Multi-source side information fusion assisted Bayesian optimisation

In this project, we study multi-source side information fusion assisted Bayesian optimisation models and algorithms. The aim of this study is to fully exploit the side information to reduce the number of computational times of expensive fitness functions, and, meanwhile, to accurately construct response surface in the parameter space for effectively searching and recognising the global optimum.