Dynamic Training Subset Selection for Supervised Learning in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8592

@InProceedings{ga94aGathercole,

• author = "Chris Gathercole and Peter Ross",
• title = "Dynamic Training Subset Selection for Supervised Learning in Genetic Programming",
• booktitle = "Parallel Problem Solving from Nature III",
• year = "1994",
• editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard M{\"a}nner",
• series = "LNCS",
• volume = "866",
• pages = "312--321",
• address = "Jerusalem",
• publisher_address = "Berlin, Germany",
• month = "9-14 " # oct,
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, DSS",
• ISBN = "3-540-58484-6",
• URL = "http://citeseer.ist.psu.edu/gathercole94dynamic.html",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/94-006.ps.gz",
• DOI = "doi:10.1007/3-540-58484-6_275",
• size = "10 pages",
• 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 GP run to select difficult and/or disused cases,

  Historical Subset Selection (HSS), using previous GP runs,

  Random Subset Selection (RSS).

  Various runs have shown that GP+DSS can produce better results in less than 20percent of the time taken by GP. GP+HSS can nearly match the results of GP, and, perhaps surprisingly, GP+RSS can occasionally approach the results of GP. GP+DSS also produced better, more general results than those reported in a paper for a variety of Neural Networks when used on a substantial problem, known as the Thyroid problem.",

• notes = "PPSN3 Describes how to reduce the number of fitness case evaluations in difficult GP problems by selecting a small subset of the training data. Dynamic Subset Selection can produce better results than GP in less than 20percent of the time. Population size of 5,000 and 10,000.

  DSS used in Discipulus \cite{francone:manual}",

}

Genetic Programming entries for Chris Gathercole Peter Ross

Citations