Data-Driven Evolutionary Optimization

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 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

Autorentext

Yaochu Jin is an "Alexander von Humboldt Professor for Artificial Intelligence" in the Faculty of Technology, Bielefeld University, Germany. He is also a part-time Distinguished Chair Professor in Computational Intelligence at the Department of Computer Science, University of Surrey, Guildford, UK. He was a "Finland Distinguished Professor" at the University of Jyväskylä, Finland, "Changjiang Distinguished Visiting Professor" at Northeastern University, China, and "Distinguished Visiting Scholar" at the University of Technology in Sydney, Australia. His main research interests include data-driven optimization, multi-objective optimization, multi-objective learning, trustworthy machine learning, and evolutionary developmental systems. Prof Jin is a Member of Academia Europaea and IEEE Fellow. Hangyu Zhu received B.Sc. degree from Yangzhou University, Yangzhou, China, in 2015, M.Sc. degree from RMIT University, Melbourne, VIC, Australia, in 2017, and PhD degree from University of Surrey, Guildford, UK, in 2021. He is currently a Lecturer with the Department of Artificial Intelligence and Computer Science, Jiangnan University, China. His main research interests are federated learning and evolutionary neural architecture search. Jinjin Xu received the B.S and Ph.D. degrees from East China University of Science and Technology, Shanghai, China, in 2017 and 2022, respectively. He is currently a researcher with the Intelligent Perception and Interaction Research Department, OPPO Research Institute, Shanghai, China. His research interests include federated learning, data-driven optimization and its applications. Yang Chen received Ph.D. from the School of Information and Control Engineering, China University of Mining and Technology, China, in 2019. He was a Research Fellow with the School of Computer Science and Engineering, Nanyang Technological University, Singapore, 2019-2022. He is currently with the School of Electrical Engineering, China University of Mining and Technology, China. His research interests include deep learning, secure machine learning, edge computing, anomaly detection, evolutionary computation, and intelligence optimization.

Inhalt
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.