Training neural networks to sort: a new approach to classical algorithms

Abstract

The paper explores a new approach to data sorting based on the use of neural networks. Traditional sorting algorithms work according to rigidly defined rules, while neural networks learn to find patterns in data, which allows them to adapt to complex and unstructured arrays. It analyzes how machine learning models can choose optimal algorithms for specific data or perform sorting independently based on experience. The advantages of such an approach are discussed, including adaptability, potential optimization, and the possibility of parallel processing. The limitations associated with computational complexity and accuracy are also considered. The conclusions indicate that neural network sorting will not replace classical methods, but opens up new prospects for solving non-standard data sorting tasks.