An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex

Asta, Shahriar; �zcan, Ender

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)
• 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