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A survey on multi-objective hyperparameter optimization algorithms for machine learning
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DOI：10.1007/s10462-022-10359-2.png)
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Abstract
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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 that 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
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13.9
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6.1K
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1.9W
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