Skip to main content

Research Repository

Advanced Search

An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex

Asta, Shahriar; �zcan, Ender

An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex Thumbnail


Authors

Shahriar Asta

Profile Image

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.

Conference Name 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)
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

Files





You might also like



Downloadable Citations