scholar
  12. 12.
    Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Information characteristics and the structure of landscapes. Evol. Comput. 8(1), 31–60 (2000)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Chicano, J.F., Luque, G., Alba, E.: Autocorrelation measures for the quadratic assignment problem. Applied Mathematics Letters 25(4), 698–705 (2012)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood components analysis. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 17, pp. 513–520. MIT Press, Cambridge (2005)Google Scholar
  16. 16.
    Pitzer, E., Vonolfen, S., Beham, A., Affenzeller, M., Bolshakov, V., Merkuryeva, G.: Structural analysis of vehicle routing problems using general fitness landscape analysis and problem specific measures. In: 14th International Asia Pacific Conference on Computer Aided System Theory, pp. 36–38 (2012)Google Scholar
  17. 17.
    Pitzer, E., Beham, A., Affenzeller, M.: Generic hardness estimation using fitness and parameter landscapes applied to robust taboo search and the quadratic assignment problem. In: GECCO 2012 Companion, pp. 393–400 (2012)Google Scholar
  18. 18.
    Pitzer, E., Beham, A., Affenzeller, M.: Correlation of Problem Hardness and Fitness Landscapes in the Quadratic Assignment Problem. In: Advanced Method and Applications in Computational Intelligence, pp. 163–192. Springer (in press, 2013)Google Scholar
  19. 19.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11(1), 10–18 (2009)CrossRefGoogle Scholar
  20. 20.
    Platt, J.C.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Advances in Kernel Methods: Support Vector Learning, pp. 185–208. MIT Press (1998)Google Scholar
  21. 21.
    Macready, W.G., Wolpert, D.H.: What makes an optimization problem hard? Complexity 5, 40–46 (1996)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Erik Pitzer
    • 1
  • Andreas Beham
    • 1
  • Michael Affenzeller
    • 1
  1. 1.School of Informatics, Communications and MediaUniversity of Applied Sciences Upper AustriaHagenbergAustria

Personalised recommendations