Title: Reinventing Classical Sorting with Deep Learning and Reinforcement Techniques
Authors: Nesreen S. Aljerjawi and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 126-133
Publication Date: 2025/08/28
Abstract:
Classical sorting algorithms, foundational to computer science, are often static and designed for theoretical optimality under specific conditions. However, the burgeoning complexity and dynamic nature of modern datasets present significant challenges to their efficiency and adaptability. This research explores a novel paradigm for sorting: reinventing classical algorithms through the integration of Deep Learning (DL) and Reinforcement Learning (RL) techniques. We hypothesize that AI-driven agents can learn optimal sorting strategies, adapt to diverse data distributions, and dynamically optimize performance in real-time, surpassing the limitations of traditional, hand-tuned heuristics. This paper will review the theoretical underpinnings and practical limitations of classical sorting, delve into the relevant advancements in deep learning and reinforcement learning for combinatorial optimization, and propose a methodology for designing and evaluating DL/RL-enhanced sorting frameworks. Expected outcomes include a demonstration of improved sorting efficiency, adaptability to unseen data patterns, and a reduction in computational resource consumption for large-scale and complex datasets, paving the way for more intelligent and robust data processing solutions.