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001484362 001__ 1484362
001484362 003__ OCoLC
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001484362 019__ $$a1409733494$$a1410104448$$a1410591491
001484362 020__ $$a9783031383106$$q(electronic bk.)
001484362 020__ $$a3031383109$$q(electronic bk.)
001484362 020__ $$z9783031383090
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001484362 0247_ $$a10.1007/978-3-031-38310-6$$2doi
001484362 035__ $$aSP(OCoLC)1410824749
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001484362 050_4 $$aQA402.5
001484362 08204 $$a519.6$$223/eng/20231128
001484362 24500 $$aDiscrete diversity and dispersion maximization :$$ba tutorial on metaheuristic optimization /$$cRafael Martí, Anna Martínez-Gavara, editors.
001484362 264_1 $$aCham :$$bSpringer,$$c2023.
001484362 300__ $$a1 online resource (349 pages) :$$billustrations (black and white, and color).
001484362 336__ $$atext$$btxt$$2rdacontent
001484362 337__ $$acomputer$$bc$$2rdamedia
001484362 338__ $$aonline resource$$bcr$$2rdacarrier
001484362 4901_ $$aSpringer optimization and its applications ;$$vvolume 204
001484362 504__ $$aIncludes bibliographical references and index.
001484362 5050_ $$aPart I Models: Discrete diversity optimization. Models and instances (Martnez-Gavara) -- The Origins of Discrete Diversity (Kuby) -- Geometrical Analysis of Solutions (Alcaraz) -- Part II Constructive based Metaheuristics: Constructive and destructive methods in heuristic search (Aringhieri) -- Greedy Randomized Adaptive Search Procedure (Snchez-Oro) -- Iterated Greedy (Lozano) -- Part III Trajectory based Metaheuristics: Tabu Search (Martnez-Gavara) -- Variable neighborhood search (Uroevi) -- Less is More approach (Todosijevi) -- Simulated Annealing (Kincaid) -- Part IV Population based Metaheuristics: Scatter Search (Martnez-Gavara) -- Memetic Algorithms (Hao) -- Part V Extensions: Data Mining in Heuristic Search (Martins) -- Multi-objective Optimization (Colmenar).
001484362 506__ $$aAccess limited to authorized users.
001484362 520__ $$aThis book demonstrates the metaheuristic methodologies that apply to maximum diversity problems to solve them. Maximum diversity problems arise in many practical settings from facility location to social network analysis and constitute an important class of NP-hard problems in combinatorial optimization. In fact, this volume presents a missing link in the combinatorial optimization-related literature. In providing the basic principles and fundamental ideas of the most successful methodologies for discrete optimization, this book allows readers to create their own applications for other discrete optimization problems. Additionally, the book is designed to be useful and accessible to researchers and practitioners in management science, industrial engineering, economics, and computer science, while also extending value to non-experts in combinatorial optimization. Owed to the tutorials presented in each chapter, this book may be used in a master course, a doctoral seminar, or as supplementary to a primary text in upper undergraduate courses. The chapters are divided into three main sections. The first section describes a metaheuristic methodology in a tutorial style, offering generic descriptions that, when applied, create an implementation of the methodology for any optimization problem. The second section presents the customization of the methodology to a given diversity problem, showing how to go from theory to application in creating a heuristic. The final part of the chapters is devoted to experimentation, describing the results obtained with the heuristic when solving the diversity problem. Experiments in the book target the so-called MDPLIB set of instances as a benchmark to evaluate the performance of the methods.
001484362 588__ $$aDescription based on print version record.
001484362 650_6 $$aOptimisation mathématique.
001484362 650_6 $$aMétaheuristiques.
001484362 650_0 $$aMathematical optimization.$$vCongresses$$0(DLC)sh2008107548
001484362 650_0 $$aMetaheuristics.
001484362 655_0 $$aElectronic books.
001484362 7001_ $$aMartí, Rafael$$q(Rafael Cunquero),$$eeditor.$$1https://isni.org/isni/0000000117916929
001484362 7001_ $$aMartínez-Gavara, Anna,$$eeditor.
001484362 77608 $$iPrint version:$$tDiscrete diversity and dispersion maximization.$$dCham : Springer, 2023$$z9783031383090$$w(OCoLC)1400116762
001484362 830_0 $$aSpringer optimization and its applications ;$$vv. 204.
001484362 852__ $$bebk
001484362 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-38310-6$$zOnline Access$$91397441.1
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001484362 980__ $$aBIB
001484362 980__ $$aEBOOK
001484362 982__ $$aEbook
001484362 983__ $$aOnline
001484362 994__ $$a92$$bISE