dc.contributor |
Juan, Ángel A., |
dc.contributor.author |
Ionescu, Dragos |
dc.contributor.author |
Faulin, Javier |
dc.contributor.author |
Ferrer i Biosca, Albert |
dc.contributor.author |
Universitat Autònoma de Barcelona. Centre de Recerca Matemàtica |
dc.date |
2010 |
dc.identifier |
https://ddd.uab.cat/record/76057 |
dc.identifier |
urn:oai:ddd.uab.cat:76057 |
dc.format |
application/pdf |
dc.language |
eng |
dc.publisher |
Centre de Recerca Matemàtica |
dc.relation |
Centre de Recerca Matemàtica. Prepublicacions ; |
dc.rights |
open access |
dc.rights |
Aquest document està subjecte a una llicència d'ús de Creative Commons, amb la qual es permet copiar, distribuir i comunicar públicament l'obra sempre que se'n citin l'autor original, la universitat i el centre i no se'n faci cap ús comercial ni obra derivada, tal com queda estipulat en la llicència d'ús |
dc.rights |
https://creativecommons.org/licenses/by-nc-nd/2.5/ |
dc.subject |
Optimització combinatòria |
dc.title |
A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics |
dc.type |
Article |
dc.type |
Prepublicació |
dc.description.abstract |
This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex con guration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases. |