  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.
