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A 2-level approach for the set covering problem: Parameter tuning of artificial bee colony algorithm by using genetic algorithm

dc.contributor.authorCrawford B.
dc.contributor.authorSoto R.
dc.contributor.authorPalma W.
dc.contributor.authorJohnson F.
dc.contributor.authorParedes F.
dc.contributor.authorOlguín E.
dc.date.accessioned2020-09-02T22:15:43Z
dc.date.available2020-09-02T22:15:43Z
dc.date.issued2014
dc.identifier10.1007/978-3-319-11857-4_22
dc.identifier.citation8794, , 189-196
dc.identifier.issn03029743
dc.identifier.urihttps://hdl.handle.net/20.500.12728/4165
dc.descriptionWe present a novel application of the Artificial Bee Colony algorithm to solve the non-unicost Set Covering Problem. The Artificial Bee Colony algorithm is a recent Swarm Metaheuristic technique based on the intelligent foraging behavior of honey bees. We present a 2-level metaheuristic approach where an Artificial Bee Colony Algorithm acts as a low-level metaheuristic and its paremeters are set by a higher level Genetic Algorithm.3 © 2014 Springer International Publishing Switzerland.
dc.language.isoen
dc.publisherSpringer Verlag
dc.subjectArtificial Bee Colony Algorithm
dc.subjectGenetic Algorithm
dc.subjectParameter Setting
dc.subjectSet Covering Problem
dc.subjectSwarm Intelligence
dc.subjectGenetic algorithms
dc.subjectSwarm intelligence
dc.subjectArtificial bee colony algorithms
dc.subjectForaging behaviors
dc.subjectMeta-heuristic approach
dc.subjectMeta-heuristic techniques
dc.subjectNovel applications
dc.subjectParameter setting
dc.subjectParameter-tuning
dc.subjectSet covering problem
dc.subjectParameter estimation
dc.titleA 2-level approach for the set covering problem: Parameter tuning of artificial bee colony algorithm by using genetic algorithm
dc.typeArticle


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