Conference Proceeding

A Survey of Genetic Improvement Search Spaces

Details

Citation

Petke J, Alexander B, Barr ET, Brownlee AEI, Wagner M & White DR (2019) A Survey of Genetic Improvement Search Spaces. In: López-Ibáñez M (ed.) GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO '19 - Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: Association for Computing Machinery, pp. 1715-1721. https://doi.org/10.1145/3319619.3326870

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.

StatusPublished
FundersEPSRC Engineering and Physical Sciences Research Council and Engineering and Physical Sciences Research Council
Publication date31/12/2019
Publication date online31/07/2019
URLhttp://hdl.handle.net/1893/31579
PublisherAssociation for Computing Machinery
Place of publicationNew York
ISBN978-1-4503-6748-6
ConferenceGECCO '19 - Genetic and Evolutionary Computation Conference
Conference locationPrague, Czech Republic
Dates

People (1)

People

Dr Sandy Brownlee
Dr Sandy Brownlee

Senior Lecturer in Computing Science, Computing Science and Mathematics - Division