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Evolutionary Computation, Soft Computing, Artificial Intelligence, Optimization


Evolutionary computation (EC) algorithms, as a branch of optimization technology, borrow ideas from biological evolution and simulate the survival of the fittest repeatedly to find the global optimum. They have attracted the attention of many researchers and have successfully solved lots of real-world applications thanks to their numerous advantages. The demand for EC algorithms with high performance grows rapidly along with real-world problems that need to be solved has become rather complicated. Thus, the primary focus of our lab is to find ways of accelerating EC convergence with lower cost consumption from three research directions below.
  1. Accelerating EC/interactive EC with estimated convergence point(s);
  2. Proposing new search strategies to improve the performance of existing EC algorithms;
  3. Developing new powerful EC algorithms.
Not limited to the topics mentioned above, we are also actively exploring some research directions. Here, we list several interesting topics that will be covered. We always welcome collaboration with enthusiastic researchers from various fields.


Note that the affiliations and positions in the below were not as of now but in those days.