Data-driven evolutionary optimization : integrating evolutionary computation, machine learning and data science
- Yaochu Jin, Handing Wang, Chaoli Sun.
- text file
- Cham, Switzerland : Springer, 
- Physical description
- 1 online resource
- Studies in computational intelligence ; v. 975. 1860-949X
- Includes bibliographical references and index.
- Introduction to Optimization.- Classical Optimization Algorithms.- Evolutionary and Swarm Optimization.- Introduction to Machine Learning.- Data-Driven Surrogate-Assisted Evolutionary Optimization.- Multi-Surrogate-Assisted Single-Objective Optimization.- Surrogate-Assisted Multi-Objective Evolutionary Optimization.
- (source: Nielsen Book Data)
- Publisher's summary
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.
(source: Nielsen Book Data)
- Publication date
- Studies in computational intelligence, 1860-949X ; v. 975
- 9783030746407 (electronic bk.)
- 3030746402 (electronic bk.)