# Search Algorithms for Automated Hyper-Parameter Tuning

arXiv: https://arxiv.org/abs/2104.14677 · HTML: https://mlsanity.com/paper/2104.14677/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Automated Machine Learning: State-of-The-Art and Open Challenges](https://mlsanity.com/paper/1906.02287/) (2019)
- [Easy Hyperparameter Search Using Optunity](https://mlsanity.com/paper/1412.1114/) (2014)
- [On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them](https://mlsanity.com/paper/1909.02523/) (2019)
- [On Parameter Tuning in Meta-learning for Computer Vision](https://mlsanity.com/paper/2003.00837/) (2020)
- [An effective algorithm for hyperparameter optimization of neural networks](https://mlsanity.com/paper/1705.08520/) (2017)
- [Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics](https://mlsanity.com/paper/1908.08563/) (2019)
- [Evolutionary Computation, Optimization and Learning Algorithms for Data Science](https://mlsanity.com/paper/1908.08006/) (2019)
- [Automated Architecture Design for Deep Neural Networks](https://mlsanity.com/paper/1908.10714/) (2019)

## Built on by (most cited first, 8 of 8)

- [HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization](https://mlsanity.com/paper/2109.05319/) (2021)
- [Detecting Severity of Diabetic Retinopathy from Fundus Images: A Transformer Network-based Review](https://mlsanity.com/paper/2301.00973/) (2023)
- [Confidence Interval Estimation of Predictive Performance in the Context of AutoML](https://mlsanity.com/paper/2406.08099/) (2024)
- [Demystifying Visual Features of Movie Posters for Multi-Label Genre Identification](https://mlsanity.com/paper/2309.12022/) (2023)
- [OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms](https://mlsanity.com/paper/2112.08511/) (2021)
- [The application of Evolutionary and Nature Inspired Algorithms in Data Science and Data Analytics](https://mlsanity.com/paper/2202.03859/) (2022)
- [Unsupervised anomaly detection in large-scale estuarine acoustic telemetry data](https://mlsanity.com/paper/2502.01543/) (2025)
- [Data-driven Insights for Informed Decision-Making: Applying LSTM Networks for Robust Electricity Forecasting in Libya](https://mlsanity.com/paper/2507.01034/) (2025)

## Abstract (arXiv)

Machine learning is a powerful method for modeling in different fields such as education. Its capability to accurately predict students' success makes it an ideal tool for decision-making tasks related to higher education. The accuracy of machine learning models depends on selecting the proper hyper-parameters. However, it is not an easy task because it requires time and expertise to tune the hyper-parameters to fit the machine learning model. In this paper, we examine the effectiveness of automated hyper-parameter tuning techniques to the realm of students' success. Therefore, we develop two automated Hyper-Parameter Optimization methods, namely grid search and random search, to assess and improve a previous study's performance. The experiment results show that applying random search and grid search on machine learning algorithms improves accuracy. We empirically show automated methods' superiority on real-world educational data (MIDFIELD) for tuning HPs of conventional machine learning classifiers. This work emphasizes the effectiveness of automated hyper-parameter optimization while applying machine learning in the education field to aid faculties, directors', or non-expert users' decisions to improve students' success.