Abstract:

Numerous real-world optimization tasks involve performance evaluations that rely on time-consuming or costly physical experiments or high-fidelity simulations, collectively referred to as expensive optimization problems. Data-driven optimization algorithms leverage techniques from data science and machine learning to assist in optimizing such problems. This talk will first introduce the fundamental concepts and taxonomy of data-driven evolutionary optimization. Subsequently, it will explore key challenges associated with applying data-driven algorithms to large-scale expensive optimization problems, as well as to expensive multi-/many-objective scenarios. Finally, applications of data-driven evolutionary optimization in the field of transportation will be discussed.

Biography:

Chaoli Sun is a Professor in the School of Computer Science and Technology at Taiyuan University of Science and Technology, Taiyuan, China. She is a Senior Member of the IEEE and a Distinguished Member of the China Computer Federation (CCF). Prof. Sun currently serves as Chair of the Intelligent Systems Applications Technical Committee (2024–2025) of the IEEE Computational Intelligence Society. She is an Associate Editor of IEEE Transactions on Evolutionary Computation and IEEE Transactions on Artificial Intelligence, and serves on the editorial boards of Engineering Applications of Artificial Intelligence, Complex & Intelligent Systems, and Memetic Computing. She previously served as Chair of the Task Force on Data-Driven Evolutionary Optimization of Expensive Problems (2016–2020). Her current research interests include computational intelligence, trusted computing, and self-organizing robotic systems.