Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{andre:1996:aigp2,
  author =       "David Andre and John R. Koza",
  title =        "Parallel Genetic Programming: A Scalable
                 Implementation Using The Transputer Network
                 Architecture",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "317--337",
  chapter =      "16",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277532",
  size =         "21 pages",
  abstract =     "This chapter describes the parallel implementation of
                 genetic programming in the C programming language using
                 a PC type computer (running Windows) acting as a host
                 and a network of processing nodes using the transputer
                 architecture. Using 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 a cost that is intermediate between
                 the two. This approach is illustrated by a comparison
                 of the computational effort required to solve the
                 problem of symbolic regression of the Boolean
                 even-5-parity function with different migration rates.
                 Genetic programming required the least computational
                 effort with an 5% migration rate. Moreover, this
                 computational effort was less than that required for
                 solving the problem with a serial computer and a
                 panmictic population of the same size. That is, apart
                 from the nearly linear speed-up in executing a fixed
                 amount of code inherent in the parallel implementation
                 of genetic programming, the use of distributed
                 sub-populations with only limited migration delivered
                 more than linear speed-up in solving the problem.",
}

Genetic Programming entries for David Andre John Koza

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