Benchmarking Pareto archiving heuristics in the presence of concept drift: diversity versus age
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- @InProceedings{Atwater:2013:GECCO,
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author = "Aaron Atwater and Malcolm I. Heywood",
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title = "Benchmarking {Pareto} archiving heuristics in the
presence of concept drift: diversity versus age",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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editor = "Christian Blum and Enrique Alba and Anne Auger and
Jaume Bacardit and Josh Bongard and Juergen Branke and
Nicolas Bredeche and Dimo Brockhoff and
Francisco Chicano and Alan Dorin and Rene Doursat and
Aniko Ekart and Tobias Friedrich and Mario Giacobini and
Mark Harman and Hitoshi Iba and Christian Igel and
Thomas Jansen and Tim Kovacs and Taras Kowaliw and
Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and
John McCall and Alberto Moraglio and
Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and
Gustavo Olague and Yew-Soon Ong and
Michael E. Palmer and Gisele Lobo Pappa and
Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and
Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and
Daniel Tauritz and Leonardo Vanneschi",
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isbn13 = "978-1-4503-1963-8",
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pages = "885--892",
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keywords = "genetic algorithms, genetic programming",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2463372.2463489",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "A framework for coevolving genetic programming teams
with Pareto archiving is benchmarked under two
representative tasks for non-stationary streaming
environments. The specific interest lies in determining
the relative contribution of diversity and aging
heuristics to the maintenance of the Pareto archive.
Pareto archiving, in turn, is responsible for targeting
data (and therefore champion individuals) as
appropriate for retention beyond the limiting scope of
the sliding window interface to the data stream.
Fitness sharing alone is considered most effective
under a non-stationary stream characterised by
continuous (incremental) changes. Fitness sharing with
an aging heuristic acts as the preferred heuristic when
the stream is characterised by non-stationary stepwise
changes.",
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notes = "Also known as \cite{2463489} GECCO-2013 A joint
meeting of the twenty second international conference
on genetic algorithms (ICGA-2013) and the eighteenth
annual genetic programming conference (GP-2013)",
- }
Genetic Programming entries for
Aaron Atwater
Malcolm Heywood
Citations