@article { , title = {Recent advances in selection hyper-heuristics}, abstract = {Hyper-heuristics have emerged as a way to raise the level of generality of search techniques for computational search problems. This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class of problem instances. The current state-of-the-art in hyper-heuristic research comprises a set of methods that are broadly concerned with intelligently selecting or generating a suitable heuristic in a given situation. Hyper-heuristics can be considered as search methods that operate on lower-level heuristics or heuristic components, and can be categorised into two main classes: heuristic selection and heuristic generation. The term hyper-heuristic was defined in the early 2000s as a heuristic to choose heuristics, but the idea of designing high-level heuristic methodologies can be traced back to the early 1960s. This paper gives a brief history of this emerging area, reviews contemporary hyper-heuristic literature, and discusses recent hyper-heuristic frameworks. In addition, the existing classification of selection hyper-heuristics is extended, in order to reflect the nature of the challenges faced in contemporary research. Unlike the survey on hyper-heuristics published in 2013, this paper focuses only on selection hyper-heuristics and presents critical discussion, current research trends and directions for future research.}, doi = {10.1016/j.ejor.2019.07.073}, issn = {0377-2217}, issue = {2}, journal = {European Journal of Operational Research}, pages = {405-428}, publicationstatus = {Published}, publisher = {Elsevier}, url = {https://nottingham-repository.worktribe.com/output/2452757}, volume = {285}, keyword = {Management Science and Operations Research, Modelling and Simulation, Information Systems and Management}, year = {2020}, author = {Drake, John H. and Kheiri, Ahmed and Özcan, Ender and Burke, Edmund K.} }