Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23386
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Woodward, John
Johnson, Colin
Brownlee, Alexander
Contact Email: ab90@cs.stir.ac.uk
Title: GP vs GI: if you can't beat them, join them
Editors: Friedrich, T
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, New York: ACM. Genetic and Evolutionary Computation Conference, GECCO-2016, 20.7.2016 - 24.7.2016, Denver, CO, USA, pp. 1155-1156.
Issue Date: 2016
Conference Name: Genetic and Evolutionary Computation Conference, GECCO-2016
Conference Dates: 2016-07-20T00:00:00Z
Conference Location: Denver, CO, USA
Abstract: Genetic Programming (GP) has been criticized for targeting irrelevant problems [12], and is also true of the wider machine learning community [11]. 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.
Type: Conference Paper
Status: Book Chapter: author post-print (pre-copy editing)
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
URI: http://hdl.handle.net/1893/23386
URL: http://dl.acm.org/citation.cfm?id=2931694&CFID=823928677&CFTOKEN=80769513
Affiliation: Computing Science and Mathematics
University of Kent
Computing Science - CSM Dept

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