Hyper-Heuristics: An Emerging Direction in Modern Search Technology

By Edmund Kieran Burke, Graham Kendall, J. Newall, Emma Hart, P. Ross, Sonia Schulenburg. 2003

791 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering, Management Science and Operations Research

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Citations in the corpus

Neural Combinatorial Optimization with Reinforcement Learning (2016, background): “This challenge has fostered interest in raising the level of generality at which optimization systems operate (Burke et al., 2003) and is the underlying motivation behind hyper-heuristics, deﬁned as ”search method[s] or learning mechanism[s] for selecting or generating heuristics to solve…”
Computational models and heuristic methods for Grid scheduling problems (2010, neutral): “Hyper-heuristics [60] are methods that guide the search, at a higher level as compared to meta-heuristics.”
A Classification of Hyper-heuristic Approaches (2010, background): “The aim of this chapter is to provide an updated version to the hyper-heuristic chapter [6] in the 2003 edition of the Handbook of Metaheuristics.”
A generic framework for constrained optimization using genetic algorithms (2005, background): “The interested reader is referred to [2] and [3] for more reading.”
A graph-based hyper-heuristic for educational timetabling problems (2007, methodology): “on timetabling ( Burke, Kendall and Soubeiga, 2003, Gaw, Rattadilok and Kwan, 2004, Burke,”
A review on algorithms for maximum clique problems (2015, positive): “CLS (Pullan, Mascia, & Brunato, 2011) further improves over PLS by following the idea of the hyper-heuristic scheme (Burke, Hart, Kendall, Newall, Ross, & Schulenburg, 2003).”
Hybrid metaheuristics in combinatorial optimization: A survey (2011, background): “Research on so-called hyper-heuristics [47] has started with the idea of developing general algorithms that can potentially be applied to many related problems without much eﬀort of adaptation.”
A survey of search methodologies and automated system development for examination timetabling (2009, background): “a search space of heuristics is the focus of attention rather than a search space of solutions (as is the case with most implementations of meta-heuristics [29, 135]).”
A Tabu-Search Hyperheuristic for Timetabling and Rostering (2003, background): “We require solutions which are 'good enough soon enough -cheap enough' [5].”