TY - GEN
T1 - Evolutionary-based Hyperparameter Tuning in Machine Learning Models for Condition Monitoring in Wind Turbines - A Survey
AU - Adedeji, Paul A.
AU - Olatunji, Obafemi O.
AU - Madushele, Nkosinathi
AU - Jen, Tien Chien
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/13
Y1 - 2021/5/13
N2 - Optimality of model hyperparameters is essential for intelligent condition monitoring (ICM) of wind turbines using machine learning models, hence the need for hyperparameter tuning. Evolutionary algorithms (EAs) have been used for hyperparameter tuning of machine learning models, however, little is known about the hyperparameter tuning of these EAs. This study presents a survey of hyperparameter tuning of EAs used for tuning hyperparameters of machine learning models that are used in ICM of wind turbines. Findings show that many studies tune hyperparameters for machine learning models, however, a few studies tune these hyperparameters with EAs. Among these few, a handful tune the hyperparameters of such EAs and such studies in ICM of wind turbines is very sparse. Hence the need to explore this double stage hyperparameter (DSHP) tuning in ICM of wind turbines.
AB - Optimality of model hyperparameters is essential for intelligent condition monitoring (ICM) of wind turbines using machine learning models, hence the need for hyperparameter tuning. Evolutionary algorithms (EAs) have been used for hyperparameter tuning of machine learning models, however, little is known about the hyperparameter tuning of these EAs. This study presents a survey of hyperparameter tuning of EAs used for tuning hyperparameters of machine learning models that are used in ICM of wind turbines. Findings show that many studies tune hyperparameters for machine learning models, however, a few studies tune these hyperparameters with EAs. Among these few, a handful tune the hyperparameters of such EAs and such studies in ICM of wind turbines is very sparse. Hence the need to explore this double stage hyperparameter (DSHP) tuning in ICM of wind turbines.
KW - Condition monitoring
KW - Evolutionary algorithm
KW - Hyperparameters
KW - Machine learning
KW - Wind turbine
UR - http://www.scopus.com/inward/record.url?scp=85111023130&partnerID=8YFLogxK
U2 - 10.1109/ICMIMT52186.2021.9476200
DO - 10.1109/ICMIMT52186.2021.9476200
M3 - Conference contribution
AN - SCOPUS:85111023130
T3 - Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021
SP - 254
EP - 258
BT - Proceedings of 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2021
Y2 - 13 May 2021 through 15 May 2021
ER -
