An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex
Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics for solving a given computationally hard problem. I...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Online Access: | https://eprints.nottingham.ac.uk/34399/ |
| _version_ | 1848794843954806784 |
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| author | Asta, Shahriar Özcan, Ender |
| author_facet | Asta, Shahriar Özcan, Ender |
| author_sort | Asta, Shahriar |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics for solving a given computationally hard problem. In this study, we investigate into a novel apprenticeship learning-based approach which is used to automatically generate a hyper-heuristic for vehicle routing. This approach itself can be considered as a hyper-heuristic which operates in a train and test fashion. A state-of-the-art hyper-heuristic is chosen as an expert which is the winner of a previous hyper-heuristic competition. Trained on small vehicle routing instances, the learning approach yields various classifiers, each capturing different actions that the expert hyper-heuristic performs during the search process. Those classifiers are then used to produce a hyper-heuristic which is potentially capable of generalizing the actions of the expert hyperheuristic while solving the unseen instances. The experimental results on vehicle routing using the Hyper-heuristic Flexible (HyFlex) framework shows that the apprenticeship-learning based hyper-heuristic delivers an outstanding performance when compared to the expert and some other previously proposed hyper-heuristics. |
| first_indexed | 2025-11-14T19:22:38Z |
| format | Conference or Workshop Item |
| id | nottingham-34399 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:22:38Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-343992020-05-04T16:58:58Z https://eprints.nottingham.ac.uk/34399/ An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex Asta, Shahriar Özcan, Ender Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics for solving a given computationally hard problem. In this study, we investigate into a novel apprenticeship learning-based approach which is used to automatically generate a hyper-heuristic for vehicle routing. This approach itself can be considered as a hyper-heuristic which operates in a train and test fashion. A state-of-the-art hyper-heuristic is chosen as an expert which is the winner of a previous hyper-heuristic competition. Trained on small vehicle routing instances, the learning approach yields various classifiers, each capturing different actions that the expert hyper-heuristic performs during the search process. Those classifiers are then used to produce a hyper-heuristic which is potentially capable of generalizing the actions of the expert hyperheuristic while solving the unseen instances. The experimental results on vehicle routing using the Hyper-heuristic Flexible (HyFlex) framework shows that the apprenticeship-learning based hyper-heuristic delivers an outstanding performance when compared to the expert and some other previously proposed hyper-heuristics. 2014-12-09 Conference or Workshop Item PeerReviewed Asta, Shahriar and Özcan, Ender (2014) An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 9-12 Dec 2014, Orlando, Florida. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7009505 10.1109/EALS.2014.7009505 10.1109/EALS.2014.7009505 10.1109/EALS.2014.7009505 |
| spellingShingle | Asta, Shahriar Özcan, Ender An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex |
| title | An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex |
| title_full | An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex |
| title_fullStr | An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex |
| title_full_unstemmed | An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex |
| title_short | An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex |
| title_sort | apprenticeship learning hyper-heuristic for vehicle routing in hyflex |
| url | https://eprints.nottingham.ac.uk/34399/ https://eprints.nottingham.ac.uk/34399/ https://eprints.nottingham.ac.uk/34399/ |