main.bib

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%% https://bibdesk.sourceforge.io/

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@webpage{cliqueCambridge,
	Date-Added = {2020-05-15 10:59:38 +0200},
	Date-Modified = {2020-05-15 11:00:23 +0200},
	Lastchecked = {15-05-2020},
	Url = {https://dictionary.cambridge.org/es/diccionario/ingles-espanol/clique}}

@electronic{vid-graf-ai,
	Author = {Neo4j Neo4j Neo4j Neo4j},
	Date-Added = {2020-03-18 17:27:51 +0100},
	Date-Modified = {2020-03-18 17:30:28 +0100},
	Keywords = {Nuevas Formas de Predecir el {\'E}xito de Inversiones Tableros Din{\'a}micos 360 de Clientes. Detecci{\'o}n de Fraude en Tiempo Real y Agilizando Procesos de AML},
	Lastchecked = {2020},
	Month = {12},
	Title = {Nuevas Formas de Predecir el {\'E}xito de Inversiones Tableros Din{\'a}micos 360 de Clientes. Detecci{\'o}n de Fraude en Tiempo Real y Agilizando Procesos de AML},
	Url = {https://www.youtube.com/watch?v=KUtEee0et-w},
	Year = {2019},
	Bdsk-Url-1 = {https://www.youtube.com/watch?v=KUtEee0et-w}}

@book{grafo-molecula,
	Author = {Foulds, L.R.},
	Date-Added = {2020-03-18 17:00:24 +0100},
	Date-Modified = {2020-03-18 17:01:58 +0100},
	Isbn = {9781461209331},
	Number = {7},
	Publisher = {Springer New York},
	Series = {Universitext},
	Title = {Graph Theory Applications},
	Url = {https://books.google.es/books?id=5G4QBwAAQBAJ},
	Year = {2012},
	Bdsk-Url-1 = {https://books.google.es/books?id=5G4QBwAAQBAJ}}

@webpage{api-graph,
	Author = {Facebook},
	Date-Added = {2020-03-18 16:35:29 +0100},
	Date-Modified = {2020-03-18 17:08:55 +0100},
	Keywords = {API Graph facebook},
	Lastchecked = {2020},
	Month = {3},
	Title = {API Graph},
	Url = {https://developers.facebook.com/docs/graph-api},
	Year = {2020},
	Bdsk-Url-1 = {https://developers.facebook.com/docs/graph-api}}

@article{mcp-compVision,
	Author = {Bomze I.M., Budinich M., Pardalos P.M., Pelillo M.},
	Date-Added = {2020-03-18 16:26:39 +0100},
	Date-Modified = {2020-03-18 16:28:49 +0100},
	Journal = {Pardalos P.M. (eds) Handbook of Combinatorial Optimization. Springer, Boston, MA},
	Title = {The Maximum Clique Problem},
	Year = {1999}}

@article{top-molec,
	Author = {Amig{\'o}, J. M. Villar Amig{\'o}, Vicente. Falc{\'o} Montesinos, Antonio.},
	Date-Added = {2020-03-18 16:04:31 +0100},
	Date-Modified = {2020-03-18 16:06:51 +0100},
	Journal = {Sociedad Espa{\~n}ola de Matem{\'a}tica Aplicada (SeMA)},
	Keywords = {Mathematics in natural history. Topolog{\'\i}a - Aplicaciones en mol{\'e}culas. Topology in molecules. Graph theory. Topolog{\'\i}a - Aplicaciones en bioqu{\'\i}mica. Topology in biochemistry. Matem{\'a}ticas - Aplicaciones en ciencias naturales. Grafos, Teor{\'\i}a de.},
	Month = {Abril},
	Number = {39},
	Pages = {135-149.},
	Title = {Topolog{\'\i}a molecular / J. M. Amig{\'o} ... [et al.].},
	Year = {2007}}

