Langdon, WB;
Alexander, BJ;
(2023)
Genetic Improvement of OLC and H3 with Magpie.
In:
Proceedings of the IEEE/ACM International Workshop on Genetic Improvement (GI) 2023.
(pp. pp. 9-16).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
Magpie (Machine Automated General Performance Improvement via Evolution of software) has been recently developed by Aymeric Blot from PyGGI 2.0. Like PyGGI, it claims to be able to optimise computer source code written in arbitrary programming languages. So far it has been demonstrated on benchmarks written in Python and C. Recently we have used hill climbing to customise two industrial open source programs: Google's Open Location Code OLC and Uber's Hexagonal Hierarchical Spatial Index H3 [W. B. Langdon et al., 'Genetic improvement of LLVM intermediate representation', in EuroGP 2023]. Magpie found much faster improvements (reducing instruction counts by up to 15% v. 2%) which generalise. Various glitches in Magpie are also reported.
Type: | Proceedings paper |
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Title: | Genetic Improvement of OLC and H3 with Magpie |
Event: | 2023 IEEE/ACM International Workshop on Genetic Improvement (GI) |
Location: | Melbourne, Australia |
Dates: | 20th May 2023 |
ISBN-13: | 979-8-3503-1232-4 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/GI59320.2023.00011 |
Publisher version: | https://doi.org/10.1109/GI59320.2023.00011 |
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. |
Keywords: | Genetic programming, GP, linear representation, SBSE, software resilience, automatic code customisation, world wide location, plus codes, zip code |
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/10176410 |
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