A survey on multi-objective hyperparameter optimization algorithms for Machine Learning

• URL: http://arxiv.org/abs/2111.13755v1
• Date: Tue, 23 Nov 2021 10:22:30 GMT
• Title: A survey on multi-objective hyperparameter optimization algorithms for Machine Learning
• Authors: Alejandro Morales-Hern\'andez and Inneke Van Nieuwenhuyse and Sebastian Rojas Gonzalez
• Abstract summary: This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms. We distinguish 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.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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