Computational Complexity, Genetic Programming, and Implications
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{rylander:2001:EuroGP,
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author = "Bart Rylander and Terry Soule and James Foster",
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title = "Computational Complexity, Genetic Programming, and
Implications",
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booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
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year = "2001",
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editor = "Julian F. Miller and Marco Tomassini and
Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
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volume = "2038",
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series = "LNCS",
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pages = "348--360",
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address = "Lake Como, Italy",
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publisher_address = "Berlin",
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month = "18-20 " # apr,
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organisation = "EvoNET",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, Computational
Complexity, Quantum Computing: Poster",
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ISBN = "3-540-41899-7",
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URL = "http://faculty.up.edu/rylander/my%20pubs/egpfin.pdf",
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DOI = "doi:10.1007/3-540-45355-5_28",
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size = "13 pages",
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abstract = "Recent theory work has shown that a Genetic Program
(GP) used to produce programs may have output that is
bounded above by the GP itself [l]. This paper presents
proofs that show that 1) a program that is the output
of a GP or any inductive process has complexity that
can be bounded by the Kolmogorov complexity of the
originating program; 2) this result does not hold if
the random number generator used in the evolution is a
true random source; and 3) an optimization problem
being solved with a GP will have a complexity that can
be bounded below by the growth rate of the minimum
length problem representation used for the
implementation. These results are then used to provide
guidance for GP implementation.",
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notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
- }
Genetic Programming entries for
Bart I Rylander
Terence Soule
James A Foster
Citations