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An Effective Implementation of a Direct Spanning Tree Representation in GAs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

This paper presents an effective implementation based on predecessor vectors of a genetic algorithm using a direct tree representation. The main operations associated with crossovers and mutations can be achieved in O(d) time, where d is the length of a path. Our approach can avoid usual drawbacks of the fixed linear representations, and provide a framework facilitating the incorporation of problem-specific knowledge into initialization and operators for constrained minimum spanning tree problems.

Keywords

Genetic Algorithm Tree Representation Span Tree Problem Main Operation Adjacency List 
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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References

  1. 1.
    Crecenzi P., Kann V. A compendium of NP optimization problems. Available online at http://www.nada.kth.se/theory/compendium/, Aug. 1998
  2. 2.
    Goldberg D.E. Genetic algorithms in search, optimization and machine learning. Addison-Wesley (Reading, Mass).Google Scholar
  3. 3.
    Raidl G.R. An Efficient Evolutionary Algorithm for the Degree-Constrained Minimum Spanning Tree Problem. Proc. of the 2000 IEEE Congress on Evolutionary Computation, San Diego, CA, pp. 104–111, July 2000.Google Scholar
  4. 4.
    Palmer C.C., Kershenbaum A. An approach to a problem in network design using genetic algorithms. Networks, vol. 26 (1995) 151–163.CrossRefzbMATHGoogle Scholar
  5. 5.
    Raidl G.R., Julstrom B.A. A Weighted Coding in a Genetic Algorithm for the Degree-Constrained Minimum Spanning Tree Problem. Proc. of the 15th ACM Symposium on Applied Computing, Como, Italy, pp. 440–445, March 2000.Google Scholar
  6. 6.
    Knowles J., Corne D. A new evolutionary approach to the degree constrained minimum spanning tree problem. IEEE Transactions on Evolutionary Computation, Volume 4number 2, pp. 125–134, July 2000.CrossRefGoogle Scholar
  7. 7.
    Raidl G.R., Drexel C. A Predecessor Coding in an Evolutionary Algorithm for the Capacitated Minimum Spanning Tree Problem. Late-Breaking-Papers Proc. Of the 2000 Genetic and Evolutionary Computation Conference, Las Vegas, NV, pp. 309-316, July 2000. bibitemli Li Y., Bouchebaba Y. A new genetic algorithm for the optical communication spanning tree problem. Proc. of Artifical Evolution 99, LNCS 1829, pp. 162–173, Dunkerque, France, 1999.Google Scholar
  8. 8.
    Gibbons A. Algorithmic graph theory. Cambridge University Press, New York.Google Scholar
  9. 9.
    Michalewicz Z. Genetic Algorithms + Data Structures = Evolutionnary Programs. Springer-Verlag, 3rd edition, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Yu Li
    • 1
  1. 1.LaRIA, Univ. de Picardie Jules VerneAmiens CedexFrance

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