Automated discovery of test statistics using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.6712
- @Article{Moore:2019:GPEM,
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author = "Jason H. Moore and Randal S. Olson and Yong Chen and
Moshe Sipper",
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title = "Automated discovery of test statistics using genetic
programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2019",
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volume = "20",
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number = "1",
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pages = "127--137",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Statistics,
Optimization, Student's T test",
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ISSN = "1389-2576",
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URL = "
http://human-competitive.org/sites/default/files/automated-discovery-of-test-statistics_0.pdf",
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DOI = "
doi:10.1007/s10710-018-9338-z",
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size = "11 pages",
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abstract = "The process of developing new test statistics is
laborious, requiring the manual development and
evaluation of mathematical functions that satisfy
several theoretical properties. Automating this
process, hitherto not done, would greatly accelerate
the discovery of much-needed, new test statistics. This
automation is a challenging problem because it requires
the discovery method to know something about the
desirable properties of a good test statistic in
addition to having an engine that can develop and
explore candidate mathematical solutions with an
intuitive representation. In this paper we describe a
genetic programming-based system for the automated
discovery of new test statistics. Specifically, our
system was able to discover test statistics as powerful
as the t test for comparing sample means from two
distributions with equal variances.",
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notes = "2019 Humies finalist. Slides:
http://www.human-competitive.org/sites/default/files/moore.pdf",
- }
Genetic Programming entries for
Jason H Moore
Randal S Olson
Yong Chen
Moshe Sipper
Citations