Hyper-heuristics: a survey of the state of the art
By Edmund K. Burke, Michel Gendreau, Matthew R. Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan, Rong Qu. 2013
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Swagatam Das
- Seyedali Mirjalili
- Eneko Osaba
- Javier Del Ser
- Xin-She Yang
- Ponnuthurai Nagaratnam Suganthan
- Pinar Çivicioglu
- Seyed Mohammad Mirjalili
Citations in the corpus
- Mathematical discoveries from program search with large language models (2023, background): “In practice, these heuristics are often programs discovered through genetic programming, typically by evolving a heuristic on a set of instances of a given combinatorial optimization problem, such as bin packing 81 .”
- Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model (2024, methodology): “Automatic heuristic algorithm design is commonly known as hyper-heuristics (Burke et al., 2013; 2019; St¨utzle & L´opez-Ib´a˜nez, 2019).”
- Bio-inspired computation: Where we stand and what's next (2019, background): “ods or learning procedures for selecting or generating heuristics for a given optimization problem” [257].”
- Automated Algorithm Selection: Survey and Perspectives (2018, methodology): “Another approach for online algorithm selection and the selection of an appropriate algorithm for a given problem instance is provided by so-called hyper-heuristics; these are algorithms for selecting or generating solvers for a given problem from a set of given heuristic components (Burke et al., 2”
- Automated Design of Production Scheduling Heuristics: A Review (2016, background): “cross-fertilization also with other hyper-heuristic concepts, such as hyper-heuristics for heuristic selection [3].”
- Recent advances in selection hyper-heuristics (2020, background): “The relevant studies covered in this paper include only the full papers that appeared after the survey of Burke et al. (2013).”
- A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, background): “Hyper-heuristics [92] is a high level methodology for automatic selection and/or generation of heuristics for solving”