Title: Intelligent Optimization of Classical Sorting Algorithms Through Deep Learning and Reinforcement Learning
Authors: Mohammed Almadani , Samy S. Abu-Naser
Volume: 10
Issue: 7
Pages: 74-78
Publication Date: 2026/07/28
Abstract:
Sorting is one of the most fundamental and most studied operations in computer science. Classical comparison-based algorithms such as Quicksort, Merge Sort, and Heap Sort have been refined for decades until they approach their theoretical limits, and further human improvement has become very difficult. Recent advances in artificial intelligence have reopened the question of whether machines can rethink sorting itself. This paper surveys two complementary directions in which deep learning and reinforcement learning are reinventing classical sorting. The first direction, learning to sort, replaces the discrete sorting operator with differentiable approximations - such as Pointer Networks and NeuralSort - so that ordering can be embedded directly inside trainable neural pipelines. The second direction, learning to discover, uses deep reinforcement learning to search the space of low-level programs and uncover faster correct algorithms, exemplified by DeepMind's AlphaDev, whose discovered routines were added to the LLVM C++ standard sorting library. We compare the methodology, results, strengths, and limitations of both directions. We conclude that AI does not replace classical sorting but extends it: enabling end-to-end learning on one side and superhuman low-level optimization on the other.