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IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2020-09-10 , DOI: arxiv-2009.06390
Yuxi Huan, Fan Wu, Michail Basios, Leslie Kanthan, Lingbo Li, Baowen Xu

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.

中文翻译:

IEO:超参数调整的智能进化优化

超参数优化是搜索最优机器学习模型的关键过程。寻找最佳超参数设置的效率一直是最近研究中的一个大问题,因为优化过程可能非常耗时,尤其是当目标函数的评估成本非常高时。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进化算法,以加速在分类问题中调整机器学习模型的整体优化过程。我们在一系列受控实验中展示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进行了比较。
更新日期:2020-09-15
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