1 INTRODUCTION  
2 BACKGROUND AND FORMULATION  
2.1 Black-box Optimization  
2.2 Bayesian Optimization  
2.3 Transfer Learning Scenarios  
3 IMPLEMENTATION OF TRANSFER LEARNING: OVERVIEW  
3.1 Transfer Learning: Opportunity and Challenge  
3.2 Categories of Transfer Learning-Based Bayesian Optimization  
4 TRANSFER LEARNING FROM THE VIEW OF SURROGATE DESIGN  
5 TRANSFER LEARNING FROM THE VIEW OF ACQUISITION FUNCTION DESIGN  
6 TRANSFER LEARNING FROM THE VIEW OF INITIALIZATION DESIGN  
7 TRANSFER LEARNING FROM THE VIEW OF SPACE DESIGN  
8 APPLICATION SCENARIOS  
8.1 AutoML Tuning  
8.2 DBMS Tuning  
8.3 Computing Platform Tuning  
9 FUTURE DIRECTION  
9.1 Evaluation Analysis  
9.2 Comprehensive Framework  
9.3 Generalized Transferable Information  
9.4 Combined Transfer Learning Method  
10 CONCLUSION