Genetic Improvement of Data for Maths Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@Article{Langdon:TELO,

• author = "William B. Langdon and Oliver Krauss",
• title = "Genetic Improvement of Data for Maths Functions",
• journal = "ACM Transactions on Evolutionary Learning and Optimization",
• year = "2021",
• volume = "1",
• number = "2",
• pages = "Article No.: 7",
• month = jul,
• 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",
• ISSN = "2688-299X",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Langdon_TELO.pdf",
• DOI = "10.1145/3461016",
• video_url = "https://youtu.be/Z3gxNb4h3u8",
• code_url = "https://github.com/oliver-krauss/Replication_GI_Division_Free_Division",
• code_url = "http://dx.doi.org://doi:10.5281/zenodo.3755346",
• size = "30 pages",
• 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.",
• notes = "https://dlnext.acm.org/journal/telo",

}

Genetic Programming entries for William B Langdon Oliver Krauss

Citations