Data-Driven Evolutionary Optimization - Integrating Evolutionary Computation, Machine Learning and Data Science

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.

List of 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.

Product details

• Authors: Sun Chaoli, Yaoch Jin, Yaochu Jin, Chaoli Sun, Handin Wang, Handing Wang
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 27.06.2021
• EAN: 9783030746391
• ISBN: 978-3-0-3074639-1
• No. of pages: 393
• Dimensions: 155 mm x 27 mm x 235 mm
• Illustrations: XXV, 393 p. 159 illus., 76 illus. in color.
• Series: Studies in Computational Intelligence
• Subject: Natural sciences, medicine, IT, technology > Technology > General, dictionaries