TensorFlow Enabled Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Staats:2017:GECCO,
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author = "Kai Staats and Edward Pantridge and Marco Cavaglia and
Iurii Milovanov and Arun Aniyan",
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title = "{TensorFlow} Enabled Genetic Programming",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4939-0",
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address = "Berlin, Germany",
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pages = "1872--1879",
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size = "8 pages",
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URL = "http://doi.acm.org/10.1145/3067695.3084216",
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DOI = "doi:10.1145/3067695.3084216",
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acmid = "3084216",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, evolutionary
computation, gpu, machine learning, multicore,
parallel, tensorflow, vectorized",
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month = "15-19 " # jul,
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abstract = "Genetic Programming, a kind of evolutionary
computation and machine learning algorithm, is shown to
benefit significantly from the application of
vectorized data and the TensorFlow numerical
computation library on both CPU and GPU architectures.
The open source, Python Karoo GP is employed for a
series of 190 tests across 6 platforms, with real-world
datasets ranging from 18 to 5.5M data points. This body
of tests demonstrates that datasets measured in tens
and hundreds of data points see 2--15x improvement when
moving from the scalar/SymPy configuration to the
vector/TensorFlow configuration, with a single core
performing on par or better than multiple CPU cores and
CPUs. A dataset composed of 90,000 data points
demonstrates a single vector/TensorFlow CPU core
performing 875x better than 40 scalar/Sympy CPU cores.
And a dataset containing 5.5M data points sees GPU
configurations out-performing CPU configurations on
average by 1.3x.",
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notes = "Also known as \cite{Staats:2017:TEG:3067695.3084216}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Kai Staats
Edward R Pantridge
Marco Cavaglia
Iurii Milovanov
Arun Aniyan
Citations