Year: 2022
Type: preprint
Abstract: The choices of hyperparameters have critical effects on the performance of machine learning models. In this paper, we present a general framework that is able to construct an adaptive optimizer, which automatically adjust the appropriate algorithm and parameters in the process of optimization. Examining the method of adaptive optimizer, we product an example of using genetic algorithm to construct an adaptive optimizer based on Bayesian Optimizer and compared effectiveness with original optimizer. Especially, It has great advantages in parallel optimization.
Source: arXiv (Cornell University)
Author: Sun, Huayuan
Institution: Peking University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4221138839