Comparing Synchronous and Asynchronous Parallel and Distributed Genetic Programming Models

  • Francisco Fernández
  • G. Galeano
  • J.A. Gómez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2278)


We present a study that analyses the respective advantages and disadvantages of the synchronous and asynchronous versions of island-based genetic programming and also a relationship between the number of subpopulations in parallel GP and the asynchronous model. We also look at a new measuring system for comparing parallel genetic programming with panmictic model. At the same time we show an interesting relationship between the bloat phenomenon and the number of individuals we use.


Genetic Programming Multiprocessor System Island Model Migration Generation Master Process 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    D. Andre and J R. Koza. “Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture”. P. Angeline and K. Kinear editors. Advances in Genetic Programming 2, Cambridge, MA, 1996.Google Scholar
  2. [2]
    E. Cantú-Paz and D. Goldberg: “Predicting Speedups of Ideal Bounding Cases of Parallel Genetic Algorithms”. Proceedings of the Seventh International Conference on Genetic Algorithms. Morgan Kaufmann. 1997.Google Scholar
  3. [3]
    A. Tetamanzi, M. Tomassini,“Soft Computing”. Springer Verlag, Heideberg, Germany 2001Google Scholar
  4. [4]
    W.F. Punch: “How effective are multiple populations in Genetic Programming”. Genetic Programming 1998: Proceedings of the Third Annual Conference, J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, D. Goldberg, H. Iba and R. L. Riolo (Eds),Morgan Kaufmann, San Francisco, CA, pp. 308–313, 1998.Google Scholar
  5. [5]
    M. Tomassini, F. Fernández, L. Vanneschi, L. Bucher, “An MPI-Based Tool for Distributed Genetic Programming” In Proceedings of IEEE International Conference on Cluster Computing CLUSTER2000, IEEE Computer Society. pp.209–216. 2000.Google Scholar
  6. [6]
    F. Fernández, M. Tomassini, L Vanneschi, L. Bucher, “The GP’s Tool”.
  7. [7]
    J. R. Koza, F. H. Bennett III, D. Andre, M.A. Keane: “Genetic Programming III. Darwinian Invention and Problem Solving”. Morgan Kaufmann Publishers. San Francisco. 1999.zbMATHGoogle Scholar
  8. [8]
    Ricardo Poli: “Evolution of graph-like programs with parallel distributed genetic programming”. In proceedings of the 7th International Conference on Genetic Algorithms, T. Bäck (ed.), Morgan Kaufmann, San Francisco, CA, 1997, pp. 346–353.Google Scholar
  9. [9]
    F. Fernández, “Parallel and Distributed Genetic Programming models, with application to logic syntesis on FPGAs”, PhD Thesis. Universidad de Extremadura, February 2001.Google Scholar
  10. [10]
    F. Fernández, M. Tomassini, L. Vanneschi: “Studying the influence of Communication Topology and Migration on Distributed Genetic Programming”, In J. Miler, M. Tomassini, P.L. Lanzi, C. Ryan, A. G.B. Tettamanzi, W. Landdon, LNCS 2038 Genetic Programming, 4th European Conference, EuroGP 2001. Pp 51.63Google Scholar
  11. [11]
    Enrique Alba, José M. Troya: “Analyzing synchronous and asynchronous parallel distributed genetic algorithms”. Future Generation Computer Systems 17 (2001) 451–465zbMATHCrossRefGoogle Scholar
  12. [12]
    W. Langdon and R. Poli. “Fitness causes bloat”. In P.K. Chawdhry et. al., editors. Soft Computing in Engineering Design and Manufacturing, pp 13–22. Springer London, 1997.Google Scholar
  13. [13]
    J. R. Koza: “Genetic Programming. On the programming of computers by means of natural selection”. Cambridge MA: The MIT Press. 1992.zbMATHGoogle Scholar
  14. [14]
    MPI Forum (1995) MPI: A Message-Passing Interface Standard.

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Francisco Fernández
    • 1
  • G. Galeano
    • 1
  • J.A. Gómez
    • 1
  1. 1.Computer Science DepartmentUniversity of ExtremaduraC/ CalvarioSpain

Personalised recommendations