Langdon, WB;
Krauss, O;
(2021)
Genetic Improvement of Data for Maths Functions.
ACM Transactions on Evolutionary Learning and Optimization
, 1
(2)
pp. 1-30.
10.1145/3461016.
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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-1/2 is far more accurate than Quake's InvSqrt, Quare root. GI shows potential for automatically creating mobile or low resource mote smart dust bespoke custom mathematical libraries with new functionality.
| Type: | Article |
|---|---|
| Title: | Genetic Improvement of Data for Maths Functions |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3461016 |
| Publisher version: | http://dx.doi.org/10.1145/3461016 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10183978 |
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