Title: Improving Sorting Algorithms Using Artificial Intelligence: A Cross Tactic
Authors: Nesreen S. Aljerjawi and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 118-125
Publication Date: 2025/08/28
Abstract:
This research paper explores the transformative role of Artificial Intelligence (AI) in enhancing and optimizing traditional sorting algorithms. In an era characterized by an unprecedented volume and complexity of data, conventional sorting methods often encounter limitations in terms of efficiency, scalability, and adaptability. This paper investigates how the integration of AI techniques, including machine learning, deep learning, and reinforcement learning, can overcome these challenges, leading to the development of more intelligent and efficient sorting solutions. We delve into the methodologies behind AI-driven sorting, examining hybrid approaches that combine the strengths of classical algorithms with the adaptive capabilities of AI. Furthermore, the paper highlights recent breakthroughs, such as DeepMind's AlphaDev, which demonstrate AI's potential to discover novel and faster sorting routines. We discuss the practical applications of AI-enhanced sorting across various domains, from big data processing to financial fraud detection, and address the inherent challenges and future directions in this evolving field. The ultimate goal is to underscore the critical importance of AI in revolutionizing data processing and fostering more secure and efficient computational environments.