GP under streaming data constraints: a case for pareto archiving?

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Aaron Atwater and Malcolm I. Heywood and 
                 Nur Zincir-Heywood",
  title =        "GP under streaming data constraints: a case for pareto
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "703--710",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330262",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Classification as applied to streaming data implies
                 that only a small number of new training instances
                 appear at each generation and are never explicitly
                 reintroduced by the stream. Pareto competitive
                 coevolution provides a potential framework for
                 archiving useful training instances between generations
                 under an archive of finite size. Such a coevolutionary
                 framework is defined for the online evolution of
                 classifiers under genetic programming. Benchmarking is
                 performed under multi-class data sets with class
                 imbalance and training partitions with between 1,000's
                 to 100,000's of instances. The impact of enforcing
                 different constraints for accessing the stream are
                 investigated. The role of online adaptation is
                 explicitly documented and tests made on the relative
                 impact of label error on the quality of streaming
                 classifier results.",
  notes =        "Also known as \cite{2330262} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",

Genetic Programming entries for Aaron Atwater Malcolm Heywood Nur Zincir-Heywood