# HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

arXiv: https://arxiv.org/abs/2109.05319 · HTML: https://mlsanity.com/paper/2109.05319/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 57 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Automated Machine Learning: From Principles to Practices](https://mlsanity.com/paper/1810.13306/) (2018)
- [Hyperparameter Search in Machine Learning](https://mlsanity.com/paper/1502.02127/) (2015)
- [Search Algorithms for Automated Hyper-Parameter Tuning](https://mlsanity.com/paper/2104.14677/) (2021)
- [On Parameter Tuning in Meta-learning for Computer Vision](https://mlsanity.com/paper/2003.00837/) (2020)
- [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)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.