Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{brabazon:2002:EuroGP,
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title = "Evolving classifiers to model the relationship between
strategy and corporate performance using grammatical
evolution",
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author = "Anthony Brabazon and Michael O'Neill and
Conor Ryan and Robin Matthews",
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editor = "James A. Foster and Evelyne Lutton and
Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
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booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
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volume = "2278",
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series = "LNCS",
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pages = "103--112",
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address = "Kinsale, Ireland",
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publisher_address = "Berlin",
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month = "3-5 " # apr,
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publisher = "Springer-Verlag",
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year = "2002",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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ISBN = "3-540-43378-3",
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DOI = "doi:10.1007/3-540-45984-7_10",
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abstract = "This study examines the potential of grammatical
evolution to construct a linear classifier to predict
whether a firm's corporate strategy will increase or
decrease shareholder wealth. Shareholder wealth is
measured using a relative fitness criterion, the change
in a firm's market-value-added ranking in the
Stern-Stewart Performance 1000 list, over a four year
period, 1992-1996. Model inputs and structure are
selected by means of grammatical evolution. The best
classifier correctly categorised the direction of
performance ranking change in 66.38percent of the firms
in the training set and 65percent in the out-of-sample
validation set providing support for a hypothesis that
changes in corporate strategy are linked to changes in
corporate performance.",
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notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
- }
Genetic Programming entries for
Anthony Brabazon
Michael O'Neill
Conor Ryan
Robin Matthews
Citations