@inproceedings{mrcp-moeni,
	Abstract = {In this paper, we are interested in studying the maximum ratio clique problem (MRCP) that is a variant of the classical maximum weight clique problem. For a given graph, we suppose that each vertex of the graph is weighted by a pair of rational numbers. The objective of MRCP consists in finding a maximal clique with the largest ratio between two sets of weights that are assigned to its vertices. It has been proven that the decision version of this problem is NP-complete and it is hard to solve MRCP for large instances. Hence, this paper looks for introducing an efficient approach based on Difference of Convex functions (DC) programming and DC Algorithm (DCA) for solving MRCP. Then, we verify the performance of the proposed method. For this purpose, we compare the solutions of DCA with the previously published results. As a second objective of this paper, we identify some valid inequalities and evaluate empirically their influence in solving MRCP. According to the numerical experiments, DCA provides promising and competitive results. Furthermore, the introduction of the valid inequalities improves the computational time of the classical approaches.},
	Address = {Cham},
	Author = {Moeini, Mahdi},
	Booktitle = {Modelling, Computation and Optimization in Information Systems and Management Sciences},
	Date-Added = {2020-03-17 16:54:32 +0100},
	Date-Modified = {2020-03-17 16:54:53 +0100},
	Editor = {Le Thi, Hoai An and Pham Dinh, Tao and Nguyen, Ngoc Thanh},
	Isbn = {978-3-319-18161-5},
	Pages = {215--227},
	Publisher = {Springer International Publishing},
	Title = {The Maximum Ratio Clique Problem: A Continuous Optimization Approach and Some New Results},
	Year = {2015}}

@article{mrcp-Sethuraman:2015,
	Abstract = {This paper introduces a fractional version of the classical maximum weight clique problem, the maximum ratio clique problem, which is to find a maximal clique that has the largest ratio of benefit and cost weights associated with the clique's vertices. NP-completeness of the decision version of the problem is established, and three solution methods are proposed. The results of numerical experiments with standard graph instances, as well as with real-life instances arising in finance and energy systems, are reported.},
	Author = {Sethuraman, Samyukta and Butenko, Sergiy},
	Da = {2015/01/01},
	Date-Added = {2020-03-13 19:43:08 +0100},
	Date-Modified = {2020-03-13 19:43:30 +0100},
	Doi = {10.1007/s10287-013-0197-z},
	Id = {Sethuraman2015},
	Isbn = {1619-6988},
	Journal = {Computational Management Science},
	Number = {1},
	Pages = {197--218},
	Title = {The maximum ratio clique problem},
	Ty = {JOUR},
	Url = {https://doi.org/10.1007/s10287-013-0197-z},
	Volume = {12},
	Year = {2015},
	Bdsk-Url-1 = {https://doi.org/10.1007/s10287-013-0197-z}}

@article{LUCE:1949,
	Address = {United States},
	An = {18152948},
	Author = {LUCE, R D and PERRY, A D},
	Date = {1949/06/},
	Date-Added = {2020-03-11 21:49:04 +0100},
	Date-Modified = {2020-03-11 21:49:04 +0100},
	Db = {PubMed},
	Doi = {10.1007/bf02289146},
	Isbn = {0033-3123},
	J2 = {Psychometrika},
	Journal = {Psychometrika},
	Keywords = {*MENTAL TESTS; *Group Structure; Humans; *Intelligence Tests},
	La = {eng},
	Month = {06},
	Number = {2},
	Pages = {95--116},
	Title = {A method of matrix analysis of group structure},
	Ty = {JOUR},
	U1 = {18152948{$[$}pmid{$]$}},
	Url = {https://pubmed.ncbi.nlm.nih.gov/18152948},
	Volume = {14},
	Year = {1949},
	Bdsk-Url-1 = {https://pubmed.ncbi.nlm.nih.gov/18152948},
	Bdsk-Url-2 = {https://doi.org/10.1007/bf02289146}}

@misc{web-maximalclique,
	Author = {Weisstein, Eric W.},
	Date-Added = {2020-03-09 22:08:36 +0100},
	Date-Modified = {2020-03-09 22:08:36 +0100},
	Howpublished = {From MathWorld--A Wolfram Web Resource.},
	Title = {Maximal Clique},
	Urldate = {https://mathworld.wolfram.com/MaximalClique.html}}

