Data-driven evolutionary optimization [electronic resource] : integrating evolutionary computation, machine learning and data science / Yaochu Jin, Handing Wang, Chaoli Sun.

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Data-driven evolutionary optimization [electronic resource] : integrating evolutionary computation, machine learning and data science / Yaochu Jin, Handing Wang, Chaoli Sun.

9783030746407 (electronic bk.)
3030746402 (electronic bk.)
3030746399
9783030746391

Cham, Switzerland : Springer, [2021]

English

1 online resource.

10.1007/978-3-030-74640-7 doi

QA402.5

519.6

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.

Includes bibliographical references and index.

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Online resource; title from PDF title page (SpringerLink, viewed July 8, 2021).

Studies in computational intelligence ; v. 975.

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