Skip to main content

Research Repository

Advanced Search

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

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

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


Profile Image

Professor of Computer Science and Operational Research

Daniel Karapetyan


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.

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 1611-3349
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
Keywords combinatorial optimization, metaheuristics, data science, machine learning
Public URL
Publisher URL
Additional Information In chapter: Learning and intelligent optimization.
ISBN 9783319190839.

The final publication is available at Springer via


You might also like

Downloadable Citations