A novel automated SuperLearner using a genetic algorithm-based hyperparameter optimization

Abstract

Industrial revolution 4.0 has pushed industries worldwide to use machine learning (ML) models to address real-world engineering problems. The industry generally faces two main challenges in ML applications: the lack of skilled data scientists and the cost of obtaining large labeled datasets. These challenges need to be addressed to unlock the full potential of ML. In this work, a novel automated SuperLearner (SL) model using a genetic algorithm (AutoSL-GA) based hyperparameter optimization (HPO) is introduced to address the aforementioned challenges and assist scientists and engineers. Detailed comparisons are performed between AutoSL-GA, SuperLearner using Bayesian-based HPO (AutoSL-BO), and another well-known automated machine learning (AutoML) algorithm called Tree-based pipeline optimization tool (TPOT). Six different benchmark datasets were used to compare the performance and computational times of the models. AutoSL-GA resulted in higher performance with lower computational time than other models for all six benchmark datasets. Finally, a sensitivity analysis for dataset size was performed, in which AutoSL-GA also outperformed the other models across the dataset sizes while consuming the least computational resources.