Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{brabazon:2002:EuroGP,

• title = "Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution",
• author = "Anthony Brabazon and Michael O'Neill and Conor Ryan and Robin Matthews",
• editor = "James A. Foster and Evelyne Lutton and Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
• booktitle = "Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002",
• volume = "2278",
• series = "LNCS",
• pages = "103--112",
• address = "Kinsale, Ireland",
• publisher_address = "Berlin",
• month = "3-5 " # apr,
• publisher = "Springer-Verlag",
• year = "2002",
• keywords = "genetic algorithms, genetic programming, grammatical evolution",
• ISBN = "3-540-43378-3",
• DOI = "doi:10.1007/3-540-45984-7_10",
• 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.",
• notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",

}

Genetic Programming entries for Anthony Brabazon Michael O'Neill Conor Ryan Robin Matthews

Citations