A parallel implementation of genetic programming that achieves super-linear performance   [PI] [GP]

by

Andre, D. and Koza, J., R.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (Conference proceedings), 1996, p. 1163-1174
Keywords:genetic algorithms, genetic programming
Abstract:
This paper describes the successful parallel implementation [PI] of genetic programming [GP] on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms [GA] and genetic programming [GP] can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort [CE] required to solve a benchmark problem. Because of the decoupled character of genetic programming, [GP] our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the use of subpopulations delivered a super linear speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the parallel genetic programming system [PGP] [GP] evolved solutions that are competitive with human performance on the same problem.
Notes:
Awarded Best Paper Award PDPTA'96
URL(s):Postscript
(G)zipped postscript

Review item:

Mark as doublet (will be reviewed)

Print entry




BibTex:
@InProceedings{andre:1996:parGP,
  author =       "David Andre and John R. Koza",
  title =        "A parallel implementation of genetic programming that
                 achieves super-linear performance",
  booktitle =    "Proceedings of the International Conference on
                 Parallel and Distributed Processing Techniques and
                 Applications",
  year =         "1996",
  editor =       "Hamid R. Arabnia",
  volume =       "III",
  pages =        "1163--1174",
  address =      "Sunnyvale",
  month =        "9-11 " # aug,
  publisher =    "CSREA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-cs-faculty.stanford.edu/pdptap96.ps",
  abstract =     "This paper describes the successful parallel
                 implementation of genetic programming on a network of
                 processing nodes using the transputer architecture.
                 With this approach, researchers of genetic algorithms
                 and genetic programming can acquire computing power
                 that is intermediate between the power of currently
                 available workstations and that of supercomputers at
                 intermediate cost. This approach is illustrated by a
                 comparison of the computational effort required to
                 solve a benchmark problem. Because of the decoupled
                 character of genetic programming, our approach achieved
                 a nearly linear speed up from parallelization. In
                 addition, for the best choice of parameters tested, the
                 use of subpopulations delivered a super linear speed-up
                 in terms of the ability of the algorithm to solve the
                 problem. Several examples are also presented where the
                 parallel genetic programming system evolved solutions
                 that are competitive with human performance on the same
                 problem.",
  notes =        "Awarded Best Paper Award PDPTA'96",
}