Genetic Improvement of Data for Maths Functions.

William B. Langdon; Oliver Krauss

doi:10.1145/3461016

Genetic Improvement of Data for Maths Functions.

William B. Langdon, Oliver Krauss

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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.

Original language	English
Article number	7
Pages (from-to)	7:1-7:30
Number of pages	30
Journal	ACM Trans. Evol. Learn. Optim.
Volume	1
Issue number	2
DOIs	https://doi.org/10.1145/3461016
Publication status	Published - 29 Jul 2021

Keywords

Evolutionary computing
Newton's method
SBSE
data transplantation
glibc
search based software engineering
software engineering
software maintenance of empirical constants
vector normalisation

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10.1145/3461016

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Genetic Improvement of Data for Maths Functions.

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Keywords

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