This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solvingprowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.

Presents a data-driven view of optimization through the framework of memetic computation (MC) Provides the first comprehensive coverage of memetic computation Includes a summary of the complete timeline of MC research activities Explores newly emerging problem settings from the optimization literature in a theoretical manner and systematically describes the associated algorithmic developments that align with the general theme of memetics Offers novel theories and algorithms for principled transfer and multitask optimization Introduces the novel idea of meme-based search space compression for large-scale optimization

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
Introduction: Rise of Memetics in Computing.- Canonical Memetic Algorithms.- Data-Driven Adaptation in Memetic Algorithms.- The Memetic Automaton.- Sequential Knowledge Transfer across Problems.- Multitask Knowledge Transfer across Problems.- Future Direction: Meme Space Evolutions.