Combining global and local surrogate models to accelerate evolutionary optimization

• Main Authors: Zhou, Zongzhao, Ong, Yew-Soon, Nair, Prasanth B., Keane, Andy J., Lum, Kai Yew
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Aerodynamic Shape Design; Evolutionary Optimization
• Online Access: https://hdl.handle.net/10356/147970
• Institution: Nanyang Technological University

In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive...