Generating a novel sort algorithm using Reinforcement Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{White:2010:cec,
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author = "Spencer K. White and Tony Martinez and
George Rudolph",
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title = "Generating a novel sort algorithm using Reinforcement
Programming",
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booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
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year = "2010",
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address = "Barcelona, Spain",
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month = "18-23 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4244-6910-9",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.9164",
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URL = "http://axon.cs.byu.edu/papers/Spencer.CEC2010Proc.pdf",
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DOI = "doi:10.1109/CEC.2010.5586457",
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size = "8 pages",
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abstract = "Reinforcement Programming (RP) is a new approach to
automatically generating algorithms, that uses
reinforcement learning techniques. This paper describes
the RP approach and gives results of experiments using
RP to generate a generalised, in-place, iterative sort
algorithm. The RP approach improves on earlier results
that that use genetic programming (GP). The resulting
algorithm is a novel algorithm that is more efficient
than comparable sorting routines. RP learns the sort in
fewer iterations than GP and with fewer resources.
Results establish interesting empirical bounds on
learning the sort algorithm: A list of size 4 is
sufficient to learn the generalized sort algorithm. The
training set only requires one element and learning
took less than 200,000 iterations. RP has also been
used to generate three binary addition algorithms: a
full adder, a binary incrementer, and a binary adder.",
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notes = "WCCI 2010. Also known as \cite{5586457}",
- }
Genetic Programming entries for
Spencer K White
Tony R Martinez
George Rudolph
Citations