Some Training Subset Selection Methods for Supervised Learning in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Unpublished{gathercole:1994:stss,

• author = "Chris Gathercole and Peter Ross",
• title = "Some Training Subset Selection Methods for Supervised Learning in Genetic Programming",
• note = "Presented at ECAI'94 Workshop on Applied Genetic and other Evolutionary Algorithms",
• year = "1994",
• keywords = "genetic algorithms, genetic programming, LEF, DSS",
• URL = "http://citeseer.ist.psu.edu/cache/papers/cs/733/ftp:zSzzSzftp.dai.ed.ac.ukzSzpubzSzuserzSzchrisgzSzchrisg_dss_paper_resubmitted_to_ecai94workshop.pdf/gathercole94some.pdf",
• URL = "http://citeseer.ist.psu.edu/gathercole94some.html",
• abstract = "When using the Genetic Programming (GP) Algorithm on a difficult problem with a large set of training cases, a large population size is needed and a very large number of function-tree evaluations must be carried out. This paper describes how to reduce the number of such evaluations by selecting a small subset of the training data set on which to actually carry out the GP algorithm. Three subset selection methods described in the paper are: Dynamic Subset Selection (DSS), using the current...",
• size = "13 pages",

}

Genetic Programming entries for Chris Gathercole Peter Ross

Citations