Learning heuristic selection using a time delay neural network for open vehicle routing
A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heuristic for Open Vehicle Routing Problem (OVRP). As...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/41373/ |
| _version_ | 1848796260503388160 |
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| author | Tyasnurita, Raras Özcan, Ender John, Robert |
| author_facet | Tyasnurita, Raras Özcan, Ender John, Robert |
| author_sort | Tyasnurita, Raras |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heuristic for Open Vehicle Routing Problem (OVRP). As a chosen ‘expert’ hyper-heuristic is run on a small set of training problem instances, data is collected to learn from the expert regarding how to decide which low-level heuristic to select and apply to the solution in hand during the search process. In this study, a Time Delay Neural Network (TDNN) is used to extract hidden patterns within the collected data in the form of a classifier ,i.e an ‘apprentice’ hyper-heuristic, which is then used to solve the ‘unseen’ problem instances. Firstly, the parameters of TDNN are tuned using Taguchi orthogonal array as a design of experiments method. Then the influence of extending and enriching the information collected from the expert and fed into TDNN is explored on the behaviour of the generated apprentice hyper-heuristic. The empirical results show that the use of distance between solutions as an additional information collected from the expert generates an apprentice which outperforms the expert algorithm on a benchmark of OVRP instances. |
| first_indexed | 2025-11-14T19:45:09Z |
| format | Conference or Workshop Item |
| id | nottingham-41373 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:45:09Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-413732020-05-04T18:49:11Z https://eprints.nottingham.ac.uk/41373/ Learning heuristic selection using a time delay neural network for open vehicle routing Tyasnurita, Raras Özcan, Ender John, Robert A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heuristic for Open Vehicle Routing Problem (OVRP). As a chosen ‘expert’ hyper-heuristic is run on a small set of training problem instances, data is collected to learn from the expert regarding how to decide which low-level heuristic to select and apply to the solution in hand during the search process. In this study, a Time Delay Neural Network (TDNN) is used to extract hidden patterns within the collected data in the form of a classifier ,i.e an ‘apprentice’ hyper-heuristic, which is then used to solve the ‘unseen’ problem instances. Firstly, the parameters of TDNN are tuned using Taguchi orthogonal array as a design of experiments method. Then the influence of extending and enriching the information collected from the expert and fed into TDNN is explored on the behaviour of the generated apprentice hyper-heuristic. The empirical results show that the use of distance between solutions as an additional information collected from the expert generates an apprentice which outperforms the expert algorithm on a benchmark of OVRP instances. 2017-06-06 Conference or Workshop Item PeerReviewed Tyasnurita, Raras, Özcan, Ender and John, Robert (2017) Learning heuristic selection using a time delay neural network for open vehicle routing. In: IEEE Congress on Evolutionary Computation 2017, 5-9 June 2017, Donostia-San Sebastian, Spain. http://ieeexplore.ieee.org/document/7969477/ |
| spellingShingle | Tyasnurita, Raras Özcan, Ender John, Robert Learning heuristic selection using a time delay neural network for open vehicle routing |
| title | Learning heuristic selection using a time delay neural network for open vehicle routing |
| title_full | Learning heuristic selection using a time delay neural network for open vehicle routing |
| title_fullStr | Learning heuristic selection using a time delay neural network for open vehicle routing |
| title_full_unstemmed | Learning heuristic selection using a time delay neural network for open vehicle routing |
| title_short | Learning heuristic selection using a time delay neural network for open vehicle routing |
| title_sort | learning heuristic selection using a time delay neural network for open vehicle routing |
| url | https://eprints.nottingham.ac.uk/41373/ https://eprints.nottingham.ac.uk/41373/ |