A software interface for supporting the application of data science to optimisation
Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value from it. Hyper-heuristics aim to get such value...
| Main Authors: | , , |
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| Format: | Article |
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Springer Verlag
2015
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| Online Access: | https://eprints.nottingham.ac.uk/33933/ |
| _version_ | 1848794737514905600 |
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| author | Parkes, Andrew J. Özcan, Ender Karapetyan, Daniel |
| author_facet | Parkes, Andrew J. Özcan, Ender Karapetyan, Daniel |
| author_sort | Parkes, Andrew J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value from it. Hyper-heuristics aim to get such value by using a specific API such as
`HyFlex' to cleanly separate the search control structure from the details of the domain. Here, we discuss various longer-term additions to the HyFlex interface that will allow much richer information exchange, and so enhance learning via data science techniques, but without losing domain independence of the search control. |
| first_indexed | 2025-11-14T19:20:57Z |
| format | Article |
| id | nottingham-33933 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:20:57Z |
| publishDate | 2015 |
| publisher | Springer Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-339332020-05-04T17:07:51Z https://eprints.nottingham.ac.uk/33933/ A software interface for supporting the application of data science to optimisation Parkes, Andrew J. Özcan, Ender Karapetyan, Daniel Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most value from it. Hyper-heuristics aim to get such value by using a specific API such as `HyFlex' to cleanly separate the search control structure from the details of the domain. Here, we discuss various longer-term additions to the HyFlex interface that will allow much richer information exchange, and so enhance learning via data science techniques, but without losing domain independence of the search control. Springer Verlag 2015-05-29 Article PeerReviewed Parkes, Andrew J., Özcan, Ender and Karapetyan, Daniel (2015) A software interface for supporting the application of data science to optimisation. Lecture Notes in Computer Science, 8994 . pp. 306-311. ISSN 0302-9743 combinatorial optimization metaheuristics data science machine learning http://link.springer.com/chapter/10.1007%2F978-3-319-19084-6_31 doi:10.1007/978-3-319-19084-6_31 doi:10.1007/978-3-319-19084-6_31 |
| spellingShingle | combinatorial optimization metaheuristics data science machine learning Parkes, Andrew J. Özcan, Ender Karapetyan, Daniel A software interface for supporting the application of data science to optimisation |
| title | A software interface for supporting the application of data science to optimisation |
| title_full | A software interface for supporting the application of data science to optimisation |
| title_fullStr | A software interface for supporting the application of data science to optimisation |
| title_full_unstemmed | A software interface for supporting the application of data science to optimisation |
| title_short | A software interface for supporting the application of data science to optimisation |
| title_sort | software interface for supporting the application of data science to optimisation |
| topic | combinatorial optimization metaheuristics data science machine learning |
| url | https://eprints.nottingham.ac.uk/33933/ https://eprints.nottingham.ac.uk/33933/ https://eprints.nottingham.ac.uk/33933/ |