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Towards optimisers that `Keep Learning'

Hart, Emma; Miguel, Ian; Stone, Christopher; Renau, Quentin

Authors

Ian Miguel

Christopher Stone



Abstract

We consider optimisation in the context of the need to apply an optimiser to a continual stream of instances from one or more domains, and consider how such a system might 'keep learning': by drawing on past experience to improve performance and learning how to both predict and react to instance and/or domain drift.

Citation

Hart, E., Miguel, I., Stone, C., & Renau, Q. (2023, July). Towards optimisers that `Keep Learning'. Presented at Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal

Presentation Conference Type Conference Paper (published)
Conference Name Companion Conference on Genetic and Evolutionary Computation
Start Date Jul 15, 2023
End Date Jul 19, 2023
Online Publication Date Jul 24, 2023
Publication Date 2023-07
Deposit Date Aug 1, 2023
Publisher Association for Computing Machinery (ACM)
Pages 1636-1638
Book Title GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
DOI https://doi.org/10.1145/3583133.3596344
Keywords Optimisation, continual learning, transfer-learning
Public URL http://researchrepository.napier.ac.uk/Output/3156082


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