The Effects of Randomly Sampled Training Data on Program Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @TechReport{oai:CiteSeerPSU:250158,
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title = "The Effects of Randomly Sampled Training Data on
Program Evolution",
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author = "Brian J. Ross",
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institution = "Dept. of Computer Science, Brock University",
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year = "1999",
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type = "Technical Report",
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number = "CS-99-03",
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address = "Canada",
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month = nov,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.cosc.brocku.ca/~bross/research/cs9903.ps",
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URL = "http://citeseer.ist.psu.edu/250158.html",
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citeseer-references = "oai:CiteSeerPSU:178700; oai:CiteSeerPSU:127185;
oai:CiteSeerPSU:333851; oai:CiteSeerPSU:331862",
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annote = "The Pennsylvania State University CiteSeer Archives",
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language = "en",
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oai = "oai:CiteSeerPSU:250158",
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rights = "unrestricted",
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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...",
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notes = "See also \cite{BRoss:2000:GECCO}",
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size = "8 pages",
- }
Genetic Programming entries for
Brian J Ross
Citations