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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 for a given situation. ... This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class of problem instances. ... Using both benchmark data-sets and real-world data, an approach using Variable Neighbourhood Descent with memory was shown to outperform a Choice Function variant, Tabu Search and Random Descent heuristic ...doi:10.1016/j.ejor.2019.07.073 fatcat:ojfs237tynhxbgtyiho6dagapi