论文标题
更好的调用替代物：用于超参数优化的混合进化算法
Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization
论文作者
论文摘要
在本文中，我们提出了一种替代辅助进化算法（EA），用于机器学习（ML）模型的超参数优化。拟议的稳定模型最初使用radialbasis函数插值估算目标函数格局，然后将知识转移到一种称为差异进化的EA技术，该技术用于进化以贝叶斯优化框架为指导的新解决方案。我们从经验上对高参数优化问题进行了经验评估，这是2020年Neurips Black Box优化挑战的一部分，并证明了Steade对Vanilla EA带来的改进。
In this paper, we propose a surrogate-assisted evolutionary algorithm (EA) for hyperparameter optimization of machine learning (ML) models. The proposed STEADE model initially estimates the objective function landscape using RadialBasis Function interpolation, and then transfers the knowledge to an EA technique called Differential Evolution that is used to evolve new solutions guided by a Bayesian optimization framework. We empirically evaluate our model on the hyperparameter optimization problems as a part of the black box optimization challenge at NeurIPS 2020 and demonstrate the improvement brought about by STEADE over the vanilla EA.
