New Paper: A Surrogate Model Assisted EA for Computationally Expensive Design Optimization Problems with Discrete Variables

The paper “A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables” focuses on discrete numerical variables, which are sometimes unavoidable in product design and manufacturing. It was written by Bo Liu (Department of Computing, Glyndwr University, UK), Nan Sun (Department of Engineering, Glyndwr University, UK),  Qingfu Zhang (Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR), Vic Grout (Department of Computing, Glyndwr University, UK), and Georges Gielen (ESAT-MICAS, Katholieke Universiteit Leuven, Belgium). The paper can be  accessed here:

Related papers, e.g., M. Zaefferer, J. Stork, and T. Bartz-Beielstein, “Distance Measures for Permutations in Combinatorial Efficient Global Optimization” can be found on the SPOTSeven publication page