@misc{web-maximumclique,
	Author = {Weisstein, Eric W.},
	Date-Added = {2020-03-09 22:07:15 +0100},
	Date-Modified = {2020-03-09 22:09:12 +0100},
	Howpublished = {From MathWorld--A Wolfram Web Resource.},
	Title = {Maximum Clique},
	Urldate = {http://mathworld.wolfram.com/MaximumClique.html}}

@misc{web-clique,
	Author = {Weisstein, Eric W.},
	Date-Added = {2020-03-09 20:27:59 +0100},
	Date-Modified = {2020-03-09 20:29:31 +0100},
	Howpublished = {From MathWorld--A Wolfram Web Resource},
	Lastchecked = {9 marzo 2020},
	Title = {Clique},
	Url = {https://mathworld.wolfram.com/Clique.html},
	Bdsk-Url-1 = {https://mathworld.wolfram.com/Clique.html}}

@article{fred-glover,
	Author = {Fred Glover},
	Date-Added = {2020-03-07 18:25:50 +0100},
	Date-Modified = {2020-03-07 18:26:51 +0100},
	Journal = {Computers operations research},
	Keywords = {Fred Glover Metaheuristica},
	Pages = {533-549},
	Title = {Future paths for integer programming and links to ar tifi cial intelligence},
	Volume = {13},
	Year = {1986}}

@book{libro-metaheuristicas,
	Author = {Abraham Duarte Mu{\~n}oz, Juan Jos{\'e} Pantrigo Fern{\'a}ndez, Micael Gallego Carrillo},
	Date-Added = {2020-03-07 18:13:23 +0100},
	Date-Modified = {2020-03-07 18:14:28 +0100},
	Editor = {Universidad Rey Juan Carlos, Servicio de Publicaciones},
	Keywords = {libro metaheuristicas},
	Title = {Metaheur{\'\i}sticas},
	Year = {2010}}

@electronic{wiki-metaheuristica,
	Date-Added = {2020-03-07 16:30:25 +0100},
	Date-Modified = {2020-03-07 20:24:30 +0100},
	Keywords = {metaheuristica wikipedia},
	Lastchecked = {7 de marzo del 2020},
	Month = {10},
	Title = {Metaheur{\'\i}stica},
	Url = {https://es.wikipedia.org/wiki/Metaheur%C3%ADstica},
	Year = {2019},
	Bdsk-Url-1 = {https://es.wikipedia.org/wiki/Metaheur%C3%ADstica}}

@book{gpolya-book-2,
	Author = {George P{\'o}lya},
	Date-Added = {2020-03-07 14:12:24 +0100},
	Date-Modified = {2020-03-07 14:13:07 +0100},
	Editor = {Editorial Trillas},
	Keywords = {How to Solve It George P{\'o}lya},
	Title = {C{\'o}mo Plantear y Resolver Problemas},
	Year = {1965}}

@book{gpolya-book-1,
	Author = {George P{\'o}lya},
	Date-Added = {2020-03-07 14:11:31 +0100},
	Date-Modified = {2020-03-07 14:12:19 +0100},
	Editor = {Princeton},
	Keywords = {How to Solve It George P{\'o}lya},
	Title = {How to Solve It},
	Year = {1945}}

@electronic{conceptodef-heuristica,
	Author = {Conceptodefinicion.de},
	Date-Added = {2020-03-07 14:03:48 +0100},
	Date-Modified = {2020-03-18 17:10:36 +0100},
	Keywords = {concepto definicion heuristica},
	Lastchecked = {7 de marzo del 2020},
	Month = {07},
	Title = {Heur{\'\i}stica},
	Url = {https://conceptodefinicion.de/heuristica/},
	Urldate = {7 de marzo del 2020},
	Year = {2019},
	Bdsk-Url-1 = {https://conceptodefinicion.de/heuristica/}}

@webpage{rae-heuristica,
	Author = {Real Academia Espa{\~n}ola},
	Date-Added = {2020-03-07 13:28:20 +0100},
	Date-Modified = {2020-03-18 17:08:34 +0100},
	Keywords = {rae heuristica},
	Title = {Heur{\'\i}stica},
	Url = {https://dle.rae.es/heur%C3%ADstico},
	Bdsk-Url-1 = {https://dle.rae.es/heur%C3%ADstico}}

