A novel automated SuperLearner using a genetic algorithm-based hyperparameter optimization. (January 2023)

• Record Type: Journal Article
• Authors: Mohan, Balaji; Badra, Jihad
• Is Part Of: Advances in engineering software. Volume 175(2023)
• Publication Date: 2023-01
• Subjects: SuperLearner -- Bayesian optimization -- Genetic algorithm -- Automated machine learning; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103358
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A novel automated machine learning framework is introduced. Genetic algorithm-based hyperparameter optimization is used. The automated SuperLearner was benchmarked against six different datasets. The framework demonstrated higher performance with the lowest computational resources. 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.