|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Unrefereed|
|Title:||Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming|
|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.|
|Conference Name:||GECCO 2016: Genetic and Evolutionary Computation Conference|
|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|
|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|
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