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

Song, Heda; Triguero, Isaac; Özcan, Ender

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: May 14, 2019
• 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
• Contract Date: May 14, 2019