论文标题
IEO：超参数调整的智能进化优化
IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
论文作者
论文摘要
超参数优化是搜索最佳机器学习模型的关键过程。在最近的研究中，找到最佳的高参数设置的效率一直是一个很大的关注点，因为优化过程可能会耗时，尤其是当目标函数非常昂贵时。在本文中，我们引入了一种智能进化优化算法，该算法将机器学习技术应用于传统的进化算法，以加速分类问题中调谐机器学习模型的整体优化过程。我们在一系列受控的实验中证明了我们的智能进化优化（IEO），与超参数调整中的传统进化优化相比。实证研究表明，在最佳情况下，我们的方法平均加速了优化速度30.40％，高达77.06％。
Hyperparameter optimisation is a crucial process in searching the optimal machine learning model. The efficiency of finding the optimal hyperparameter settings has been a big concern in recent researches since the optimisation process could be time-consuming, especially when the objective functions are highly expensive to evaluate. In this paper, we introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm to accelerate the overall optimisation process of tuning machine learning models in classification problems. We demonstrate our Intelligent Evolutionary Optimisation (IEO)in a series of controlled experiments, comparing with traditional evolutionary optimisation in hyperparameter tuning. The empirical study shows that our approach accelerates the optimisation speed by 30.40% on average and up to 77.06% in the best scenarios.
