Evolutionary optimization of cooperative heterogeneous teams

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  author =       "Terence Soule and Robert B. Heckendorn",
  title =        "Evolutionary optimization of cooperative heterogeneous
  booktitle =    "SPIE Defense and Security 2007",
  year =         "2007",
  editor =       "Misty Blowers and Alex F. Sisti",
  volume =       "6563",
  address =      "USA",
  month =        "12-13 " # apr,
  organisation = "SPIE",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Algorithms, Cooperation, Autonomous Vehicles, Planning,
                 Heterogeneous Teams",
  isbn13 =       "9780819466921",
  DOI =          "doi:10.1117/12.724018",
  size =         "10 pages",
  abstract =     "There is considerable interest in developing teams of
                 autonomous, unmanned vehicles that can function in
                 hostile environments without endangering human lives.
                 However, heterogeneous teams, teams of units with
                 specialised roles and/or specialized capabilities, have
                 received relatively little attention. Specialised roles
                 and capabilities can significantly increase team
                 effectiveness and efficiency. Unfortunately, developing
                 effective cooperation mechanisms is much more difficult
                 in heterogeneous teams. Units with specialised roles or
                 capabilities require specialised software that take
                 into account the role and capabilities of both itself
                 and its neighbours. Evolutionary algorithms, algorithms
                 modelled on the principles of natural selection, have a
                 proven track record in generating successful teams for
                 a wide variety of problem domains. Using classification
                 problems as a prototype, we have shown that typical
                 evolutionary algorithms either generate highly
                 effective teams members that cooperate poorly or poorly
                 performing individuals that cooperate well. To overcome
                 these weaknesses we have developed a novel class of
                 evolutionary algorithms. In this paper we apply these
                 algorithms to the problem of controlling simulated,
                 heterogeneous teams of 'scouts' and 'investigators'.
                 Our test problem requires producing a map of an area
                 and to further investigate 'areas of interest'. We
                 compare several evolutionary algorithms for their
                 ability to generate individually effective members and
                 high levels of cooperation.",
  notes =        "Evolutionary and Bio-inspired Computation: Theory and

Genetic Programming entries for Terence Soule Robert B Heckendorn