Overview
- Presents the first comprehensive and systematic introduction to Evolutionary Multi-Task (EMT) optimization
- Describes in detail the application of EMT algorithms in solving various optimization problems
- Written by leading experts in the field of evolutionary computation
Part of the book series: Machine Learning: Foundations, Methodologies, and Applications (MLFMA)
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Table of contents (9 chapters)
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Front Matter
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Background
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Front Matter
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Evolutionary Multi-Task Optimization for Solving Continuous Optimization Problems
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Front Matter
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Evolutionary Multi-Task Optimization for Solving Combinatorial Optimization Problems
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Front Matter
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Evolutionary Multi-Task Optimization for Solving Large-Scale Optimization Problems
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Front Matter
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Back Matter
Keywords
About this book
Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks.
This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.
Authors and Affiliations
About the authors
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.Bibliographic Information
Book Title: Evolutionary Multi-Task Optimization
Book Subtitle: Foundations and Methodologies
Authors: Liang Feng, Abhishek Gupta, Kay Chen Tan, Yew Soon Ong
Series Title: Machine Learning: Foundations, Methodologies, and Applications
DOI: https://doi.org/10.1007/978-981-19-5650-8
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
Hardcover ISBN: 978-981-19-5649-2Published: 30 March 2023
Softcover ISBN: 978-981-19-5652-2Due: 15 April 2024
eBook ISBN: 978-981-19-5650-8Published: 29 March 2023
Series ISSN: 2730-9908
Series E-ISSN: 2730-9916
Edition Number: 1
Number of Pages: X, 219
Number of Illustrations: 1 b/w illustrations
Topics: Artificial Intelligence, Machine Learning, Optimization, Computational Intelligence