GP under streaming data constraints: a case for pareto archiving?
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Atwater:2012:GECCO,
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author = "Aaron Atwater and Malcolm I. Heywood and
Nur Zincir-Heywood",
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title = "GP under streaming data constraints: a case for pareto
archiving?",
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booktitle = "GECCO '12: Proceedings of the fourteenth international
conference on Genetic and evolutionary computation
conference",
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year = "2012",
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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",
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isbn13 = "978-1-4503-1177-9",
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pages = "703--710",
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keywords = "genetic algorithms, genetic programming",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Philadelphia, Pennsylvania, USA",
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DOI = "doi:10.1145/2330163.2330262",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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
Citations