IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning

• URL: http://arxiv.org/abs/2009.06390v1
• Date: Thu, 10 Sep 2020 18:47:04 GMT
• Title: IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
• Authors: Yuxi Huan, Fan Wu, Michail Basios, Leslie Kanthan, Lingbo Li, Baowen Xu
• Abstract summary: We introduce an intelligent evolutionary optimisation algorithm which applies machine learning technique to the traditional evolutionary algorithm. Our approach accelerates the optimisation speed by 30.40% on average and up to 77.06% in the best scenarios.
• Score: 9.082096472600751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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