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

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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

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/751054
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.
Keywords: combinatorial optimization, metaheuristics, data science, machine learning
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1007/978-3-319-19084-6_31
Depositing User: Ozcan, Dr Ender
Date Deposited: 13 Jun 2016 11:10
Last Modified: 04 May 2020 17:07
URI: https://eprints.nottingham.ac.uk/id/eprint/33933

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