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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms: 938 (Studies in Computational Intelligence) - Tapa dura
Sinopsis
This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization.
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This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization.
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
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Springer International Publishing, 2021
ISBN 10: 3030637727
ISBN 13: 9783030637729
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Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Highlights recent research on Archiving Strategies for Evolutionary Multi-objective Optimization AlgorithmsProvides an overview of the different archiving methods which allow convergence of Multi-objective evolutionary algorithms in a st. Nº de ref. del artículo: 448686156
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms (Studies in Computational Intelligence, 938)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
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Springer International Publishing Jan 2021, 2021
ISBN 10: 3030637727
ISBN 13: 9783030637729
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization. 248 pp. Englisch. Nº de ref. del artículo: 9783030637729
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
Publicado por
Springer International Publishing, 2021
ISBN 10: 3030637727
ISBN 13: 9783030637729
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the fieldof multi-objective optimization. Nº de ref. del artículo: 9783030637729
Cantidad disponible: 1 disponibles
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
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Springer International Publishing, Springer International Publishing Jan 2021, 2021
ISBN 10: 3030637727
ISBN 13: 9783030637729
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Neuware -This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the fieldof multi-objective optimization.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 248 pp. Englisch. Nº de ref. del artículo: 9783030637729
Cantidad disponible: 2 disponibles
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms (Studies in Computational Intelligence, 938)
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26390048409
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms (Studies in Computational Intelligence, 938)
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Condición: New. Nº de ref. del artículo: ABLIING23Mar3113020024964
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms (Studies in Computational Intelligence, 938)
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand. Nº de ref. del artículo: 390600006
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms
Librería: Revaluation Books, Exeter, Reino Unido
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Hardcover. Condición: Brand New. 247 pages. 9.25x6.10x0.63 inches. In Stock. Nº de ref. del artículo: x-3030637727
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Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms (Studies in Computational Intelligence, 938)
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
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Condición: New. PRINT ON DEMAND. Nº de ref. del artículo: 18390048403
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