dc.contributor |
Juan, Ángel A., |
dc.contributor.author |
Calvet Liñan, Laura |
dc.contributor.author |
Serrat, Carles |
dc.contributor.author |
Ries, Jana |
dc.date |
2016 |
dc.identifier |
https://ddd.uab.cat/record/158313 |
dc.identifier |
urn:oai:ddd.uab.cat:158313 |
dc.identifier |
urn:oai:raco.cat:article/310078 |
dc.identifier |
urn:articleid:20138830v40n1p201 |
dc.format |
application/pdf |
dc.language |
eng |
dc.publisher |
|
dc.relation |
; |
dc.relation |
SORT : statistics and operations research transactions ; Vol. 40 Núm. 1 (January-June 2016), p. 201-224 |
dc.rights |
open access |
dc.rights |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. |
dc.rights |
https://creativecommons.org/licenses/by-nc-nd/3.0/ |
dc.subject |
Parameter fine-tuning |
dc.subject |
Metaheuristics |
dc.subject |
Statistical learning |
dc.subject |
Biased randomization |
dc.title |
A statistical learning based approach for parameter fine-tuning of metaheuristics |
dc.type |
Article |
dc.description.abstract |
Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem. |