HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization
Leila Zahedi, Farid Ghareh Mohammadi, M. Hadi Amini

TL;DR
HyP-ABC introduces a hybrid evolutionary algorithm for hyper-parameter tuning that improves efficiency and reduces parameter tuning complexity, demonstrated on multiple ML models with real-world data.
Contribution
The paper presents HyP-ABC, a novel hybrid optimization algorithm based on modified artificial bee colony, specifically designed for efficient hyper-parameter tuning in machine learning.
Findings
HyP-ABC outperforms existing methods in efficiency.
It requires fewer parameters to tune.
It achieves robust performance on real-world data.
Abstract
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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Machine Learning and Data Classification · Advanced Multi-Objective Optimization Algorithms
