The Effects of Randomly Sampled Training Data on Program Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@TechReport{oai:CiteSeerPSU:250158,

• title = "The Effects of Randomly Sampled Training Data on Program Evolution",
• author = "Brian J. Ross",
• institution = "Dept. of Computer Science, Brock University",
• year = "1999",
• type = "Technical Report",
• number = "CS-99-03",
• address = "Canada",
• month = nov,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.cosc.brocku.ca/~bross/research/cs9903.ps",
• URL = "http://citeseer.ist.psu.edu/250158.html",
• citeseer-references = "oai:CiteSeerPSU:178700; oai:CiteSeerPSU:127185; oai:CiteSeerPSU:333851; oai:CiteSeerPSU:331862",
• annote = "The Pennsylvania State University CiteSeer Archives",
• language = "en",
• oai = "oai:CiteSeerPSU:250158",
• rights = "unrestricted",
• abstract = "The effects of randomly sampled training data during genetic programming is empirically investigated. Sometimes the most natural, if not only, means of characterizing the target behaviour for some problems is to randomly sample training cases inherent to the problems in question. A natural question to raise about this strategy is, how deleterious is the randomly sampling of training data to evolution performance? Would such sampling reduce the evolutionary search to hill climbing? We address these questions by undertaking a suite of different GP experiments. Various sampling strategies are used, such as different training set sizes, single and multiple samples per run, and manually derived {"}ideal distribution{"} training sets. Both generational and steady--state evolution are tested, in order to see if random sampling particularly affects one or the other. Non-- evolutionary search strategies, such as hill climbing and random search, are also used for comparison. Th...",
• notes = "See also \cite{BRoss:2000:GECCO}",
• size = "8 pages",

}

Genetic Programming entries for Brian J Ross

Citations