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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
E. Alba and J. M. Troya. A survey of parallel distributed genetic algorithms. Complexity, 4(4):31–52, 1999.
E. Alba and J. M. Troya. Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Generation Computer Systems, 17:451–465, January 2001.
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.
E. Cantú-Paz. Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Press, 2000.
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.
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.
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.
Message Passing Interface Forum. MPI: A message-passing interface standard. International Journal of Supercomputer Applications, 8(3–4):165–414, 1994.
J. R. Koza. Genetic Programming. The MIT Press, Cambridge, Massachusetts, 1992.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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