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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Salgueiro, R., de Almeida, A. & Oliveira, O. (2017). New genetic algorithm approach for the min-degree constrained minimum spanning tree. European Journal of Operational Research . 258 (3), 877-886
Exportar Referência (IEEE) 
R. P. Salgueiro et al.,  "New genetic algorithm approach for the min-degree constrained minimum spanning tree", in European Journal of Operational Research , vol. 258, no. 3, pp. 877-886, 2017
Exportar BibTeX 
@article{salgueiro2017_1775746468388,
	author = "Salgueiro, R. and de Almeida, A. and Oliveira, O.",
	title = "New genetic algorithm approach for the min-degree constrained minimum spanning tree",
	journal = "European Journal of Operational Research ",
	year = "2017",
	volume = "258",
	number = "3",
	doi = "10.1016/j.ejor.2016.11.007",
	pages = "877-886",
	url = "http://www.sciencedirect.com/science/article/pii/S0377221716309134"
}
Exportar RIS 
TY  - JOUR
TI  - New genetic algorithm approach for the min-degree constrained minimum spanning tree
T2  - European Journal of Operational Research 
VL  - 258
IS  - 3
AU  - Salgueiro, R.
AU  - de Almeida, A.
AU  - Oliveira, O.
PY  - 2017
SP  - 877-886
SN  - 0377-2217
DO  - 10.1016/j.ejor.2016.11.007
UR  - http://www.sciencedirect.com/science/article/pii/S0377221716309134
AB  - A novel approach is proposed for the NP-hard min-degree constrained minimum spanning tree (md-MST). The NP-hardness of the md-MST demands that heuristic approximations are used to tackle its intractability and thus an original genetic algorithm strategy is described using an improvement of the Martins-Souza heuristic to obtain a md-MST feasible solution, which is also presented. The genetic approach combines the latter improvement with three new approximations based on different chromosome representations for trees that employ diverse crossover operators. The genetic versions compare very favourably with the best known results in terms of both the run time and obtaining better quality solutions. In particular, new lower bounds are established for instances with higher dimensions.
ER  -