Petke, J;
Alexander, B;
Barr, ET;
Brownlee, AEI;
Wagner, M;
White, DR;
(2019)
A survey of genetic improvement search spaces.
In: López-Ibáñez, M and Auger, A and Stützle, T, (eds.)
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion.
(pp. pp. 1715-1721).
Association for Computing Machinery (ACM): New York, NY, USA.
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Abstract
Genetic Improvement (GI) uses automated search to improve existing software. Most GI work has focused on empirical studies that successfully apply GI to improve software's running time, fix bugs, add new features, etc. There has been little research into why GI has been so successful. For example, genetic programming has been the most commonly applied search algorithm in GI. Is genetic programming the best choice for GI? Initial attempts to answer this question have explored GI's mutation search space. This paper summarises the work published on this question to date.
| Type: | Proceedings paper |
|---|---|
| Title: | A survey of genetic improvement search spaces |
| Event: | Genetic and Evolutionary Computation Conference (GECCO '19) |
| Location: | Prague, Czech Republic |
| ISBN-13: | 978-1-4503-6748-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3319619.3326870 |
| Publisher version: | https://doi.org/10.1145/3319619.3326870 |
| 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 Improvement, Search-based Software Engineering, Program Repair, Search Space, GI, APR, SBSE |
| 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/10074378 |
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