Genetic evolution of hierarchical behavior structures

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{1277296,
  author =       "Brian G. Woolley and Gilbert L. Peterson",
  title =        "Genetic evolution of hierarchical behavior
                 structures",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1731--1738",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1731.pdf",
  DOI =          "doi:10.1145/1276958.1277296",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, behaviour
                 based robotics, evolutionary robotics, unified
                 behaviour framework",
  abstract =     "The development of coherent and dynamic behaviours for
                 mobile robots is an exceedingly complex endeavour ruled
                 by task objectives, environmental dynamics and the
                 interactions within the behavior structure. This paper
                 discusses the use of genetic programming techniques and
                 the unified behaviour framework to develop effective
                 control hierarchies using interchangeable behaviors and
                 arbitration components. Given the number of possible
                 variations provided by the framework, evolutionary
                 programming is used to evolve the overall behaviour
                 design. Competitive evolution of the behaviour
                 population incrementally develops feasible solutions
                 for the domain through competitive ranking. By
                 developing and implementing many simple behaviours
                 independently and then evolving a complex behaviour
                 structure suited to the domain, this approach allows
                 for the reuse of elemental behaviours and eases the
                 complexity of development for a given domain.
                 Additionally, this approach has the ability to locate a
                 behaviour structure which a developer may not have
                 previously considered, and whose ability exceeds
                 expectations. The evolution of the behaviour structure
                 is demonstrated using agents in the Robocode
                 environment, with the evolved structures performing up
                 to 122 percent better than one crafted by an expert.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for Brian G Woolley Gilbert L Peterson

Citations