Searching for Novel Classifiers
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{naredo:2013:EuroGP,
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author = "Enrique Naredo and Leonardo Trujillo and
Yuliana Martinez",
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title = "Searching for Novel Classifiers",
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booktitle = "Proceedings of the 16th European Conference on Genetic
Programming, EuroGP 2013",
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year = "2013",
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month = "3-5 " # apr,
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editor = "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and
A. Sima Uyar and Bin Hu",
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series = "LNCS",
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volume = "7831",
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publisher = "Springer Verlag",
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address = "Vienna, Austria",
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pages = "145--156",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, Novelty
Search, Classification",
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isbn13 = "978-3-642-37206-3",
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DOI = "doi:10.1007/978-3-642-37207-0_13",
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abstract = "Natural evolution is an open-ended search process
without an a priori fitness function that needs to be
optimised. On the other hand, evolutionary algorithms
(EAs) rely on a clear and quantitative objective. The
Novelty Search algorithm (NS) substitutes fitness-based
selection with a novelty criteria; i.e., individuals
are chosen based on their uniqueness. To do so,
individuals are described by the behaviours they
exhibit, instead of their phenotype or genetic content.
NS has mostly been used in evolutionary robotics, where
the concept of behavioural space can be clearly
defined. Instead, this work applies NS to a more
general problem domain, classification. To this end,
two behavioral descriptors are proposed, each
describing a classifier's performance from two
different perspectives. Experimental results show that
NS-based search can be used to derive effective
classifiers. In particular, NS is best suited to solve
difficult problems, where exploration needs to be
encouraged and maintained.",
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notes = "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
and EvoApplications2013",
- }
Genetic Programming entries for
Enrique Naredo
Leonardo Trujillo
Yuliana Martinez
Citations