The Effect of Plagues in Genetic Programming: A Study of Variable-Size Populations

  • Francisco Fernandez
  • Leonardo Vanneschi
  • Marco Tomassini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2610)


A study on the effect of variable size populations in genetic programming is presented in this work.We apply the idea of plague (high desease of individuals).We show that although plagues are generally considered as negative events, they can help populations to save computing time and at the same time surviving individuals can reach high peaks in the fitness landscape.


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  1. 1.
    D. E. Goldberg. Sizing Populations for serial and parallel genetic algorithms. In Schaffer, J. D. (Ed.), Proceedings of the Third International Conference on Genetic Algorithms, pages 70–79. San Mateo, CA: Morgan Kaufmann, 1989.Google Scholar
  2. 2.
    E. Burke, S. Gustafson, G. Kendall, and N. Krasnogor. Advanced population diversity measures in genetic programming. In J. J. Merelo, P. Adamidis, H. G. Beyer, J.-L. Fernández-Villacanas, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature-PPSN VII, volume 2439 of Lecture Notes in Computer Science, pages 341–350. Springer-Verlag, Heidelberg, 2002. au]3._C. Gathercole and P. Ross. Small populations over many generations can beat large populations over few generations in genetic programming. In J. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, and R. L. Riolo, editors, Genetic Programming, Proceedings of the Second Annual Conference, pages 111–118, Morgan Kaufmann, San Francisco, CA, USA, 1997.Google Scholar
  3. 4.
    M. Fuchs. Large populations are not always the best choice in genetic programming. In W. Banzhaf, J. Daida, A. E. Eiben, M. Garzon, V. Honavar, M. Jakiela, and R. Smith, editors, Proceedings of the genetic and evolutionary computation conference GECCO’99, pages 1033–1038, Morgan Kaufmann, San Francisco, CA, 1999.Google Scholar
  4. 5.
    Tan, K.C., Lee, T.H., and E. F. Khor. Evolutionary Algorithms With Dynamic Population Size and Local Exploration for Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, Vol. 5, Num. 6, pages 565–588, 2001.CrossRefGoogle Scholar
  5. 6.
    W. B. Langdon and R. Poli. Foundations of Genetic Programming. Springer-Verlag, Heidelberg, 2001.Google Scholar
  6. 7.
    F. Fernandez, M. Tomassini and L. Vanneschi. An Empirical Study of Multipopulation Genetic Programming, in Genetic Programming and Evolvable Machines, Kluwer Academic Publishers. To appear, 2002.Google Scholar
  7. 8.
    J. R. Koza. Genetic Programming. On the Programming of Computers by Means of Natural Selection, The MIT Press, Cambridge, MA, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Francisco Fernandez
    • 1
  • Leonardo Vanneschi
    • 2
  • Marco Tomassini
    • 2
  1. 1.Computer Science DepartmentCentro Universitario de MeridaUniversity of ExtremaduraMeridaSpain
  2. 2.Computer Science InstituteUniversity of LausanneLausanneSwitzerland

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