Searching for novel clustering programs
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Naredo:2013:GECCO,
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author = "Enrique Naredo and Leonardo Trujillo",
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title = "Searching for novel clustering programs",
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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 = "1093--1100",
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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:10.1145/2463372.2463505",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Novelty search (NS) is an open-ended evolutionary
algorithm that eliminates the need for an explicit
objective function. Instead, NS focuses selective
pressure on the search for novel solutions. NS has
produced intriguing results in specialised domains, but
has not been applied in most machine learning areas.
The key component of NS is that each individual is
described by the behaviour it exhibits, and this
description is used to determine how novel each
individual is with respect to what the search has
produced thus far. However, describing individuals in
behavioural space is not trivial, and care must be
taken to properly define a descriptor for a particular
domain. This paper applies NS to a mainstream pattern
analysis area: data clustering. To do so, a descriptor
of clustering performance is proposed and tested on
several problems, and compared with two control
methods, Fuzzy C-means and K-means. Results show that
NS can effectively be applied to data clustering in
some circumstances. NS performance is quite poor on
simple or easy problems, achieving basically random
performance. Conversely, as the problems get harder NS
performs better, and outperforming the control methods.
It seems that the search space exploration induced by
NS is fully exploited only when generating good
solutions is more challenging.",
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notes = "Also known as \cite{2463505} 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
Enrique Naredo
Leonardo Trujillo
Citations