Shahriar Asta
An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex
Asta, Shahriar; �zcan, Ender
Authors
ENDER OZCAN ender.ozcan@nottingham.ac.uk
Professor of Computer Science and Operational Research
Abstract
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.
Citation
Asta, S., & Özcan, E. (2014). An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS). https://doi.org/10.1109/EALS.2014.7009505
Conference Name | 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) |
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End Date | Dec 12, 2014 |
Acceptance Date | Dec 9, 2014 |
Publication Date | Dec 9, 2014 |
Deposit Date | Jun 27, 2016 |
Publicly Available Date | Jun 27, 2016 |
Peer Reviewed | Peer Reviewed |
Book Title | 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) |
DOI | https://doi.org/10.1109/EALS.2014.7009505 |
Public URL | https://nottingham-repository.worktribe.com/output/741265 |
Publisher URL | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7009505 |
Contract Date | Jun 27, 2016 |
Files
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(204 Kb)
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
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