|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||GP vs GI: if you can't beat them, join them|
|Citation:||Woodward J, Johnson C & Brownlee A (2016) GP vs GI: if you can't beat them, join them In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. Genetic and Evolutionary Computation Conference, GECCO-2016, New York, 20.07.2016-24.07.2016. New York: ACM, pp. 1155-1156. https://doi.org/10.1145/2908961.2931694.|
|Conference Name:||Genetic and Evolutionary Computation Conference, GECCO-2016|
|Conference Dates:||2016-07-20 - 2016-07-24|
|Conference Location:||Denver, CO, USA|
|Abstract:||Genetic Programming (GP) has been criticized for targeting irrelevant problems , and is also true of the wider machine learning community . which has become detached from the source of the data it is using to drive the field forward. However, recently GI provides a fresh perspective on automated programming. In contrast to GP, GI begins with existing software, and therefore immediately has the aim of tackling real software. As evolution is the main approach to GI to manipulating programs, this connection with real software should persuade the GP community to confront the issues around what it originally set out to tackle i.e. evolving real software.|
|Status:||AM - Accepted Manuscript|
|Rights:||Publisher policy allows this work to be made available in this repository. Published in GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion Pages 1155-1156 by ACM. The original publication is available at: http://dl.acm.org/citation.cfm?id=2931694&CFID=823928677&CFTOKEN=80769513|
|gpVSgi05.pdf||Fulltext - Accepted Version||159.3 kB||Adobe PDF||View/Open|
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