Langdon, WB;
Ochoa, G;
(2016)
Genetic improvement: A key challenge for evolutionary computation.
In:
Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC).
(pp. pp. 3068-3075).
IEEE: Vancouver, BC, Canada.
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Abstract
Automatic Programming has long been a sub-goal of Artificial Intelligence (AI). It is feasible in limited domains. Genetic Improvement (GI) has expanded these dramatically to more than 100 000 lines of code by building on human written applications. Further scaling may need key advances in both Search Based Software Engineering (SBSE) and Evolutionary Computation (EC) research, particularly on representations, genetic operations, fitness landscapes, fitness surrogates, multi objective search and co-evolution.
| Type: | Proceedings paper |
|---|---|
| Title: | Genetic improvement: A key challenge for evolutionary computation |
| Event: | 2016 IEEE Congress on Evolutionary Computation (CEC) |
| ISBN-13: | 9781509006229 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CEC.2016.7744177 |
| Publisher version: | http://dx.doi.org/10.1109/CEC.2016.7744177 |
| Language: | English |
| Additional information: | © 2016 IEEE. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| 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/10055927 |
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