报告题目：Towards Next-Generation Evolutionary Computation: Some Reflections
主讲人：Kay Chen Tan，香港理工大学讲席教授，IEEE CIS副主席（出版），IEEE Fellow
Evolutionary computation refers to a set of stochastic search methods inspired by the principles of natural selection and genetics. It has demonstrated strong search capability in various applications, ranging from engineering design to financial forecasting and beyond. However, despite their success in some domains, there are several limitations when it comes to solving real-world search problems with high dimensionality, such as computational complexity, lack of robustness, and limited scalability.
It is known that deep machine learning has achieved remarkable breakthroughs in recent years, largely driven by rapid advancements in computing resources, the availability of big data, and the development of advanced algorithms. By learning from the success of deep machine learning, it is believed that the next generation of evolutionary algorithms should also leverage the availability of high-performance computing resources and other technologies to address the challenges of real-world problems. These algorithms should be more flexible, adaptive, and efficient, allowing them to provide optimal search solutions in a shorter amount of time.
In this talk, I will provide an overview of optimization in practical applications and and delve into the reasons behind developing the next generation of evolutionary algorithms. Specifically, I will introduce our research on evolutionary transfer optimization, where we focus on creating distributed, scalable, and learnable evolutionary algorithms to tackle challenging optimization problems. Furthermore, I will present an open-source evolutionary optimization platform that leverages the computational power of advanced hardware, such as GPU clusters, to enable users to develop high-performance evolutionary algorithms for solving real-world problems. Lastly, I will wrap up by discussing potential future research directions in this exciting field.
Kay Chen Tan is currently a Chair Professor (Computational Intelligence) of the Department of Computing, The Hong Kong Polytechnic University. He has co-authored 9 books and published over 300 peer-reviewed journal articles. Prof. Tan serves as the Vice-President (Publications) of IEEE Computational Intelligence Society, USA from 2021-2024. He was the Editor-in-Chief of IEEE Transactions on Evolutionary Computation from 2015-2020 and IEEE Computational Intelligence Magazine from 2010-2013. Prof. Tan is an IEEE Fellow and an Honorary Professor at University of Nottingham in UK. He also serves as the Chief Co-Editor of Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications.