Strongly typed genetic programming in evolving cooperation strategies   [ST] [GP] [CS]

by

Haynes, T., Wainwright, R., Sen, S. and Schoenefeld, D.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95) (Conference proceedings), 1995, p. 271-278
Keywords:genetic algorithms, genetic programming
Abstract:
A key concern in genetic programming (GP) [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). [ST] [GP] We applied both GP and STGP to construct cooperation strategies to [CS] 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 [AI] (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. [GA] 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.
URL(s):Postscript
(G)zipped postscript

Review item:

Mark as doublet (will be reviewed)

Print entry



BibTex:
@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 =       "L. 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://euler.mcs.utulsa.edu/~rogerw/haynes/icga95.ps",
  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.

                 ",
}