Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: 975 (Studies in Computational Intelligence, 975) von Jin, Yaochu/ Wang, Handing/ Sun, Chaoli: Brand New Hardcover (2021)

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418 pages. 9.25x6.10x1.02 inches. In Stock.

Bestandsnummer des Verk�ufers x-3030746399

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Bibliografische Details

Titel: Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: 975 (Studies in Computational Intelligence, 975)
Verlag: Springer
Ver�ffentlichungsdatum: 2021
Sprache: Englisch
ISBN-10: 3030746399
ISBN-13: 9783030746391
Bindung: Hardcover
Zustand: Brand New

�ber diesen Titel

Inhaltsangabe

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.

Von der hinteren Coverseite

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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