New Genetic Algorithm for the p-Median Problem

Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

genetic algorithm p-median problem experiments 

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References

  1. 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. 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)CrossRefMATHMathSciNetGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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)CrossRefGoogle Scholar
  6. 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)CrossRefMATHMathSciNetGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 8.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)Google Scholar
  9. 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)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Pullan, W.: A population based hybrid metaheuristic for the p-median problem, pp. 75–82 (2008)Google Scholar
  11. 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. 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. 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

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer and Electrical EngineeringUniversity of AlbertaEdmontonCanada
  2. 2.IT4Innovations & Department of Computer ScienceVŠB Technical University of OstravaOstravaCzech Republic

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