Evolving team compositions by agent swapping

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Lichocki:2012:ieeeTEC,
  author =       "Pawel Lichocki and Steffen Wischmann and 
                 Laurent Keller and Dario Floreano",
  title =        "Evolving team compositions by agent swapping",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2013",
  volume =       "17",
  number =       "2",
  pages =        "282--298",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Multiagent
                 systems, cooperation, crossover, evolutionary
                 computation, team composition, team optimisation",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2012.2191292",
  size =         "18 pages",
  abstract =     "Optimising collective behaviour in multiagent systems
                 requires algorithms to find not only appropriate
                 individual behaviors but also a suitable composition of
                 agents within a team. Over the last two decades,
                 evolutionary methods have been shown to be a promising
                 approach for the design of agents and their
                 compositions into teams. The choice of a crossover
                 operator that facilitates the evolution of optimal team
                 composition is recognised to be crucial, but so far it
                 has never been thoroughly quantified. Here we highlight
                 the limitations of two different crossover operators
                 that exchange entire agents between teams: restricted
                 agent swapping that exchanges only corresponding agents
                 between teams and free agent swapping that allows an
                 arbitrary exchange of agents. Our results show that
                 restricted agent swapping suffers from premature
                 convergence, whereas free agent swapping entails
                 insufficient convergence. Consequently, in both cases
                 the exploration and exploitation aspects of the
                 evolutionary algorithm are not well balanced resulting
                 in the evolution of suboptimal team compositions. To
                 overcome this problem we propose to combine the two
                 methods. Our approach first applies free agent swapping
                 to explore the search space and then restricted agent
                 swapping to exploit it. This mixed approach turns out
                 to be a much more efficient strategy for the evolution
                 of team compositions compared to either strategy alone.
                 Our results suggest that such a mixed agent swapping
                 algorithm should always be preferred whenever the
                 optimal composition of individuals in a multiagent
                 system is unknown.",
  notes =        "also known as \cite{6171841}",
}

Genetic Programming entries for Pawel Lichocki Steffen Wischmann Laurent Keller Dario Floreano

Citations