Evolving Parallel Machine Programs for a Multi-ALU Processor

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Kwong Sak Leung and Kin Hong Lee and Sin Man Cheang",
  title =        "Evolving Parallel Machine Programs for a {Multi-ALU}
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and 
                 Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and 
                 Mark Shackleton",
  pages =        "1703--1708",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  month =        "12-17 " # may,
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, Factorial
                 sequence, Fibonacci sequence, Sextic function,
                 experiments, genetic parallel programming, linear
                 genetic programming, multi-ALU processor, optimal
                 parallel program evolution, parallel machine programs,
                 program execution speed optimisation, two-phase
                 evolution, instruction sets, multiprocessing systems,
                 parallel programming",
  DOI =          "doi:10.1109/CEC.2002.1004499",
  abstract =     "This paper proposes a novel genetic parallel
                 programming (GPP) paradigm for evolving optimal
                 parallel programs running on a multi-ALU processor by
                 linear genetic programming. GPP uses a two-phase
                 evolution approach. It evolves completely correct
                 solution programs in the first phase. Then it optimises
                 execution speeds of solution programs in the second
                 phase. Besides, GPP also employs a new genetic
                 operation that swaps sub-instructions of a solution
                 program. Three experiments (Sextic, Fibonacci and
                 Factorial) are given as examples to show that GPP could
                 discover novel parallel programs that fully use the
                 processor's parallelism",

Genetic Programming entries for Kwong-Sak Leung Kin-Hong Lee Ivan Sin Man Cheang