Validation Sets for Evolutionary Curtailment with Improved Generalisation

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{DBLP:conf/ichit/FitzgeraldR11,

• author = "Jeannie Fitzgerald and Conor Ryan",
• title = "Validation Sets for Evolutionary Curtailment with Improved Generalisation",
• booktitle = "5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011",
• year = "2011",
• editor = "Geuk Lee and Daniel Howard and Dominik Slezak",
• volume = "6935",
• series = "Lecture Notes in Computer Science",
• pages = "282--289",
• address = "Daejeon, Korea",
• month = sep # " 22-24",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-24081-2",
• DOI = "doi:10.1007/978-3-642-24082-9_35",
• size = "8 page",
• abstract = "This paper investigates the leveraging of a validation data set with Genetic Programming (GP) to counteract over-fitting. It considers fitness on both training and validation fitness, combined with with an early stopping mechanism to improve generalisation while significantly reducing run times. The method is tested on six benchmark binary classification data sets. Results of this preliminary investigation suggest that the strategy can deliver equivalent or improved results on test data.",
• notes = "ICHIT (1)",
• affiliation = "Jeannie Fitzgerald, BDS Group, CSIS Department, University of Limerick, Ireland",

}

Genetic Programming entries for Jeannie Fitzgerald Conor Ryan

Citations