Genetic Improvement of Data for Maths Functions
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Langdon:TELO,
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author = "William B. Langdon and Oliver Krauss",
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title = "Genetic Improvement of Data for Maths Functions",
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journal = "ACM Transactions on Evolutionary Learning and
Optimization",
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year = "2021",
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volume = "1",
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number = "2",
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pages = "Article No.: 7",
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month = jul,
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keywords = "genetic algorithms, genetic programming, genetic
improvement, evolutionary computing, Evolution
Strategies, CMA-ES, software engineering, search based
software engineering, SBSE, GGGP, software maintenance
of empirical constants, software maintenance of
literals, data transplantation, glibc, sqrt, cbrt,
vector normalisation, log2, Newton's method",
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ISSN = "2688-299X",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Langdon_TELO.pdf",
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DOI = "doi:10.1145/3461016",
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video_url = "https://youtu.be/Z3gxNb4h3u8",
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code_url = "https://github.com/oliver-krauss/Replication_GI_Division_Free_Division",
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code_url = "http://dx.doi.org://doi:10.5281/zenodo.3755346",
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size = "30 pages",
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abstract = "We use continuous optimisation and manual code changes
to evolve up to 1024 Newton-Raphson numerical values
embedded in an open source GNU C library glibc square
root sqrt to implement a double precision cube root
routine cbrt, binary logarithm log2 and reciprocal
square root function for C in seconds. The GI inverted
square root x**(-0.5) is far more accurate than Quakes
InvSqrt, Quare root. GI shows potential for
automatically creating mobile or low resource mote
smart dust bespoke custom mathematical libraries with
new functionality.",
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notes = "https://dlnext.acm.org/journal/telo",
- }
Genetic Programming entries for
William B Langdon
Oliver Krauss
Citations