
Multiobjective Evolutionary Algorithms and Applications
Springer (Publisher)
Published on 22. October 2010
Book
Paperback/Softback
X, 296 pages
978-1-84996-935-2 (ISBN)
Description
Evolutionary multiobjective optimization is currently gaining a lot of attention, particularly for researchers in the evolutionary computation communities. This monograph is suitable as a secondary text for graduate level computational intelligence courses, and as a reference for researchers, lecturers, and practitioners in industry.
More details
Series
Edition
Softcover reprint of hardcover 1st ed. 2005
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Research
Illustrations
X, 296 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 17 mm
Weight
470 gr
ISBN-13
978-1-84996-935-2 (9781849969352)
DOI
10.1007/1-84628-132-6
Schweitzer Classification
Other editions
Additional editions

Kay CHen Tan | Eik Fun Khor | Tong Heng Lee
Multiobjective Evolutionary Algorithms and Applications
E-Book
11/2005
1st Edition
Springer
€149.79
Available for download

Kay CHen Tan | Eik Fun Khor | Tong Heng Lee
Multiobjective Evolutionary Algorithms and Applications
Book
05/2005
Springer
€160.49
Shipment within 15-20 days
Persons
Liang Feng is a Professor at the College of Computer Science, Chongqing University, China. His research interests include computational and artificial intelligence, memetic computing, big data optimization and learning, as well as transfer learning and optimization. His research on evolutionary multitasking won the 2019 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is an associate editor of the IEEE Computational Intelligence Magazine, IEEE Transactions on Emerging Topics in Computational Intelligence, Memetic Computing, and Cognitive Computation. He is also the founding chair of the IEEE CIS Intelligent Systems Applications Technical Committee Task Force on "Transfer Learning & Transfer Optimization."
Abhishek Gupta is currently a scientist and technical lead at the Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR). Over the past 5 years, Dr. Gupta has been working at the intersectionof optimization, neuroevolution and machine learning, with particular focus on theories and algorithms in transfer and multi-task optimization. He is interested in applications in engineering design and scientific computing. He received the 2019 IEEE Transactions on Evolutionary Computation Outstanding Paper Award by the IEEE Computational Intelligence Society (CIS), for his work on evolutionary multi-tasking. He is an associate editor of the IEEE Transactions on Emerging Topics in Computational Intelligence, and is also the founding chair of the IEEE CIS Emergent Technology Technical Committee (ETTC) Task Force on Multitask Learning and Multitask Optimization.
Kay Chen Tan is a Chair Professor of Computational Intelligence at the Department of Computing, The Hong Kong Polytechnic University. He has published over 300 peer-reviewed articles and seven books. He is currently the Vice-President (Publications) of IEEE Computational Intelligence Society. He has served as the Editor-in-Chief of IEEE Transactions on Evolutionary Computation (2015-2020) and IEEE Computational Intelligence Magazine (2010-2013), and currently serves as the Editorial Board Member of several journals. He has received several IEEE outstanding paper awards, and is currently an IEEE Distinguished Lecturer Program (DLP) speaker and Chief Co-Editor of Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications.
Yew-Soon Ong is a President Chair Professor in Computer Science at Nanyang Technological University (NTU), and serves as Chief Artificial Intelligence Scientist at the Agency for Science, Technology and Research Singapore. At NTU, he serves as co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab, and Director of the Data Science and Artificial Intelligence Research Center. His research interest is in machine learning, evolution and optimization. He is founding Editor-in-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence and serves as associate editor of IEEE Transactions on Neural Network & Learning Systems, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Artificial Intelligence and others. He has received several IEEE outstanding paper awards and was listed as a Thomson Reuters highly cited researcher and among the World's Most Influential Scientific Minds.
Content
Review of MOEAs.- Conceptual Framework and Distribution Preservation Mechanisms for MOEAs.- Decision Supports and Advanced Features for MOEAs.- Dynamic Population Size and Local Exploration for MOEAs.- A Distributed Cooperative Coevolutionary Multiobjective Algorithm.- Learning the Search Range in Dynamic Environments.- Performance Assessment and Comparison of MOEAs.- A Multiobjective Evolutionary Algorithm Toolbox.- Evolutionary Computer-Aided Control System Design.- Evolutionary Design Automation of Multivariable QFT Control System.- Evolutionary Design of HDD Servo Control System.- Evolutionary Scheduling - VRPTW.- Evolutionary Scheduling - TTVRP.