Evolving Parallel Machine Programs for a Multi-ALU Processor

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

@InProceedings{leung:2002:epmpfamp,
  author =       "Kwong Sak Leung and Kin Hong Lee and Sin Man Cheang",
  title =        "Evolving Parallel Machine Programs for a {Multi-ALU}
                 Processor",
  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

Citations