@article{grasp-feo-resende,
	Author = {Feo, Thomas and Resende, Mauricio},
	Doi = {10.1007/BF01096763},
	Journal = {Journal of Global Optimization},
	Month = {03},
	Pages = {109-133},
	Title = {Greedy Randomized Adaptive Search Procedures},
	Volume = {6},
	Year = {1995},
	Bdsk-Url-1 = {https://doi.org/10.1007/BF01096763}}

@inproceedings{grasp-flightrecoveryproblem,
	Author = {Shaochang, Wang and Fei, Xu and Weixia, Yang and Zhe, Ma},
	Date-Modified = {2020-03-07 22:45:34 +0100},
	Month = {01},
	Title = {Application of Greedy Random Adaptive Search Algorithm (GRASP) in Flight Recovery Problem},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.2991/icsnce-18.2018.17}}

@inproceedings{grasp-parallel,
	Author = {Santos, Joao and Lima, Francisco and Magalh{\~a}es, Rafael and Melo, J.D. and Neto, Adriao},
	Doi = {10.1109/IJCNN.2009.5178938},
	Month = {07},
	Pages = {2798 - 2803},
	Title = {A parallel hybrid implementation using genetic algorithm, GRASP and reinforcement learning},
	Year = {2009},
	Bdsk-Url-1 = {https://doi.org/10.1109/IJCNN.2009.5178938}}

@article{grasp-weapon,
	Author = {Park, Kuk-Kwon and Kang, Tae and Ryoo, Chang-Kyung and Jung, YoungRan},
	Doi = {10.5139/JKSAS.2019.47.12.856},
	Journal = {Journal of the Korean Society for Aeronautical and Space Sciences},
	Month = {12},
	Pages = {856-864},
	Title = {GRASP Algorithm for Dynamic Weapon-Target Assignment Problem},
	Volume = {47},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.5139/JKSAS.2019.47.12.856}}

@article{grasp-vertex,
	Author = {Ram{\'\i}rez, Jorge},
	Month = {01},
	Pages = {28-41},
	Title = {Metaheur{\'\i}stica GRASP para el problema Vertex Bisection Minimization.},
	Volume = {12},
	Year = {2018}}

@article{grasp-empaquetado,
	Author = {Beltran, J. and Calderon, J. and Cabrera, R. and Moreno-Vega, J.},
	Doi = {10.4114/ia.v6i15.755},
	Journal = {Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial},
	Month = {01},
	Pages = {26-33},
	Title = {Procedimientos constructivos adaptativos (GRASP) para el problema del empaquetado bidimensional},
	Volume = {6},
	Year = {2002},
	Bdsk-Url-1 = {https://doi.org/10.4114/ia.v6i15.755}}

@article{grasp-ruta,
	Author = {Villavicencio, V{\'\i}ctor},
	Month = {03},
	Title = {GRASP para el problema de ruta de veh{\'\i}culos},
	Year = {2020}}

@article{mcp-ants,
	Author = {Ponce, Julio and Ponce de Leon Senti, Eunice and Padilla, Alejandro and Padilla, Felipe and Ochoa-Zezzatti, Carlos},
	Month = {01},
	Title = {Algoritmo de Colonia de Hormigas para el Problema del Clique M{\'a}ximo con un Optimizador Local K-opt},
	Year = {2008}}

@article{mrcp-GOEKE2017283,
	Abstract = {Consider a graph in which every vertex has two non-negative weights. In this graph, the maximum ratio clique problem (MRCP) searches for a maximal clique that maximizes a fractional function defined by the ratio of the sums of vertex weights. It has been proved that MRCP is NP-hard and, consequently, it is difficult to solve MRCP by exact methods. Due to this fact, we present the first heuristic approach, i.e., a multi-start Variable Neighborhood Search (MS-VNS) algorithm. In order to verify the performance of our MS-VNS, we use standard instances and according to our observations, our MS-VNS approach provides high-quality solutions in a short computation time. Furthermore, on most of the instances, our algorithm outperforms the classical methods that have already been used for solving MRCP.},
	Author = {Dominik Goeke and Mahdi Moeini and David Poganiuch},
	Doi = {https://doi.org/10.1016/j.cor.2017.01.010},
	Issn = {0305-0548},
	Journal = {Computers y Operations Research},
	Keywords = {Variable Neighborhood Search, Heuristic, Metaheuristic, Maximum ratio clique problem, Maximal clique},
	Pages = {283 - 291},
	Title = {A Variable Neighborhood Search heuristic for the maximum ratio clique problem},
	Url = {http://www.sciencedirect.com/science/article/pii/S0305054817300102},
	Volume = {87},
	Year = {2017},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0305054817300102},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.cor.2017.01.010}}

