Improving Generalization of Evolved Programs Through Automatic Simplification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Helmuth:2017:GECCO,
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author = "Thomas Helmuth and Nicholas Freitag McPhee and
Edward Pantridge and Lee Spector",
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title = "Improving Generalization of Evolved Programs Through
Automatic Simplification",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4920-8",
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address = "Berlin, Germany",
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pages = "937--944",
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size = "8 pages",
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URL = "http://cs.hamilton.edu/~thelmuth/Pubs/2017-GECCO-simplification-for-generalization.pdf",
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URL = "http://doi.acm.org/10.1145/3071178.3071330",
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DOI = "doi:10.1145/3071178.3071330",
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acmid = "3071330",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, automatic
simplification, generalization, overfitting, push",
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month = "15-19 " # jul,
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abstract = "Programs evolved by genetic programming unfortunately
often do not generalize to unseen data. Reliable
synthesis of programs that generalize to unseen data is
therefore an important open problem. We present
evidence that smaller programs evolved using the PushGP
system tend to generalize better over a range of
program synthesis problems. Like in many genetic
programming systems, programs evolved by PushGP usually
have pieces that can be removed without changing the
behaviour of the program. We describe methods for
automatically simplifying evolved programs to make them
smaller and potentially improve their generalization.
We present five simplification methods and analyse
their strengths and weaknesses on a suite of general
program synthesis benchmark problems. All of our
methods use a straightforward hill-climbing procedure
to remove pieces of a program while ensuring that the
resulting program gives the same errors on the training
data as did the original program. We show that
automatic simplification, previously used both for
post-run analysis and as a genetic operator, can
significantly improve the generalization rates of
evolved programs.",
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notes = "Also known as \cite{Helmuth:2017:IGE:3071178.3071330}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Thomas Helmuth
Nicholas Freitag McPhee
Edward R Pantridge
Lee Spector
Citations