Skip to main content

New Genetic Algorithm for the p-Median Problem

  • Conference paper

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 298))

Abstract

The p-median problem is a well-known combinatorial optimization problem with several possible formulations and many practical applications in areas such as operational research and planning. It has been also used as a testbed for heuristic and metaheuristic optimization algorithms. This work proposes a new genetic algorithm for the p-median problem and evaluates it in a series of computational experiments.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC (2009)

    Google Scholar 

  2. Alp, O., Erkut, E., Drezner, Z.: An efficient genetic algorithm for the p-median problem. Annals of Operations Research 122(1-4), 21–42 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  3. Arroyo, J.E.C., dos Santos, P.M., Soares, M.S., Santos, A.G.: A multi-objective genetic algorithm with path relinking for the p-median problem. In: Kuri-Morales, A., Simari, G.R. (eds.) IBERAMIA 2010. LNCS, vol. 6433, pp. 70–79. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  4. Correa, E.S., Steiner, M.T.A., Freitas, A.A., Carnieri, C.: A genetic algorithm for the p-median problem. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W., Voigt, H.M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), July 7-11, pp. 1268–1275. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  5. Czarn, A., MacNish, C., Vijayan, K., Turlach, B.: Statistical exploratory analysis of genetic algorithms: The influence of gray codes upon the difficulty of a problem. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 1246–1252. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Landa-Torres, I., Del Ser, J., Salcedo-Sanz, S., Gil-Lopez, S., Portilla-Figueras, J., Alonso-Garrido, O.: A comparative study of two hybrid grouping evolutionary techniques for the capacitated p-median problem. Computers and Operations Research 39(9), 2214–2222 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  7. Lim, A., Xu, Z.: A fixed-length subset genetic algorithm for the p-median problem. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 1596–1597. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

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

    Google Scholar 

  9. Mladenović, N., Brimberg, J., Hansen, P., Moreno-Pérez, J.A.: The p-median problem: A survey of metaheuristic approaches. European Journal of Operational Research 179(3), 927–939 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  10. Pullan, W.: A population based hybrid metaheuristic for the p-median problem, pp. 75–82 (2008)

    Google Scholar 

  11. Sabeti, M., Boostani, R., Zoughi, T.: Using genetic programming to select the informative eeg-based features to distinguish schizophrenic patients. Neural Network World 22(1), 3–20 (2012)

    Google Scholar 

  12. Wu, A.S., Lindsay, R.K., Riolo, R.: Empirical observations on the roles of crossover and mutation. In: Bäck, T. (ed.) Proc. of the Seventh Int. Conf. on Genetic Algorithms, pp. 362–369. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  13. Yao, J.B., Yao, B.Z., Li, L., Jiang, Y.L.: Hybrid model for displacement prediction of tunnel surrounding rock. Neural Network World 22(3), 263–275 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pavel Krömer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Krömer, P., Platoš, J. (2014). New Genetic Algorithm for the p-Median Problem. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume II. Advances in Intelligent Systems and Computing, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-319-07773-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07773-4_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07772-7

  • Online ISBN: 978-3-319-07773-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics