Evolutionary Computation Techniques for Telephone Networks Traffic Supervision Based on a Qualitative Stream Propagation Model

  • I. Servet
  • L. Travé-Massuyès
  • D. Stern
Conference paper


Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex functions. In this paper, we consider three of them: multiple restart hill-climbing, population-based incremental learning and genetic algorithms. Their binary version and a real-coded variant of each of these techniques are experimented on a real problem: traffic supervision in telephone networks. Indeed, this task need to determine streams responsible for call losses in a network by comparing their traffic values to nominal values. However, stream traffic values are not directly available from the on-line data acquisition system and, hence, have to be computed by inverting a computational model of stream propagation in circuit-switched networks only based on the Erlang’s formula plus qualitative knowledge about the network. Then, our stream propagation model inversion has been computed thanks to the previous techniques and using several fitness measures to show how their choice can impact on the final results.


Genetic Algorithm Solution Vector Stream Traffic Telephone Network Stream Propagation 
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.


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  1. [1]
    S. Baluja. Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, 1994.Google Scholar
  2. [2]
    S. Baluja. An empirical comparison of seven iterative and evolutionary function optimization heuristics. Technical Report CMU-CS-95-193, 1995.Google Scholar
  3. [3]
    D. Beasley, D.R. Bull, and D.R. Martin. An overview of genetic algorithms: Parti, fundamentals. University computing, 15(2):58–69, 1993.Google Scholar
  4. [4]
    D. Beasley, D.R. Bull, and D.R. Martin. An overview of genetic algorithms: Part 2, research topics. University computing, 15(4):170–181, 1993.Google Scholar
  5. [5]
    L.J. Eshelman and J.D. Schaffer. Real-coded genetic algorithms and interval-schemata, volume 2. 1992.Google Scholar
  6. [6]
    J. Heitkokker and D. Beasley. The hitch-hiker’s guide to evolutionary computation: A list of frequently asked questions, available by anonymous ftp at rtfm.mit.edu., 1994.Google Scholar
  7. [7]
    F. Le Gall, J. Bernussou, and J.M. Garcia. A one-moment model for telephone networks with dependance on link blocking probabilities, pages 449–458. 1984.Google Scholar
  8. [8]
    A. Passeron. Notions élémentaires sur le trafic téléphonique. Technical Report DE/ATR/57.84, CNET, Issy-les-Moulineaux, 1984.Google Scholar
  9. [9]
    I. Servet, L. Travé-Massuyès, and D. Stern. Traffic supervision based on a one-moment model of telephone networks built from qualitative knowledge. In Proc. IMACS/IEEE CESA’96. Villeneuve d’Asq (France), 1996.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • I. Servet
    • 1
  • L. Travé-Massuyès
    • 1
  • D. Stern
    • 2
  1. 1.LAAS/CNRSToulouse CedexFrance
  2. 2.CNETIssy-les-MoulineauxFrance

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