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Genetic improvement of programs

Langdon, W; (2015) Genetic improvement of programs. In: Proceedings of the 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing 2014. IEEE Green open access

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Abstract

Genetic programming can optimise software, including: evolving test benchmarks, generating hyper-heuristics by searching meta-heuristics, generating communication protocols, composing telephony systems and web services, generating improved hashing and C++ heap managers, redundant programming and even automatic bug fixing. Particularly in embedded real-time or mobile systems, there may be many ways to trade off expenses (such as time, memory, energy, power consumption) vs. Functionality. Human programmers cannot try them all. Also the best multi-objective Pareto trade off may change with time, underlying hardware and network connection or user behaviour. It may be GP can automatically suggest different trade offs for each new market. Recent results include substantial speed up by evolving a new version of a program customised for a special case.

Type: Proceedings paper
Title: Genetic improvement of programs
Event: The 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Location: Timisoara, Romania
Dates: 22nd- 25th September 2014
ISBN-13: 978-1-4799-8448-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/SYNASC.2014.10
Publisher version: https://doi.org/10.1109/SYNASC.2014.10
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, Software engineering, Graphics processing units, Evolutionary computation, Grammar
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10074596
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