A software interface for supporting the application of data science to optimisation

Parkes, Andrew J.; �zcan, Ender; Karapetyan, Daniel

Abstract

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.

Citation

Parkes, A. J., Özcan, E., & Karapetyan, D. (2015). A software interface for supporting the application of data science to optimisation. Lecture Notes in Artificial Intelligence, 8994, 306-311. https://doi.org/10.1007/978-3-319-19084-6_31

• Journal Article Type: Article
• Acceptance Date: Jan 15, 2015
• Publication Date: May 29, 2015
• Deposit Date: Jun 13, 2016
• Publicly Available Date: Jun 13, 2016
• Journal: Lecture Notes in Computer Science
• Electronic ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 8994
• Pages: 306-311
• Series Title: Lecture Notes in Computer Science
• Book Title: Learning and Intelligent Optimization
• DOI: https://doi.org/10.1007/978-3-319-19084-6_31
• Keywords: combinatorial optimization, metaheuristics, data science, machine learning
• Public URL: https://nottingham-repository.worktribe.com/output/751054
• Publisher URL: http://link.springer.com/chapter/10.1007%2F978-3-319-19084-6_31
• Additional Information: In chapter: Learning and intelligent optimization.; ISBN 9783319190839.; ; The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19084-6_31.