Novel ways of improving cooperation and performance in ensemble classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Russell Thomason and Terence Soule",
  title =        "Novel ways of improving cooperation and performance in
                 ensemble classifiers",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1708--1715",
  address =      "London",
  URL =          "",
  DOI =          "doi:10.1145/1276958.1277293",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, performance
  abstract =     "There are two common methods of evolving teams of
                 genetic programs. Research suggests Island approaches
                 produce teams of strong individuals that cooperate
                 poorly and Team approaches produce teams of weak
                 individuals that cooperate strongly. Ideally, teams
                 should be composed of strong individuals that cooperate
                 well. In this paper we present a new class of
                 algorithms called Orthogonal Evolution of Teams (OET)
                 that overcomes the weaknesses of current Island and
                 Team approaches by applying evolutionary pressure at
                 both the level of teams and individuals during
                 selection and replacement. We present four novel
                 algorithms in this new class and compare their
                 performance to Island and Team approaches as well as
                 multi-class Adaboost on a number of classification
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",

Genetic Programming entries for Russell Thomason Terence Soule