Kay Chen Tan

Traditional optimization algorithms rely on repeated manual redesign. This talk proposes a new paradigm of Algorithm Modelization, which treats algorithm execution trajectories as computational experience and builds the A2M model. A two‑stage “imitation‑to‑surpassing” training integrates knowledge from evolutionary, Bayesian and gradient‑based optimizers. The model shows strong generalization across problems and extrapolates to higher unseen dimensions. It can keep absorbing new algorithms for progressive enhancement. Experiments validate this paradigm, paving the way toward general optimization models that accumulate and evolve without hand‑crafted rules.

Biography:

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.