Greedy Semantic Local Search for Small Solutions
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Ffrancon:2015:GECCOcomp,
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author = "Robyn Ffrancon and Marc Schoenauer",
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title = "Greedy Semantic Local Search for Small Solutions",
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booktitle = "GECCO 2015 Semantic Methods in Genetic Programming
(SMGP'15) Workshop",
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year = "2015",
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editor = "Colin Johnson and Krzysztof Krawiec and
Alberto Moraglio and Michael O'Neill",
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isbn13 = "978-1-4503-3488-4",
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keywords = "genetic algorithms, genetic programming, Semantic
Methods",
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pages = "1293--1300",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "https://hal.inria.fr/UMR8623/hal-01169074v1",
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URL = "http://doi.acm.org/10.1145/2739482.2768504",
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DOI = "doi:10.1145/2739482.2768504",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Semantic Backpropagation (SB) was introduced in GP so
as to take into account the semantics of a GP tree at
all intermediate states of the program execution, i.e.,
at each node of the tree. The idea is to compute the
optimal should-be values each subtree should return,
whilst assuming that the rest of the tree is unchanged,
and to choose a subtree that matches as well as
possible these target values. A single tree is evolved
by iteratively replacing one of its nodes with the best
subtree from a static library according to this local
fitness, with tree size as a secondary criterion.
Previous results for standard Boolean GP benchmarks
that have been obtained by the authors with another
variant of SB are improved in term of tree size. SB is
then applied for the first time to categorical GP
benchmarks, and outperforms all known results to date
for three variable finite algebras.",
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notes = "Also known as \cite{2768504} Distributed at
GECCO-2015.",
- }
Genetic Programming entries for
Robyn Ffrancon
Marc Schoenauer
Citations