| Description |
1 online resource |
| Series |
Studies in computational intelligence, 1860-949X ; v. 975 |
|
Studies in computational intelligence ; v. 975. 1860-949X
|
| Contents |
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 |
| 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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed July 8, 2021) |
| Subject |
Mathematical optimization.
|
|
Evolutionary computation.
|
|
Metaheuristics.
|
|
Evolutionary computation
|
|
Mathematical optimization
|
|
Metaheuristics
|
| Form |
Electronic book
|
| Author |
Wang, Handing, author
|
|
Sun, Chaoli, author
|
| ISBN |
9783030746407 |
|
3030746402 |
|
