Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23394
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Unrefereed
Authors: Woodward, John
Johnson, Colin
Brownlee, Alexander
Contact Email: ab90@cs.stir.ac.uk
Title: Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming
Editors: Friedrich, T
Citation: Woodward J, Johnson C & Brownlee A (2016) Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, New York: ACM. GECCO 2016: Genetic and Evolutionary Computation Conference, 20.7.2016 - 24.7.2016, Denver, CO, USA, pp. 1357-1358.
Issue Date: 2016
Conference Name: GECCO 2016: Genetic and Evolutionary Computation Conference
Conference Dates: 2016-07-20T00:00:00Z
Conference Location: Denver, CO, USA
Abstract: Automatically designing algorithms has long been a dream of computer scientists. Early attempts which generate computer programs from scratch, have failed to meet this goal. However, in recent years there have been a number of different technologies with an alternative goal of taking existing programs and attempting to improvement them.  These methods form a continuum of methodologies, from the “limited” ability to change (for example only the parameters) to the “complete” ability to change the whole program. These include; automatic parameter tuning (APT), using GP as a hyper-heuristic (GPHH) to automatically design algorithms, and GI, which we will now briefly review. Part of research is building links between existing work, and the aim of this paper is to bring together these currently separate approaches
Type: Conference Paper
Status: Post-print (author final draft post-refereeing)
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 by ACM. The original publication is available at: http://dl.acm.org/citation.cfm?id=2931728&CFID=823928677&CFTOKEN=80769513
URI: http://hdl.handle.net/1893/23394
URL: http://dl.acm.org/citation.cfm?id=2931728&CFID=823928677&CFTOKEN=80769513
Affiliation: Computing Science and Mathematics
University of Kent
Computing Science - CSM Dept

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