Skip to main content
SpringerLink
Log in

Experimental Investigation of Three Distributed Genetic Programming Models

  • Conference paper
  • First Online:
Parallel Problem Solving from Nature — PPSN VII (PPSN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2439))

Included in the following conference series:

Abstract

Three models of distributed Genetic Programming are presented comprising synchronous and asynchronous communication. These three models are compared with each other and with the standard panmictic model on three well known Genetic Programming benchmarks. The measures used are the computational effort, the phenotypic entropy of the populations, and the execution time. We find that all the distributed models are better than the sequential one in terms of effort and time. The differences among the distributed models themselves are rather small in terms of effort but one of the asynchronous models turns out to be significantly faster. The entropy con.rms that migration helps in conserving some phenotypic diversity in the populations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. E. Alba and J. M. Troya. A survey of parallel distributed genetic algorithms. Complexity, 4(4):31–52, 1999.

    Article  MathSciNet  Google Scholar 

  2. E. Alba and J. M. Troya. Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Generation Computer Systems, 17:451–465, January 2001.

    Google Scholar 

  3. D. Andre and J. R. Koza. Parallel genetic programming: A scalable implementation using the transputer network architecture. In P. Angeline and K. Kinnear, editors, Advances in Genetic Programming 2, pages 317–337, Cambridge, MA, 1996. The MIT Press.

    Google Scholar 

  4. E. Cantú-Paz. Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Press, 2000.

    Google Scholar 

  5. F. Fernández, M. Tomassini, W. F. Punch III, and J. M. Sánchez. Experimental study of multipopulation parallel genetic programming. In Riccardo Poli, Wolfgang Banzhaf, William B. Langdon, Julian F. Miller, Peter Nordin, and Terence C. Fogarty, editors, Genetic Programming, Proceedings of EuroGP’2000, volume 1802 of LNCS, pages 283–293. Springer-Verlag, 2000.

    Google Scholar 

  6. F. Fernández, M. Tomassini, and L. Vanneschi. Studying the in.uence of communication topology and migration on distributed genetic programming. In J. Miller, M. Tomassini, P. L. Lanzi, C. Ryan, A. Tettamanzi, and W. Langdon, editors, Genetic Programming, Proceedings of EuroGP'2001, volume 2038 of LNCS, pages 51–63. Springer-Verlag, 2001.

    Google Scholar 

  7. F. Fernández, M. Tomassini, L. Vanneschi, and L. Bucher. A distributed computing environment for genetic programming using MPI. In J. Dongarra, P. Kaksuk, and N. Podhorszki, editors, Recent Advances in Parallel Virtual Machine and Message Passing Interface, volume 1908 of Lecture Notes in Computer Science, pages 322–329. Springer-Verlag, Heidelberg, 2000.

    Chapter  Google Scholar 

  8. Message Passing Interface Forum. MPI: A message-passing interface standard. International Journal of Supercomputer Applications, 8(3–4):165–414, 1994.

    Google Scholar 

  9. J. R. Koza. Genetic Programming. The MIT Press, Cambridge, Massachusetts, 1992.

    MATH  Google Scholar 

  10. W. Punch. How effective are multiple populations in genetic programming. In J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, D. Goldberg, H. Iba, and R. L. Riolo, editors, Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 308–313, San Francisco, CA, 1998. Morgan Kaufmann.

    Google Scholar 

  11. Justinian P. Rosca. Entropy-driven adaptive representation. In Justinian P. Rosca, editor, Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pages 23–32, Tahoe City, California, USA, 1995.

    Google Scholar 

  12. M. Tomassini. Parallel and distributed evolutionary algorithms: a review. In K. Miettinen, M. äkelä, P. Neittanmäki, and J. Périaux, editors, Evolutionary Algorithms in Engineering and Computer Science, pages 113–133. J. Wiley, New York, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Institute, University of Lausanne, 1015, Lausanne, Switzerland

    Marco Tomassini & Leonardo Vanneschi

  2. Computer Science Department, University of Extremadura, C/ Calvario, s/n., 06800, Merida, Spain

    Francisco Fernández & Germán Galeano

Authors
  1. Marco Tomassini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonardo Vanneschi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Germán Galeano
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Informática, 18071, Granada, Spain

    Juan Julián Merelo Guervós

  2. Department of Informatics, Technological Educational Institute of Thessaloniki, 54101, Thessaloniki, Greece

    Panagiotis Adamidis

  3. Department of Informatics XI, University of Dortmund, 44221, Dortmund, Germany

    Hans-Georg Beyer  & Hans-Paul Schwefel  & 

  4. Department of Signal Theory and Communications, Universidad Carlos III, Madrid, Spain

    José-Luis Fernández-Villacañas

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tomassini, M., Vanneschi, L., Fernández, F., Galeano, G. (2002). Experimental Investigation of Three Distributed Genetic Programming Models. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_62

Download citation

  • DOI: https://doi.org/10.1007/3-540-45712-7_62

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44139-7

  • Online ISBN: 978-3-540-45712-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation