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Optimising Existing Software with Genetic Programming

Langdon, WB; Harman, M; (2013) Optimising Existing Software with Genetic Programming. IEEE Transactions on Evolutionary Computation , PP (99) -. 10.1109/TEVC.2013.2281544. Green open access

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Abstract

We show genetic improvement of programs (GIP) can scale by evolving increased performance in a widely-used and highly complex 50000 line system. GISMOE found code that is 70 times faster (on average) and yet is at least as good functionally. Indeed it even gives a small semantic gain.

Type: Article
Title: Optimising Existing Software with Genetic Programming
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TEVC.2013.2281544
Publisher version: http://dx.doi.org/10.1109/TEVC.2013.2281544
Language: English
Additional information: This is the author's accepted version of this published work. © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Genetic algorithms, genetic programming, Software, Search methods, genetic algorithms, SBSE, automatic software re-engineering, Bowtie2GP, multiple objective exploration
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/1413298
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