Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: 975 (Studies in Computational Intelligence, 975) - Tapa dura

Libro 473 de 538: Studies in Computational Intelligence

Sun, Chaoli; Wang, Handing; Jin, Yaochu

 
9783030746391: Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: 975 (Studies in Computational Intelligence, 975)
Tapa dura
ISBN 10: 3030746399 ISBN 13: 9783030746391
Editorial: Springer, 2021

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2021
Idioma
Inglés
ISBN 10 
3030746399
ISBN 13 
9783030746391
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
420
Contacto del fabricante
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Darmstadt
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Alemania

Otras ediciones populares con el mismo título

9783030746421: Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: 975 (Studies in Computational Intelligence)

Edición Destacada

ISBN 10:  3030746429 ISBN 13:  9783030746421
Editorial: Springer, 2022
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