Cooperation of the Fittest

by

Haynes, T. and Sen, S.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: 1996
Keywords:Genetic Programming, Genetic Algorithms
Abstract:
We introduce a cooperative co-evolutionary system to facilitate the development of teams of heterogeneous agents. We believe that $k$ different behavioral strategies for controlling the actions of a group of $k$ agents can combine to form a cooperation strategy which efficiently achieves global goals. We examine the on-line adaption of behavioral strategies utilizing genetic programming. [GP] Specifically, we deal with the credit assignment problem [CA] of how to fairly split the fitness of a team to all of its participants. We present several crossover mechanisms in a genetic programming system to [GP] facilitate the evolution of more than one member in the team during each crossover operation. Our goal is to reduce the time needed to either evolve a good team or reach convergence.
Notes:
evolution of cooperation (multi-agent,multi-tree) NOT coevolution of fitness function evolution. Our printer barfs on page 9.
URL(s):Postscript
(G)zipped postscript

Review item:

Mark as doublet (will be reviewed)

Print entry




BibTex:
@TechReport{Haynes:1996:CF,
  author =       "Thomas Haynes and Sandip Sen",
  title =        "Cooperation of the Fittest",
  number =       "UTULSA-MCS-96-09",
  institution =  "The University of Tulsa",
  year =         "1996",
  month =        apr # " 12,",
  size =         "9+ pages",
  keywords =     "Genetic Programming, Genetic Algorithms",
  abstract =     "We introduce a cooperative co-evolutionary system to
                 facilitate the development of teams of heterogeneous
                 agents. We believe that $k$ different behavioral
                 strategies for controlling the actions of a group of
                 $k$ agents can combine to form a cooperation strategy
                 which efficiently achieves global goals. We examine the
                 on-line adaption of behavioral strategies utilizing
                 genetic programming. Specifically, we deal with the
                 credit assignment problem of how to fairly split the
                 fitness of a team to all of its participants. We
                 present several crossover mechanisms in a genetic
                 programming system to facilitate the evolution of more
                 than one member in the team during each crossover
                 operation. Our goal is to reduce the time needed to
                 either evolve a good team or reach convergence.",
  URL =          "http://euler.mcs.utulsa.edu/~haynes/coopevol.ps",
  notes =        "evolution of cooperation (multi-agent,multi-tree) NOT
                 coevolution of fitness function evolution. Our printer
                 barfs on page 9.",
}