An Empirical Study of the GPP Accelerating Phenomenon

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@InProceedings{cheang:2003:CIRAS,
  author =       "Sin Man Cheang",
  title =        "An Empirical Study of the {GPP} Accelerating
                 Phenomenon",
  booktitle =    "Proceedings of the second International Conference on
                 Computational Intelligence, Robotics and Autonomous
                 Systems -- CIRAS-2003",
  year =         "2003",
  editor =       "P. Vadakkepat and T. W. Wan and T. K. Chen and 
                 L. A. Poh",
  pages =        "PS04--4--03",
  address =      "Singapore",
  month =        "15-18 " # dec,
  organisation = "Centre for Intelligent Control, National Univ. of
                 Singapore",
  publisher =    "National Univ. of Singapore",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "The Genetic Parallel Programming (GPP) is a novel
                 Linear-structure Genetic Programming paradigm that
                 learns parallel programs. We discover the GPP
                 Accelerating Phenomenon, i.e. parallel programs are
                 evolved faster than their counterpart sequential
                 programs of identical functions. This paper presents an
                 empirical study of Boolean function regression based on
                 a Multi-ALU Processor that results in the phenomenon.
                 We performed a series of random search experiments
                 using different numbers of ALUs (w) and instructions
                 (l). We identify that w (the degree of parallelism of
                 the program) is the dominant factor that affects the
                 searching performance. In a 3-input Boolean function
                 experiment, searching a single-ALU program requires 875
                 times on average of the computational effort of an
                 8-ALU program. An investigation on the probabilities of
                 finding solutions to different problem instances shows
                 that parallel representation of programs can increase
                 the probabilities of finding solutions to hard
                 problems.",
  notes =        "http://ciras.nus.edu.sg/2003/Proceedings/ProgramDec17.pdf",
}

Genetic Programming entries for Ivan Sin Man Cheang

Citations