Surrogate Fitness via Factorization of Interaction Matrix
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Liskowski:2016:EuroGP,
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author = "Pawel Liskowski and Krzysztof Krawiec",
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title = "Surrogate Fitness via Factorization of Interaction
Matrix",
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booktitle = "EuroGP 2016: Proceedings of the 19th European
Conference on Genetic Programming",
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year = "2016",
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month = "30 " # mar # "--1 " # apr,
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editor = "Malcolm I. Heywood and James McDermott and
Mauro Castelli and Ernesto Costa and Kevin Sim",
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series = "LNCS",
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volume = "9594",
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publisher = "Springer Verlag",
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address = "Porto, Portugal",
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pages = "68--82",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, test-based
problem, recommender systems, machine learning,
surrogate fitness",
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isbn13 = "978-3-319-30668-1",
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DOI = "doi:10.1007/978-3-319-30668-1_5",
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abstract = "We propose \mname, a method that reduces the number of
required interactions between programs and tests in
genetic programming. \mname performs factorization of
the matrix of the outcomes of interactions between the
programs in a working population and the tests.
Crucially, that factorization is applied to matrix that
is only partially filled with interaction outcomes,
i.e., sparse. The reconstructed approximate interaction
matrix is then used to calculate the fitness of
programs. In empirical comparison to several reference
methods in categorical domains, \mname attains higher
success rate of synthesizing correct programs within a
given computational budget.",
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notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
conjunction with EvoCOP2016, EvoMusArt2016 and
EvoApplications2016",
- }
Genetic Programming entries for
Pawel Liskowski
Krzysztof Krawiec
Citations