Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science (Studies in Computational Intelligence)

Jin, Yaochu; Wang, Handing; Sun, Chaoli

ISBN 10: 3030746429 ISBN 13: 9783030746421
Verlag: Springer, 2022
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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques.  New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available.

This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.

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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques.  New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available.

This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.

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Titel: Data-Driven Evolutionary Optimization: ...
Verlag: Springer
Erscheinungsdatum: 2022
Einband: Softcover
Zustand: New

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Yaochu Jin (u. a.)
Verlag: Springer, 2022
ISBN 10: 3030746429 ISBN 13: 9783030746421
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Taschenbuch. Zustand: Neu. Data-Driven Evolutionary Optimization | Integrating Evolutionary Computation, Machine Learning and Data Science | Yaochu Jin (u. a.) | Taschenbuch | xxv | Englisch | 2022 | Springer | EAN 9783030746421 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 121978599

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ISBN 10: 3030746429 ISBN 13: 9783030746421
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included. Artikel-Nr. 9783030746421

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ISBN 10: 3030746429 ISBN 13: 9783030746421
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Taschenbuch. Zustand: Neu. Neuware -Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available.This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 420 pp. Englisch. Artikel-Nr. 9783030746421

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