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dc.contributor.authorChicano, Francisco-
dc.contributor.authorBlum, Christian-
dc.contributor.authorOchoa, Gabriela-
dc.description.abstractFirst paragraph: In combinatorial optimization, the goal is to find an optimal solution, according to some objective function, from a discrete search space. These problems arise widely in industry and academia and, unfortunately, many of them are NP-hard and no polynomial time algorithm can guarantee their solution to a certified optimality unless. Therefore, in the last decades researchers have investigated the use of stochastic search algorithms to find near optimal solutions to these problems. In particular, great research efforts have been devoted to the development and application of metaheuristic algorithms to solve combinatorial optimization problems.en_UK
dc.publisherMIT Press-
dc.relationChicano F, Blum C & Ochoa G (2016) Editorial for the Special Issue on Combinatorial Optimization Problems, Evolutionary Computation, 24 (4), pp. 573-575.-
dc.rightsPublisher policy allows this work to be made available in this repository. Published in Evolutionary Computation, Volume 24, Issue 4, December 2016, p.573-575 by MIT Press. The original publication is available at:-
dc.titleEditorial for the Special Issue on Combinatorial Optimization Problemsen_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleEvolutionary Computation-
dc.type.statusPublisher version-
dc.contributor.affiliationUniversity of Malaga-
dc.contributor.affiliationUniversity of the Basque Country-
dc.contributor.affiliationComputing Science - CSM Dept-
Appears in Collections:Computing Science and Mathematics Journal Articles

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