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European Parallel Virtual Machine / Message Passing Interface Users’ Group Meeting

EuroPVM/MPI 2000: Recent Advances in Parallel Virtual Machine and Message Passing Interface pp 322–329Cite as

A Distributed Computing Environment for Genetic Programming Using MPI

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1908))

Abstract

This paper presents an environment for distributed genetic programming using MPI. Genetic programming is a stochastic evolutionary learning methodology that can greatly benefit from parallel/distributed implementations. We describe the distributed system, as well as a user-friendly graphical interface to the tool. The usefulness of the distributed setting is demonstrated by the results obtained to date on several difficult problems, one of which is described in the text.

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References

  1. 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 

  2. B. Chopard, O. Pictet, and M. Tomassini. Parallel and distributed evolutionary computation for financial applications. Parallel Algorithms and Applications, 2000. (to appear).

    Google Scholar 

  3. F. Fernández, J. M. Sánchez, M. Tomassini, and J. A. Gómez. A parallel genetic programming tool based on pvm. In J. Dongarra, E. Luque, and Tomás Margalef, editors, Recent Advances in Parallel Virtual Machine and Message Passing Interface, volume 1697 of Lecture Notes in Computer Science, pages 241–2480. Springer-Verlag, Heidelberg, 1999.

    Chapter  Google Scholar 

  4. 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, Heidelberg, 2000.

    Google Scholar 

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

    MATH  Google Scholar 

  6. M. Oussaidéne, B. Chopard, O. Pictet, and M. Tomassini. Parallel genetic programming and its application to trading model induction. Parallel Computing, 23:1183–1198, 1997.

    Article  MATH  Google Scholar 

  7. T. Starkweather, D. Whitley, and K. Mathias. Optimization using distributed genetic algorithms. In H.-P. Schwefel and R. Männer, editors, Parallel Problem Solving from Nature, volume 496 of Lecture Notes in Computer Science, pages 176–185, Heidelberg, 1991. Springer-Verlag.

    Chapter  Google Scholar 

  8. T. Weinbrenner. Genetic Programming Kernel version 0.5.2 C++ Class Library. University of Darmstadt.

    Google Scholar 

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Author information

Authors and Affiliations

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

    Marco Tomassini, Leonardo Vanneschi & Laurent Bucher

  2. Dpto. Arquitectura y Tecnología de Computadores, Universidad de Extremadura, Cáceres, Spain

    Francisco Fernández

Authors
  1. Francisco Fernández
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  2. Marco Tomassini
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  3. Leonardo Vanneschi
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  4. Laurent Bucher
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Editor information

Editors and Affiliations

  1. Innovative Computing Lab., University of Tennessee, 104 Yres Hall, Knoxville, TN, 37996, USA

    Jack Dongarra

  2. MTA SZTAKI Computer and Automation Research Institute, P.O. Box 63, Budapest, 1518, Hungary

    Peter Kacsuk  & Norbert Podhorszki  & 

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© 2000 Springer-Verlag Berlin Heidelberg

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Fernández, F., Tomassini, M., Vanneschi, L., Bucher, L. (2000). A Distributed Computing Environment for Genetic Programming Using MPI. In: Dongarra, J., Kacsuk, P., Podhorszki, N. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2000. Lecture Notes in Computer Science, vol 1908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45255-9_44

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  • DOI: https://doi.org/10.1007/3-540-45255-9_44

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41010-2

  • Online ISBN: 978-3-540-45255-3

  • eBook Packages: Springer Book Archive

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