- author = "Kristopher Hoover and Rachel Marceau and Tyndall Harris and Nicholas Hardison and David Reif and Alison Motsinger-Reif",
- title = "Optimization of grammatical evolution decision trees",
- booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation",
- year = "2011",
- editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger",
- isbn13 = "978-1-4503-0690-4",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, Bioinformatics, computational, systems, and synthetic biology: Poster",
- pages = "35--36",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
-
DOI = "
doi:10.1145/2001858.2001879",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "The detection of gene-gene and gene-environment interactions in genetic association studies presents a difficult computational and statistical challenge, especially as advances in genotyping technology have rapidly expanded the number of potential genetic predictors in such studies. The scale of these studies makes exhaustive search approaches infeasible, inspiring the application of evolutionary computation algorithms to perform variable selection and build classification models. Recently, an application of grammatical evolution to evolve decision trees (GEDT) has been introduced for detecting interaction models. Initial results were promising, but relied on arbitrary parameter choices for the evolutionary process. In the current study, we present the results of a parameter sweep evaluating the power of GEDT and show that improved parameter choices improves the performance of the method. The results of these experiments are important for the continued optimisation, evaluation, and comparison of this and related methods, and for proper application in real data.",
-
notes = "Also known as \cite{2001879} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
