Experimental investigation of three distributed genetic programming models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2439, Issue , 2002, Pages 641-650

  Tomassini, Marco ^a   Vanneschi, Leonardo ^a   Fernández, Francisco ^b   Galeano, Germán ^b  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ENTROPY; GENETIC ALGORITHMS;

ASYNCHRONOUS MODELS; COMPUTATIONAL EFFORT; DISTRIBUTED GENETIC PROGRAMMING; DISTRIBUTED MODELS; EXPERIMENTAL INVESTIGATIONS; PHENOTYPIC DIVERSITY; SYNCHRONOUS AND ASYNCHRONOUS COMMUNICATIONS; THREE MODELS;

GENETIC PROGRAMMING;

EID: 84944325286     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45712-7_62     Document Type: Conference Paper

References (12)

1. E. Alba and J. M. Troya. A survey of parallel distributed genetic algorithms. Complexity, 4(4):31–52, 1999.
2. E. Alba and J. M. Troya. Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Generation Computer Systems, 17:451–465, January 2001.
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.
4. E. Cantú-Paz. Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Press, 2000.
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.
6. F. Fernández, M. Tomassini, and L. Vanneschi. Studying the influence 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.
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.
8. Message Passing Interface Forum. MPI: A message-passing interface standard. International Journal of Supercomputer Applications, 8(3-4):165–414, 1994.
9. J. R. Koza. Genetic Programming. The MIT Press, Cambridge, Massachusetts, 1992.
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.
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.
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.