IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
This addresses efficiency concerns for researchers and practitioners in machine learning, though it is incremental as it builds on traditional evolutionary algorithms.
The paper tackles the problem of time-consuming hyperparameter optimization in machine learning by introducing an intelligent evolutionary optimization algorithm that uses machine learning to accelerate the process, achieving an average speedup of 30.40% and up to 77.06% in best cases.
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.