@article{mcp-batsyn,
	Abstract = {In this paper we present improvements to one of the most recent and fastest branch-and-bound algorithm for the maximum clique problem---MCS algorithm by Tomita et al. (Proceedings of the 4th international conference on Algorithms and Computation, WALCOM'10, pp. 191--203, 2010). The suggested improvements include: incorporating of an efficient heuristic returning a high-quality initial solution, fast detection of clique vertices in a set of candidates, better initial colouring, and avoiding dynamic memory allocation. Our computational study shows some impressive results, mainly we have solved p_hat1000-3 benchmark instance which is intractable for MCS algorithm and got speedups of 7, 3000, and 13000 times for gen400_p0.9_55, gen400_p0.9_65, and gen400_p0.9_75 instances correspondingly.},
	Address = {Boston},
	Author = {Batsyn, Mikhail and Goldengorin, Boris and Maslov, Evgeny and Pardalos, Panos},
	Issn = {1382-6905},
	Journal = {Journal of Combinatorial Optimization},
	Keywords = {Maximum clique problem ; Branch-and-bound algorithm ; Heuristic solution ; Graph colouring},
	Language = {eng},
	Number = {2},
	Pages = {397--416},
	Publisher = {Springer US},
	Title = {Improvements to MCS algorithm for the maximum clique problem},
	Volume = {27},
	Year = {2014}}

@inproceedings{mcp-ryp,
	Author = {Nikolaev, A. and Batsyn, M. and San Segundo, P.},
	Copyright = {Copyright 2015 Elsevier B.V., All rights reserved.},
	Isbn = {9783319190839},
	Issn = {03029743},
	Journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	Keywords = {Branch-and-Bound Algorithm ; Maximum Clique Problem ; Reusing Coloring},
	Pages = {275--280},
	Publisher = {Springer Verlag},
	Title = {Reusing the same coloring in the child nodes of the search tree for the maximum clique problem},
	Volume = {8994},
	Year = {2015}}

@article{mcp-neuro,
	Abstract = {<p>In this paper, we analyze the formula of weights definition in the discrete competitive Hopfield network (DCHOM) and point out its flaw when using it to solve some special instances of maximum clique problem (MCP). Based on the analysis, we propose an improved competitive Hopfield network algorithm (ICHN). In ICHN, we introduce a flexible weight definition method which excites the competitive dynamics, and we also present an initial values setting strategy which efficiently increases the probability of finding optimal solutions. Furthermore, an inhibitive competitive activation mechanism is introduced to form a new input updating rule which reduces significantly the number of neurons with an intermediate level of activations. Our algorithm effectively overcomes the flaw of the DCHOM, and exhibits powerful solving ability for the MCP. Experiments on the benchmark problems and practical applications verify the validity of our algorithm.</p>},
	Author = {Yang, Gang and Yang, Nan and Yi, Junyan and Tang, Zheng},
	Issn = {0925-2312},
	Journal = {Neurocomputing},
	Keywords = {Competitive Hopfield Network ; Maximum Clique Problem ; Inhibitive Competitive Mechanism ; Community Detection ; Competitive Hopfield Network ; Maximum Clique Problem ; Inhibitive Competitive Mechanism ; Community Detection ; Computer Science},
	Language = {eng},
	Pages = {28--35},
	Publisher = {Elsevier B.V},
	Title = {An improved competitive Hopfield network with inhibitive competitive activation mechanism for maximum clique problem},
	Volume = {130},
	Year = {2014}}

