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A review on the self and dual interactions between machine learning and optimisation

Song, Heda; Triguero, Isaac; Özcan, Ender

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Authors

Heda Song

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ENDER OZCAN ender.ozcan@nottingham.ac.uk
Professor of Computer Science and Operational Research



Abstract

Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from the latter search for the best option or solution to a given problem. To employ these techniques automatically and effectively aligning with the real aim of artificial intelligence, both sets of techniques are frequently hybridised, interacting with each other and themselves. This study focuses on such interactions aiming at (1) presenting a broad overview of the studies on self and dual interactions between machine learning and optimisation; (2) providing a useful tutorial for researchers and practitioners in both fields in support of collaborative work through investigation of the recent advances and analyses of the advantages and disadvantages of different techniques to tackle the same or similar problems; (3) clarifying the overlapping terminologies having different meanings used in both fields; (4) identifying research gaps and potential research directions.

Citation

Song, H., Triguero, I., & Özcan, E. (2019). A review on the self and dual interactions between machine learning and optimisation. Progress in Artificial Intelligence, 8(2), 143–165. https://doi.org/10.1007/s13748-019-00185-z

Journal Article Type Article
Acceptance Date Apr 16, 2019
Online Publication Date Apr 25, 2019
Publication Date 2019-06
Deposit Date May 1, 2019
Publicly Available Date Mar 29, 2024
Journal Progress in Artificial Intelligence
Electronic ISSN 2192-6360
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 8
Issue 2
Pages 143–165
DOI https://doi.org/10.1007/s13748-019-00185-z
Keywords Artificial Intelligence
Public URL https://nottingham-repository.worktribe.com/output/1857310
Publisher URL https://link.springer.com/article/10.1007%2Fs13748-019-00185-z
Additional Information Received: 20 December 2018; Accepted: 16 April 2019; First Online: 25 April 2019

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