Overview
- Includes a brief introduction to mathematical programming, metaheuristic algorithms, and machine learning techniques
- Presents a systematic description of most recent research advances in data-driven evolutionary optimization, including surrogate-assisted single-, multi-, and many-objective optimization
- Introduces various intuitive and mathematical surrogate management strategies, such as the trust region method and acquisition functions in Bayesian optimization
- Provides applications of data-driven optimization to engineering design, automation of process industry, health care, and automated machine learning
Part of the book series: Studies in Computational Intelligence (SCI, volume 975)
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Table of contents (12 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Data-Driven Evolutionary Optimization
Book Subtitle: Integrating Evolutionary Computation, Machine Learning and Data Science
Authors: Yaochu Jin, Handing Wang, Chaoli Sun
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-74640-7
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-74639-1Published: 29 June 2021
Softcover ISBN: 978-3-030-74642-1Published: 30 June 2022
eBook ISBN: 978-3-030-74640-7Published: 28 June 2021
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XXV, 393
Number of Illustrations: 83 b/w illustrations, 76 illustrations in colour
Topics: Data Engineering, Computational Intelligence, Artificial Intelligence