@article{mwcp-ls,
	Abstract = {The maximum weight clique problem (MWCP) is an important generalization of the maximum clique problem with wide applications. In this study, we develop two efficient local search algorithms for MWCP, namely SCCWalk and SCCWalk4L, where SCCWalk4L is improved from SCCWalk for large graphs. There are two main ideas in SCCWalk, including strong configuration checking (SCC) and walk perturbation. SCC is a new variant of a powerful strategy called configuration checking for local search. The walk perturbation procedure is used to lead the algorithm to leave the current area and come into a new area of feasible solution space. Moreover, to improve the performance on massive graphs, we apply a low-complexity heuristic called best from multiple selection to select the swapping vertex pair quickly and effectively, resulting in the SCCWalk4L algorithm. In addition, SCCWalk4L uses two recent reduction rules to decrease the scale of massive graphs. We carry out experiments to evaluate our algorithms on several popular benchmarks, which are divided into two groups, including classical benchmarks of small graphs namely DIMACS, BHOSLIB, winner determination problem, and graphs derived from clustering aggregation, as well as massive graphs, including a suite of massive real-world graphs and large-scale FRB graphs. Experiments show that, compared to state-of-the-art heuristic algorithms and exact algorithm, the proposed algorithms perform better on classical benchmarks, and obtain the best solutions for most massive graphs.},
	Author = {Yiyuan Wang and Shaowei Cai and Jiejiang Chen and Minghao Yin},
	Doi = {https://doi.org/10.1016/j.artint.2019.103230},
	Issn = {0004-3702},
	Journal = {Artificial Intelligence},
	Keywords = {The maximum weight clique problem, Local search, Strong configuration checking, Walk perturbation procedure, Massive graph},
	Pages = {103230},
	Title = {SCCWalk: An efficient local search algorithm and its improvements for maximum weight clique problem},
	Url = {http://www.sciencedirect.com/science/article/pii/S0004370219302164},
	Volume = {280},
	Year = {2020},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0004370219302164},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.artint.2019.103230}}

@article{mwcp-ml,
	Author = {Y. {Sun} and X. {Li} and A. {Ernst}},
	Doi = {10.1109/TPAMI.2019.2954827},
	Issn = {1939-3539},
	Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	Keywords = {Machine learning;Optimization;Machine learning algorithms;Search problems;Heuristic algorithms;Atmospheric measurements;Particle measurements;Combinatorial optimization;machine learning;data mining;statistics;problem reduction},
	Pages = {1-1},
	Title = {Using Statistical Measures and Machine Learning for Graph Reduction to Solve Maximum Weight Clique Problems},
	Year = {2019},
	Bdsk-Url-1 = {https://doi.org/10.1109/TPAMI.2019.2954827}}

@article{market-data,
	Abstract = {We consider a network representation of the stock market data referred to as the market graph, which is constructed by calculating cross-correlations between pairs of stocks based on the opening prices data over a certain period of time. We study the evolution of the structural properties of the market graph over time and draw conclusions regarding the dynamics of the stock market development based on the interpretation of the obtained results.},
	Author = {Vladimir Boginski and Sergiy Butenko and Panos M. Pardalos},
	Doi = {https://doi.org/10.1016/j.cor.2005.01.027},
	Issn = {0305-0548},
	Journal = {Computers and Operations Research},
	Keywords = {Market graph, Data mining, Stock price fluctuations, Cross-correlation, Graph theory, Degree distribution, Power-law model, Clustering, Clique, Independent set},
	Note = {Part Special Issue: Operations Research and Data Mining},
	Number = {11},
	Pages = {3171 - 3184},
	Title = {Mining market data: A network approach},
	Url = {http://www.sciencedirect.com/science/article/pii/S0305054805000286},
	Volume = {33},
	Year = {2006},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S0305054805000286},
	Bdsk-Url-2 = {https://doi.org/10.1016/j.cor.2005.01.027}}

@book{opt-comb-rg,
	Author = {Granada-Echeverri, Mauricio and Santa, Jhon},
	Isbn = {978-958-8859-43-9},
	Month = {05},
	Title = {Optimizaci{\'o}n combinatoria - de la teor{\'\i}a a la pr{\'a}ctica},
	Year = {2018}}