TensorGP - Genetic Programming Engine in TensorFlow
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Baeta:2021:evoapplications,
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author = "Francisco Baeta and Joao Correia and Tiago Martins and
Penousal Machado",
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title = "{TensorGP} - Genetic Programming Engine in
{TensorFlow}",
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booktitle = "24th International Conference, EvoApplications 2021",
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year = "2021",
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month = "7-9 " # apr,
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editor = "Pedro Castillo and Juanlu Jimenez-Laredo",
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series = "LNCS",
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volume = "12694",
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publisher = "Springer Verlag",
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address = "virtual event",
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pages = "763--778",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming,
Parallelisation, Vectorisation, TensorFlow, GPU
computing",
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isbn13 = "978-3-030-72698-0",
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DOI = "doi:10.1007/978-3-030-72699-7_48",
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abstract = "we resort to the TensorFlow framework to investigate
the benefits of applying data vectorisation and fitness
caching methods to domain evaluation in Genetic
Programming. For this purpose, an independent engine
was developed, TensorGP, along with a testing suite to
extract comparative timing results across different
architectures and amongst both iterative and vectorized
approaches. Our performance benchmarks demonstrate that
by exploiting the TensorFlow eager execution model,
performance gains of up to two orders of magnitude can
be achieved on a parallel approach running on dedicated
hardware when compared to a standard iterative
approach.",
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notes = "http://www.evostar.org/2021/ EvoApplications2021 held
in conjunction with EuroGP'2021, EvoCOP2021 and
EvoMusArt2021",
- }
Genetic Programming entries for
Francisco Baeta
Joao Nuno Goncalves Costa Cavaleiro Correia
Tiago Martins
Penousal Machado
Citations