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dc.contributor.authorSoto R.
dc.contributor.authorCrawford B.
dc.contributor.authorOlivares R.
dc.contributor.authorGalleguillos C.
dc.contributor.authorCrawford K.
dc.contributor.authorJohnson F.
dc.contributor.authorParedes F.
dc.date.accessioned2020-09-02T22:28:43Z
dc.date.available2020-09-02T22:28:43Z
dc.date.issued2015
dc.identifier10.1007/978-3-319-21380-4_101
dc.identifier.citation528, , 593-597
dc.identifier.issn18650929
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6320
dc.descriptionOptimization problems can be found in several real application domains such as engineering, medicine, mathematics, mechanics, physics, mining, games, design, and biology, among others. There exist several techniques to the efficient solving of these problems, which can be organized in two groups: exact and approximate methods. Metaheuristics are one of the most famous and widely used approximate methods for solving optimization problems. Most of them are known for being inspired on interesting behaviors that can be found on the nature, such as the way in which ants, bees and fishes found food, or the way in which fireflies and bats move on the environment. However, solving optimization problems via metaheuristics is not always a simple trip. In this paper, we analyze and discuss from an usability standpoint how the effort needed to design and implement efficient and robust metaheuristics can be conveniently managed and reduced. © Springer International Publishing Switzerland 2015.
dc.language.isoen
dc.publisherSpringer Verlag
dc.sourceStephanidis C.
dc.subjectLocal solution
dc.subjectMetaheuristics
dc.subjectOptimal solution
dc.subjectOptimization problems
dc.subjectApproximation theory
dc.subjectHeuristic algorithms
dc.subjectHuman computer interaction
dc.subjectProblem solving
dc.subjectApproximate methods
dc.subjectDesign and implements
dc.subjectMeta heuristics
dc.subjectOptimal solutions
dc.subjectOptimization problems
dc.subjectReal applications
dc.subjectOptimization
dc.titleThe complexity of designing and implementing metaheuristics
dc.typeConference Paper


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