@inproceedings{007c56009d6d47cb9581d0c80a074885,
title = "Descent with mutations applied to the linear ordering problem",
abstract = "We study here the application of the “descent with mutations” metaheuristic to the linear ordering problem. We compare this local search metaheuristic with another very efficient metaheuristic, obtained by the hybridization of a classic simulated annealing with some ingredients coming from the noising methods. The computational experiments on the linear ordering problem show that the descent with mutations provides results which are comparable to the ones given by this improved simulated annealing, or even better, while the descent with mutations is much easier to design and to tune, since there is no parameter to tune (except the CPU time that the user wants to spend to solve his or her problem).",
keywords = "Combinatorial optimization, Condorcet-Kemeny{\textquoteright}s problem, Linear ordering problem, Median order, Metaheuristics, Noising methods, Simulated annealing, Slater{\textquoteright}s problem",
author = "Olivier Hudry",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 5th International Symposium on Combinatorial Optimization, ISCO 2018 ; Conference date: 11-04-2018 Through 13-04-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-96151-4\_22",
language = "English",
isbn = "9783319961507",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "253--264",
editor = "Giovanni Rinaldi and Mahjoub, \{A. Ridha\} and Jon Lee",
booktitle = "Combinatorial Optimization - 5th International Symposium, ISCO 2018, Revised Selected Papers",
}