Ke Tang, PhD,Professor
Department of Computer Science and Engineering,
Southern University of Science and Technology
Title: Scalable Evolutionary Search
Abstract
In the past decades, Evolutionary Algorithms (EAs) have been shown to be capable of achieving high quality solutions to many hard optimization problems (e.g., NP-Hard problems). On the other hand, it is often criticized as having high computational cost, especially for large-scale problems. This drawback is becoming a major hurdle for EAs’ applications in the real-world. This talk will present advances on making EAs more scalable in 3 most common circumstances, which aim at accelerating EAs significantly without deteriorate the quality of obtained solutions. In addition to general ideas and methodologies, case studies on vehicle routing, deep learning and social network analysis will also be given.
Biography
Ke Tang is a Professor at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech). Before that, he was with University of Science and Technology of China, first as an Associate Professor (2007-2011) and then a full Professor (2011-2017). His major research interests include evolutionary computation, machine learning and their applications. He has published more than 160 journal and conference papers. He is/was an Associate Editor or Editorial Board Member of the IEEE Trans. on Evolutionary Computation, IEEE Computational Intelligence Magazine, Computational Optimization and Applications (Springer), Natural Computing (Springer) and Memetic Computing (Springer) and served as program/technical chairs/co-chairs of 10 international conferences. He received the Royal Society Newton Advanced Fellowship in 2015 and the 2018 IEEE Computational Intelligence Society Outstanding Early Career Award.