Parallel Genetic Programming on a Network of Transputers   [PGP] [GP]

by

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

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Info: 1995
Keywords:genetic algorithms, genetic programming
Abstract:
This report describes the parallel implementation [PI] of genetic programming [GP] in the C programming language using a PC 486 type computer (running Windows) acting as a host and a network of transputers acting as processing nodes. Using 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 a cost that is intermediate between the two. A comparison is made of the computational effort [CE] required to solve the problem of symbolic regression [SR] of the Boolean even-5-parity function with different migration rates. Genetic programming [GP] required the least computational effort [CE] with an 8% migration rate. Moreover, this computational effort [CE] 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 [PI] of genetic programming, parallelization [GP] delivered more than linear speed-up in solving the problem using genetic programming. [GP]
Notes:
Our printers barfed about halfway through. See also andre:1995:parallel
URL(s):(G)zipped postscript
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BibTex:
@TechReport{Koza:1995:pGPnt,
  author =       "John R. Koza and David Andre",
  title =        "Parallel Genetic Programming on a Network of
                 Transputers",
  institution =  "Stanford University, Department of Computer Science",
  year =         "1995",
  type =         "Technical Report",
  number =       "CS-TR-95-1542",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "ftp::/elib.stanford.edu/pub/reports/cs/tr/95/1542/",
  abstract =     "This report describes the parallel implementation of
                 genetic programming in the C programming language using
                 a PC 486 type computer (running Windows) acting as a
                 host and a network of transputers acting as processing
                 nodes. 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.

                 A comparison is made 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 8% 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, parallelization
                 delivered more than linear speed-up in solving the
                 problem using genetic programming.",
  notes =        "Our printers barfed about halfway through. See also
                 andre:1995:parallel",
  size =         "21 pages",
}