Evolutionary Multi-Task Optimization (eBook)

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. 

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 intersection of 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.

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A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain's ability to generalize in optimization - particularly in population-based evolutionary algorithms - have received little attention to date.  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.