Evolving team compositions by agent swapping
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- @Article{Lichocki:2012:ieeeTEC,
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author = "Pawel Lichocki and Steffen Wischmann and
Laurent Keller and Dario Floreano",
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title = "Evolving team compositions by agent swapping",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2013",
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volume = "17",
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number = "2",
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pages = "282--298",
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month = apr,
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keywords = "genetic algorithms, genetic programming, Multiagent
systems, cooperation, crossover, evolutionary
computation, team composition, team optimisation",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2012.2191292",
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size = "18 pages",
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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