Title: An Intelligent Machine Learning Approach for Adaptive Sorting Algorithm Selection and Performance Optimization .
Authors: Lena Zaharna, Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 58-62
Publication Date: 2026/07/28
Abstract:
: Sorting algorithms are fundamental components in computer science and data processing systems. However, selecting the most efficient sorting algorithm depends on the characteristics of the input data, such as size, distribution, and order. Traditional approaches rely on static selection, which may lead to sub-optimal performance in dynamic environments. This research proposes an intelligent machine learning-based framework that dynamically selects the most appropriate sorting algorithm based on dataset features. The model is trained on performance metrics of multiple sorting algorithms under different data conditions to predict the optimal choice. Experimental results demonstrate that the proposed approach improves overall sorting efficiency in terms of time complexity and execution performance compared to static selection methods.