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In den Warenkorbhardcover. Zustand: Good. General shelfwear to the cover and page edges.
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In den WarenkorbZustand: New. In English.
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In den WarenkorbZustand: New.
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In den Warenkorbhardcover. Zustand: New. New. book.
Verlag: Springer International Publishing, Springer Nature Switzerland, 2024
ISBN 10: 303143546X ISBN 13: 9783031435461
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 149,79
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In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer.In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies.From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems.While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding.This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas.
Verlag: Springer International Publishing, Springer Nature Switzerland, 2023
ISBN 10: 3031435435 ISBN 13: 9783031435430
Sprache: Englisch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 149,79
Währung umrechnenAnzahl: 1 verfügbar
In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer.In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies.From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems.While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding.This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas.
Verlag: Springer International Publishing AG, Cham, 2023
ISBN 10: 3031435435 ISBN 13: 9783031435430
Sprache: Englisch
Anbieter: CitiRetail, Stevenage, Vereinigtes Königreich
EUR 151,24
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In den WarenkorbHardcover. Zustand: new. Hardcover. This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer. In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies.From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems.While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding.This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Verlag: Springer-Nature New York Inc, 2024
ISBN 10: 3031435435 ISBN 13: 9783031435430
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 213,00
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In den WarenkorbHardcover. Zustand: Brand New. 266 pages. 9.25x6.10x0.75 inches. In Stock.
Verlag: Springer International Publishing, Springer Nature Switzerland Dez 2024, 2024
ISBN 10: 303143546X ISBN 13: 9783031435461
Sprache: Englisch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
EUR 149,79
Währung umrechnenAnzahl: 2 verfügbar
In den WarenkorbTaschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer.In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies.From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems.While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding.This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas. 268 pp. Englisch.
Verlag: Springer International Publishing, Springer Nature Switzerland Dez 2023, 2023
ISBN 10: 3031435435 ISBN 13: 9783031435430
Sprache: Englisch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
EUR 149,79
Währung umrechnenAnzahl: 2 verfügbar
In den WarenkorbBuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers a careful selection of studies in optimization techniques based on artificial intelligence, applied to inverse problems in radiative transfer.In this book, the reader will find an in-depth exploration of heuristic optimization methods, each meticulously described and accompanied by historical context and natural process analogies.From simulated annealing and genetic algorithms to artificial neural networks, ant colony optimization, and particle swarms, this volume presents a wide range of heuristic methods. Additional approaches such as generalized extreme optimization, particle collision, differential evolution, Luus-Jaakola, and firefly algorithms are also discussed, providing a rich repertoire of tools for tackling challenging problems.While the applications showcased primarily focus on radiative transfer, their potential extends to various domains, particularly nonlinear and large-scale problems where traditional deterministic methods fall short. With clear and comprehensive presentations, this book empowers readers to adapt each method to their specific needs. Furthermore, practical examples of classical optimization problems and application suggestions are included to enhance your understanding.This book is suitable to any researcher or practitioner whose interests lie on optimization techniques based in artificial intelligence and bio-inspired algorithms, in fields like Applied Mathematics, Engineering, Computing, and cross-disciplinary areas. 268 pp. Englisch.
Verlag: Springer International Publishing, 2023
ISBN 10: 3031435435 ISBN 13: 9783031435430
Sprache: Englisch
Anbieter: moluna, Greven, Deutschland
EUR 127,40
Währung umrechnenAnzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides an impressive selection of computational intelligence techniques, focusing on biologically inspired methodsPresents inverse problems in radiative transfer as optimization problems which can be applied to many other fieldsOffers a s.