The Lawnmower Problem Revisited: Stack-Based Genetic Programming and Automatically Defined Functions

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

@InProceedings{bruce:1997:lprsbGPADF,
  author =       "Wilker Shane Bruce",
  title =        "The Lawnmower Problem Revisited: Stack-Based Genetic
                 Programming and Automatically Defined Functions",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and 
                 David B. Fogel and Max Garzon and Hitoshi Iba and 
                 Rick L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "52--57",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  broken =       "http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-97.ps",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp97.pdf/bruce97lawnmower.pdf",
  URL =          "http://citeseer.ist.psu.edu/bruce97lawnmower.html",
  size =         "6 pages",
  abstract =     "Stack-based genetic programming is an alternative to
                 Koza-style tree-based genetic programming that
                 generates linear programs that are executed on a
                 virtual machine using a FORTH-style operand stack
                 instead of tree-based function calls. A stack-based
                 genetic programming system was extended to include the
                 ability to generate programs containing automatically
                 defined functions. Experiments were run to test the
                 system using Koza's lawnmower problem. The stack-based
                 system using automatically...",
  notes =        "GP-97 Zero fitness if attempts to pop empty stack.
                 LEFT primitive removed from population. ARG0 never in
                 best best of run. {"}SBGP required significantly more
                 search than tree-based GP{"} {"}comparisons ... may be
                 problem dependant{"}. {"}In both systems [GP and SBGP]
                 the use of ADFs appreciably improved the ability of the
                 GP system to quickly find a solution to the [lawn
                 mower] problem.{"} failure of SBGP without ADFs to
                 solve 8x12 {"}is most probably due to our limit of a
                 maximium of 256 elements in a solution{"}.",
}

Genetic Programming entries for Wilker Shane Bruce

Citations