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CAGE: A Tool for Parallel Genetic Programming Applications

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Book cover Genetic Programming (EuroGP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2038))

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Abstract

A new parallel implementation of genetic programming based on the cellular model is presented and compared with the island model approach. Although the widespread belief that cellular model is not suitable for parallel genetic programming implementations, experimental results show a better convergence with respect to the island approach, a good scale-up behaviour and a nearly linear speed-up.

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References

  1. Andre D., Koza, J.R. Exploiting the fruits of parallelism: An implementation of parallel genetic programming that achieves super-linear performance. Information Science Journal, Elsevier, 1997.

    Google Scholar 

  2. Cantú-Paz, E. A summary of research on parallel genetic algorithms, Technical Report 950076, Illinois Genetic Algorithm Laboratory, University of Illinois at Urbana Champaign, Urbana, July 1995.

    Google Scholar 

  3. Dracopoulos, D.C., Kent, S. Speeding up Genetic Programming: A Parallel BSP implementation. Genetic Programming 1996, Proceedings of the First Annual Conference, pp 125–136, MIT Press, Stanford University, July 1996.

    Google Scholar 

  4. Fernández, F., Tomassini, M., Punch, W.F., Sánchez, J.M. Experimental Study of Multipopulation Parallel Genetic Programming. European Conference on Genetic Progamming, LNCS 1082, Springer, Edinburgh 1999.

    Google Scholar 

  5. Fernández, F., Tomassini, M., Vanneschi, L., Bucher, L. A Distributed Computing Environment for Genetic Programming Using MPI. Recent Advances in Parallel Virtual Machine and Message Passing Interface, 7th European PVM/MPI Users’ Group Meeting, Balatonfured, Hungary, September 2000.

    Google Scholar 

  6. Juillé, H., Pollack, J.B. Parallel Genetic Programming on Fine-Grained SIMD Architectures. Working Notes of the AAAI-95 Fall Symposium on Genetic Programming”, AAAI Press, 1995.

    Google Scholar 

  7. Juillé, H., Pollack, J.B. Massively Parallel Genetic Programming. In P. Angeline and K. Kinnear, editors, Advances in Genetic Programming: Volume 2, MIT Press, Cambridge, 1996.

    Google Scholar 

  8. Koza, J.R. Genetic Programming: On the Programming of Computers by means of Natural Selection. MIT Press, Cambridge, 1992.

    MATH  Google Scholar 

  9. J.R. Koza and D. Andre (1995) Parallel genetic programming on a network of transputers. Technical Report CS-TR-95-1542, Computer Science Department, Stanford University.

    Google Scholar 

  10. W.N. Martin, J. Lienig and J.P. Cohoon (1997), Island (migration) models: evolutionary algorithms based on punctuated equilibria, in T. Bäck, D.B. Fogel, Z. Michalewicz (eds.), Handbook of evolutionary Computation. IOP Publishing and Oxford University Press.

    Google Scholar 

  11. Niwa, T., Iba, H. Distributed Genetic Programming-Empirical Study and Analisys -Genetic Programming 1996, Proceedings of the First Annual Conference, MIT Press, Stanford University, July 1996.

    Google Scholar 

  12. Oussaidéne, M., Chopard, B. Pictet, O., Tommasini, M. Parallel Genetic Programming and its Application to Trading Model Induction. Parallel Computing, vol. 23,n. 2, September 1997.

    Google Scholar 

  13. C.C. Pettey (1997), Diffusion (cellular) models, in T. Bäck, D.B. Fogel, Z. Michalewicz (eds.), Handbook of evolutionary Computation. IOP Publishing and Oxford University Press.

    Google Scholar 

  14. Punch, W.F. How effective are Multiple Populations in Genetic Programming. Genetic Programming 1998, Proceedings of the Third Annual Conference, MIT Press, University of Winsconsin, July 1998.

    Google Scholar 

  15. Salhi, A., Glaser, H., De Roure, D. Parallel Implementation of a Genetic-Programming based Tool for Symbolic Regression. Technical Report DSSE-TR-97-3, Dept. Comp. Science, University of Souhampton, 1997.

    Google Scholar 

  16. Tackett, W.A., Carmi, A. Simple Genetic Programming in C, Available through the genetic programmming archive at ftp://ftp.io.com/pub/genetic-programming/code/sgpc1.tar.Z.

  17. T. Toffoli and N. Margolus (1986).Cellular Automata Machines A New Environment for Modeling. The MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  18. Tomassini M. Parallel and Distributed Evolutionary Algorithms: A Review, J. Wiley and Sons,Chichester, K. Miettinen, M. Mkel, P. Neittaanmki and J. Periaux (editors), pp. 113–133, 1999.

    Google Scholar 

  19. D. Whitley (1993). Cellular Genetic Algorithms. Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Univ. della Calabria, Via P. Bucci Cubo 41C, 87036, Rende, CS, Italy

    Gianluigi Folino, Clara Pizzuti & Giandomenico Spezzano

Authors
  1. Gianluigi Folino
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  2. Clara Pizzuti
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  3. Giandomenico Spezzano
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham

    Julian Miller

  2. Computer Science Institute, University of Lausanne, Lausanne

    Marco Tomassini

  3. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy

    Pier Luca Lanzi

  4. Computer Science and Information Systems, University of Limerick, Limerick

    Conor Ryan

  5. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Italy

    Andrea G. B. Tettamanzi

  6. Department of Computer Science, University College London, London

    William B. Langdon

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© 2001 Springer-Verlag Berlin Heidelberg

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Folino, G., Pizzuti, C., Spezzano, G. (2001). CAGE: A Tool for Parallel Genetic Programming Applications. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds) Genetic Programming. EuroGP 2001. Lecture Notes in Computer Science, vol 2038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45355-5_6

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  • DOI: https://doi.org/10.1007/3-540-45355-5_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41899-3

  • Online ISBN: 978-3-540-45355-0

  • eBook Packages: Springer Book Archive

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