High-Performance, Parallel, Stack-Based Genetic Programming

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

  author =       "Kilian Stoffel and Lee Spector",
  title =        "High-Performance, Parallel, Stack-Based Genetic
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and 
                 David B. Fogel and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "224--229",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://helios.hampshire.edu/lspector/pubs/HiGP-gp96-e.pdf",
  size =         "9 pages",
  abstract =     "HiGP is a new high-performance genetic programming
                 system. This system combines techniques from
                 string-based genetic algorithms, Sexpression-based
                 genetic programming systems, and high-performance
                 parallel computing. The result is a fast, flexible, and
                 easily portable genetic programming engine with a clear
                 and efficient parallel implementation. HiGP manipulates
                 and produces linear programs for a stack-based virtual
                 machine, rather than the tree-structured S-expressions
                 used in traditional genetic programming. In this paper
                 we describe the HiGP virtual machine and genetic
                 programming algorithms. We demonstrate the system's
                 performance on a symbolic regression problem and show
                 that HiGP can solve this problem with substantially
                 less computational effort than can a traditional
                 genetic programming system. We also show that HiGP's
                 time performance is significantly better than that of a
                 well-written S-expression-based system, also written in
                 C. We further show that our parallel version of HiGP
                 achieves a speedup that is nearly linear in the number
                 of processors, without mandating the use of localised
                 breeding strategies.",
  URL =          "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap27.pdf",
  URL =          "http://cognet.mit.edu/library/books/view?isbn=0262611279",
  notes =        "GP-96 lil-gp, IBM SP2",

Genetic Programming entries for Kilian Stoffel Lee Spector