Automated Algorithm Configuration and Parameter Tuning

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AutoML: A Survey of the State-of-the-Art (2019, methodology): “Grid search [107, 108, 109] is one of the most popular HPO methods, as it is simple to implement in parallel and typically finds hyperparameters that are as good as or better than those revealed by manual search, and in the”
Towards Integrative Machine Learning and Knowledge Extraction (2017, background): “Similarly, [61] addresses the problem of skin cancer detection using deep neural networks.”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, methodology): “Based on the parameter types classification in existing work [11], [38]–[40], parameters can be mainly classified into categorial and numerical parameters, according to their searchability [41], [42].”
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon (2018, background): “Algorithm conﬁguration, detailed in Hoos (2012); Bischl et al. (2016), is a well studied area that captures the setting presented here.”