Skip to content
Research Fellows Directory

Ke Tang

Professor Ke Tang

Research Fellow

Organisation

University of Birmingham

Research summary

My research falls into the general area of artificial intelligence. In particular, I mainly focus on metaheuristic search, e.g., evolutionary algorithms, which is a technology of choice for highly nonlinear and/or discontinuous real-world optimization problems. Recent development of computing and storage technologies (e.g., cloud computing) has offered powerful infrastructures/facilities to record and analyze the execution data of metaheuristic search methods. Under the support of a Newton Advanced Fellowship, I am closely collaborating with Professor Xin Yao at the University of Birmingham (the UK host of the Fellowship) on data-driven metaheuristic search. That is, leveraging on the abundant execution data generated during the search courses to design smarter meta-heuristic search methods, so as to tackle challenging optimization problems that cannot be solved by any existing approaches. Through both fundamental and applied research, we aim to advance the state-of-the-art of metaheuristic search, as well as to facilitate the solving of hard real-world optimization problems with ever-growing complexity.

Interests and expertise (Subject groups)

Grants awarded

Data-Driven Metaheuristic Search

Scheme: Newton Advanced Fellowship

Dates: Mar 2016 - Mar 2019

Value: £90,000