Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes
A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Researchers classify hyper-heuristics according to the source of feedback during learning: Online learning hyper-heuristics learn while solving a given instance o...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2018
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| Online Access: | https://eprints.nottingham.ac.uk/52348/ |
| _version_ | 1848798706109775872 |
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| author | Hong, Libin |
| author_facet | Hong, Libin |
| author_sort | Hong, Libin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Researchers classify hyper-heuristics according to the source of feedback during learning: Online learning hyper-heuristics learn while solving a given instance of a problem; Offline learning hyper-heuristics learn from a set of training instances, a method that can generalise to unseen instances.
Genetic programming (GP) can be considered a specialization of the more widely known genetic algorithms (GAs) where each individual is a computer program. GP automatically generates computer programs to solve specified tasks. It is a method of searching a space of computer programs. GP can be used as a kind of hyper-heuristic to be a learning algorithm when it uses some feedback from the search process. Our research mainly uses genetic programming as offline hyper-heuristic approach to automatically design various heuristics for evolutionary programming. |
| first_indexed | 2025-11-14T20:24:02Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-52348 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:24:02Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-523482025-02-28T14:10:07Z https://eprints.nottingham.ac.uk/52348/ Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes Hong, Libin A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Researchers classify hyper-heuristics according to the source of feedback during learning: Online learning hyper-heuristics learn while solving a given instance of a problem; Offline learning hyper-heuristics learn from a set of training instances, a method that can generalise to unseen instances. Genetic programming (GP) can be considered a specialization of the more widely known genetic algorithms (GAs) where each individual is a computer program. GP automatically generates computer programs to solve specified tasks. It is a method of searching a space of computer programs. GP can be used as a kind of hyper-heuristic to be a learning algorithm when it uses some feedback from the search process. Our research mainly uses genetic programming as offline hyper-heuristic approach to automatically design various heuristics for evolutionary programming. 2018-11 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/52348/1/THESIS_LATEST_12JUNE2018.pdf Hong, Libin (2018) Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes. PhD thesis, University of Nottingham. hyper-heuristic; evolutionary programming; |
| spellingShingle | hyper-heuristic; evolutionary programming; Hong, Libin Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes |
| title | Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes |
| title_full | Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes |
| title_fullStr | Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes |
| title_full_unstemmed | Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes |
| title_short | Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes |
| title_sort | hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes |
| topic | hyper-heuristic; evolutionary programming; |
| url | https://eprints.nottingham.ac.uk/52348/ |