A survey on multi-objective hyperparameter optimization algorithms for Machine Learning
This is an incremental survey that organizes existing literature for researchers and practitioners in machine learning.
The paper surveys multi-objective hyperparameter optimization algorithms for machine learning from 2014 to 2020, categorizing them into metaheuristic-based, metamodel-based, and hybrid approaches, and discusses quality metrics and future research directions.
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.