research-article

Genetic Improvement of Data for Maths Functions

Authors:

William B. Langdon,

Oliver KraussAuthors Info & Claims

ACM Transactions on Evolutionary Learning and Optimization, Volume 1, Issue 2

Article No.: 7, Pages 1 - 30

https://doi.org/10.1145/3461016

Published: 29 July 2021 Publication History

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.

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Cited By

Langdon W(2023)Jaws 30Genetic Programming and Evolvable Machines10.1007/s10710-023-09467-x24:2Online publication date: 22-Nov-2023
https://dl.acm.org/doi/10.1007/s10710-023-09467-x
Rainford PPorter B(2022)Code and Data Synthesis for Genetic Improvement in Emergent Software SystemsACM Transactions on Evolutionary Learning and Optimization10.1145/35428232:2(1-35)Online publication date: 16-Aug-2022
https://dl.acm.org/doi/10.1145/3542823
Zuo SBlot APetke JWagner M(2022)Evaluation of genetic improvement tools for improvement of non-functional properties of softwareProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534004(1956-1965)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534004

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cover image ACM Transactions on Evolutionary Learning and Optimization

ACM Transactions on Evolutionary Learning and Optimization Volume 1, Issue 2

June 2021

107 pages

EISSN:2688-3007

DOI:10.1145/3476125

Editors:
Juergen Branke
Warwick Business School, UK
,
Darrell Whitley
Colorado State University, USA

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Publication History

Published: 29 July 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Received: 01 January 2020

Published in TELO Volume 1, Issue 2

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Cited By

Langdon W(2023)Jaws 30Genetic Programming and Evolvable Machines10.1007/s10710-023-09467-x24:2Online publication date: 22-Nov-2023
https://dl.acm.org/doi/10.1007/s10710-023-09467-x
Rainford PPorter B(2022)Code and Data Synthesis for Genetic Improvement in Emergent Software SystemsACM Transactions on Evolutionary Learning and Optimization10.1145/35428232:2(1-35)Online publication date: 16-Aug-2022
https://dl.acm.org/doi/10.1145/3542823
Zuo SBlot APetke JWagner M(2022)Evaluation of genetic improvement tools for improvement of non-functional properties of softwareProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534004(1956-1965)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534004

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