This book provides readers the recent algorithmic advances towards realizing the notion of optinformatics in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the readers will understand the concept of optinformatics , recent research progresses in this direction, as well as particular algorithm designs and application of optinformatics . Evolutionary algorithms (EAs) are adaptive search approaches that take inspiration from the principles of natural selection and genetics. Due to their efficacy of global search and ease of usage, EAs have been widely deployed to address complex optimization problems occurring in a plethora of real-world domains, including image processing, automation of machine learning, neural architecture search, urban logistics planning, etc. Despite the success enjoyed by EAs, it is worth noting that most existing EA optimizers conduct the evolutionary search process from scratch, ignoring the data that may have been accumulated from different problems solved in the past. However, today, it is well established that real-world problems seldom exist in isolation, such that harnessing the available data from related problems could yield useful information for more efficient problem-solving. Therefore, in recent years, there is an increasing research trend in conducting knowledge learning and data processing along the course of an optimization process, with the goal of achieving accelerated search in conjunction with better solution quality. To this end, the term optinformatics has been coined in the literature as the incorporation of information processing and data mining (i.e., informatics) techniques into the optimization process. The primary market of this book is researchers from both academia and industry, who are working on computational intelligence methods and their applications. This book is also written to be used as a textbook for a postgraduate course in computational intelligence emphasizing methodologies at the intersection of optimization and machine learning.

Summarizes recent algorithmic advances toward realizing the notion of optinformatics in evolutionary learning and optimization Contains a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning Covers future directions for algorithmic development in the field of evolutionary computation

Autorentext
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.

Inhalt
Evolutionary Learning and Optimization.- The Rise of Optinformatics in Evolutionary Computation.- Knowledge Learning and Transfer in Meta-heuristics.- Knowledge Reuse in The Form of Local Search.- Knowledge Reuse via Transfer Learning from Past Search Experiences.- Optinformatics across Heterogeneous Problem Domains and Solvers.- Potential Research Directions.