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http://hdl.handle.net/1942/36205
Title: | A survey on multi-objective hyperparameter optimization algorithms for Machine Learning | Authors: | MORALES HERNANDEZ, Alejandro VAN NIEUWENHUYSE, Inneke ROJAS GONZALEZ, Sebastian |
Issue Date: | 2021 | Abstract: | Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared which focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures, and present future research directions. | Keywords: | hyperparameter optimization;multi-objective optimization;metamodel;meta-heuristic;machine learning | Document URI: | http://hdl.handle.net/1942/36205 | Category: | O | Type: | Preprint |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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Literature_review_AIR_Journal.pdf | Non Peer-reviewed author version | 589.67 kB | Adobe PDF | View/Open |
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