Langdon, WB;
Harman, M;
(2013)
Optimising Existing Software with Genetic Programming.
IEEE Transactions on Evolutionary Computation
, PP
(99)
-.
10.1109/TEVC.2013.2281544.
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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 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-pp.ucl.ac.uk/id/eprint/1413298 |
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