Orthogonal Evolution of Teams: A Class of Algorithms for Evolving Teams with Inversely Correlated Errors   [EOT]

by

Soule, T. and Komireddy, P.

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Info: Genetic Programming Theory and Practice IV, 2006, p. -
Keywords:genetic algorithms, genetic programming
Abstract:
Several general evolutionary approaches have proven quite successful at evolving teams (or ensembles) consisting of cooperating team members. However, in this paper we demonstrate that the existing approaches have subtle, but significant, weaknesses. We then present a novel class of evolutionary algorithms (orthogonal evolution [EA] [EOT] of teams (OET)) for evolving teams that overcomes these weaknesses. Specifically it is shown that a typical algorithm from the OET class of algorithms successfully generates team members that have fitnesses comparable to those evolved independently and that have inversely correlated errors, which maximises the teams' overall performance. Finally it is shown that the OET approach performs significantly better than the standard evolutionary approaches.
Notes:
part of \cite{Riolo:2006:GPTP}

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BibTex:
@InCollection{Soule:2006:GPTP,
  author =	 "Terence Soule and Pavankumarreddy Komireddy",
  title =	 "Orthogonal Evolution of Teams: {A} Class of
                  Algorithms for Evolving Teams with Inversely
                  Correlated Errors",
  booktitle =	 "Genetic Programming Theory and Practice {IV}",
  year =	 "2006",
  editor =	 "Rick L. Riolo and Terence Soule and Bill Worzel",
  volume =	 "5",
  series =	 "Genetic and Evolutionary Computation",
  chapter =	 "8",
  pages =	 "-",
  address =	 "Ann Arbor",
  month =	 "11-13 " # may,
  publisher =	 "Springer",
  keywords =	 "genetic algorithms, genetic programming",
  ISBN =	 "0-387-33375-4",
  abstract =	 "Several general evolutionary approaches have proven
                  quite successful at evolving teams (or ensembles)
                  consisting of cooperating team members. However, in
                  this paper we demonstrate that the existing
                  approaches have subtle, but significant,
                  weaknesses. We then present a novel class of
                  evolutionary algorithms (orthogonal evolution of
                  teams (OET)) for evolving teams that overcomes these
                  weaknesses. Specifically it is shown that a typical
                  algorithm from the OET class of algorithms
                  successfully generates team members that have
                  fitnesses comparable to those evolved independently
                  and that have inversely correlated errors, which
                  maximises the teams' overall performance. Finally it
                  is shown that the OET approach performs
                  significantly better than the standard evolutionary
                  approaches.",
  notes =	 "part of \cite{Riolo:2006:GPTP}",
}