论文标题
自动优化器的自适应优化器
Adaptive Optimizer for Automated Hyperparameter Optimization Problem
论文作者
论文摘要
超参数的选择对机器学习模型的性能有关键影响。在本文中，我们提出了一个能够构建自适应优化器的通用框架，该框架在优化过程中自动调整适当的算法和参数。研究自适应优化器的方法，我们为使用遗传算法构建基于贝叶斯优化器的自适应优化器的示例，并将其与原始优化器进行了比较。特别是，它在并行优化方面具有很大的优势。
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.
