Strongly typed genetic programming in evolving cooperation strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Hayes:1995,
  author =       "Thomas Haynes and Roger Wainwright and Sandip Sen and 
                 Dale Schoenefeld",
  title =        "Strongly typed genetic programming in evolving
                 cooperation strategies",
  booktitle =    "Genetic Algorithms: Proceedings of the Sixth
                 International Conference (ICGA95)",
  year =         "1995",
  editor =       "Larry J. Eshelman",
  pages =        "271--278",
  address =      "Pittsburgh, PA, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "15-19 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-370-0",
  URL =          "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icga95.pdf",
  abstract =     "A key concern in genetic programming (GP) is the size
                 of the state-space which must be searched for large and
                 complex problem domains. One method to reduce the
                 state-space size is by using Strongly Typed Genetic
                 Programming (STGP). We applied both GP and STGP to
                 construct cooperation strategies to be used by multiple
                 predator agents to pursue and capture a prey agent on a
                 grid-world. This domain has been extensively studied in
                 Distributed Artificial Intelligence (DAI) as an
                 easy-to-describe but difficult-to-solve cooperation
                 problem. The evolved programs from our systems are
                 competitive with manually derived greedy algorithms. In
                 particular the STGP paradigm evolved strategies in
                 which the predators were able to achieve their goal
                 without explicitly sensing the location of other
                 predators or communicating with other predators. This
                 represents an improvement over previous research in
                 this area. The results of our experiments indicate that
                 STGP is able to evolve programs that perform
                 significantly better than GP evolved programs. In
                 addition, the programs generated by STGP were easier to
                 understand.",
  notes =        "Our printers barf at graph on page 8.

                 ",
}

Genetic Programming entries for Thomas D Haynes Roger L Wainwright Sandip Sen Dale A Schoenefeld