Evolving Teamwork and Coordination with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Sean Luke and Lee Spector",
  title =        "Evolving Teamwork and Coordination with 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 =        "150--156",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  URL =          "http://www.cs.gmu.edu/~sean/papers/cooperation.pdf",
  URL =          "http://www.cs.gmu.edu/~sean/papers/cooperation.ps.gz",
  size =         "9 pages",
  abstract =     "Some problems can be solved only by multi-agent teams.
                 In using genetic programming to produce such teams, one
                 faces several design decisions. First, there are
                 questions of team diversity and of breeding strategy.
                 In one commonly used scheme, teams consist of clones of
                 single individuals; these individuals breed in the
                 normal way and are cloned to form teams during fitness
                 evaluation. In contrast, teams could also consist of
                 distinct individuals. In this case one can either allow
                 free interbreeding between members of different teams,
                 or one can restrict interbreeding in various ways. A
                 second design decision concerns the types of
                 coordination-facilitating mechanisms provided to
                 individual team members; these range from sensors of
                 various sorts to complex communication systems. This
                 paper examines three breeding strategies (clones, free,
                 and restricted) and three coordination mechanisms
                 (none, deictic sensing, and name-based sensing) for
                 evolving teams of agents in the Serengeti world, a
                 simple predator/prey environment. Among the conclusions
                 are the fact that a simple form of restricted
                 interbreeding outperforms free interbreeding in all
                 teams with distinct individuals, and the fact that
                 name-based sensing consistently outperforms deictic
  URL =          "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap18.pdf",
  URL =          "http://cognet.mit.edu/library/books/view?isbn=0262611279",
  notes =        "GP-96",

Genetic Programming entries for Sean Luke Lee Spector