We present a new adaptive sorting algorithm which is optimal for most disorder metrics and, more important, has a simple and quick implementation. On input X, our algorithm has a theoretical Ω(∣X∣) lower bound and a O(∣X∣log∣X∣) upper bound, exhibiting amazing adaptive properties which makes it run closer to its lower bound as disorder (computed on different metrics) diminishes. From a practical point of view, \textit{NeatSort} has proven itself competitive with (and often better than) \textit{qsort} and any \textit{Random Quicksort} implementation, even on random arrays.
@article{arxiv.1407.6183,
title = {NeatSort - A practical adaptive algorithm},
author = {Marcello La Rocca and Domenico Cantone},
journal= {arXiv preprint arXiv:1407.6183},
year = {2014}
}