Recent advances in selection hyper-heuristics
| Autor: | Edmund K. Burke, John H. Drake, Ender Özcan, Ahmed Kheiri |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Class (computer programming) 021103 operations research Information Systems and Management General Computer Science business.industry Heuristic Computer science 05 social sciences 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Machine learning computer.software_genre Industrial and Manufacturing Engineering Term (time) Modeling and Simulation Problem domain 0502 economics and business Artificial intelligence Heuristics business Set (psychology) computer Selection (genetic algorithm) |
| Zdroj: | European Journal of Operational Research. 285:405-428 |
| ISSN: | 0377-2217 |
| DOI: | 10.1016/j.ejor.2019.07.073 |
| Popis: | 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 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. 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. 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. Here we will focus on the first of these two categories, selection hyper-heuristics. This paper gives a brief history of this emerging area, reviews contemporary selection hyper-heuristic literature, and discusses recent selection 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. |
| Databáze: | OpenAIRE |
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