- author = "Cliodhna Tuite and Alexandros Agapitos and Michael O'Neill and Anthony Brabazon",
- title = "Early stopping criteria to counteract overfitting in genetic programming",
- 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",
- pages = "203--204",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, symbolic regression, overfitting: Poster",
- isbn13 = "978-1-4503-0690-4",
-
DOI = "
doi:10.1145/2001858.2001971",
- publisher_address = "New York, NY, USA",
- language = "en",
- oai = "oai:CiteSeerX.psu:10.1.1.1023.6399",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1023.6399",
-
URL = "
http://researchrepository.ucd.ie/bitstream/handle/10197/3538/Early_Stopping_Criteria_to_Counteract_Overfitting_in_Genetic_Programming.pdf",
- size = "2 pages",
- abstract = "Early stopping typically stops training the first time validation fitness disimproves. This may not be the best strategy given that validation fitness can subsequently increase or decrease. We examine the effects of stopping subsequent to the first disimprovement in validation fitness, on symbolic regression problems. Stopping points are determined using criteria which measure generalisation loss and training progress. Results suggest that these criteria can improve the generalistion ability of symbolic regression functions evolved using Grammar-based GP.",
-
notes = "Also known as \cite{2001971} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
