Artificial Neural Network Hyperparameters Optimization: A Survey


  • Zahraa Saddi Kadhim university of technology
  • Hasanen S. Abdullah University of technology
  • Khalil Ibrahim Ghathwan University of technology



Hyperparameter Optimization, artificial neural network, Deep learning, machine learning


Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics. Neural Networks (NNs) are one of the most es-sential ways to ML; the most challenging element of designing a NN is de-termining which hyperparameters to employ to generate the optimal model, in which hyperparameter optimization improves NN performance. This study includes a brief explanation regarding a few types of NN as well as some methods for hyperparameter optimization, as well as previous work results in enhancing ANN performance using optimization methods that aid research-ers and data analysts in developing better ML models via identifying the ap-propriate hyperparameter configurations.

Author Biography

Zahraa Saddi Kadhim, university of technology

she obtained her B.Sc. in computer science from University of Technology, Iraq in 2015. Currently studying computer science. she worked as a trainer for a non-governmental group teaching young people how to code robots in 2017-2019.her interested is mobile application, Artificial Intelligence, Machine Learning, Pattern Recognition, Data encryption and robotic(email:




How to Cite

Kadhim, Z. S., Abdullah, H. S., & Ghathwan, K. I. (2022). Artificial Neural Network Hyperparameters Optimization: A Survey. International Journal of Online and Biomedical Engineering (iJOE), 18(15), pp. 59–87.