Island-based evolutionary computation with diverse surrogates and adaptive knowledge transfer for high-dimensional data-driven optimization

• Main Authors: ZHANG, Xian-Rong, GONG, Yue-Jiao, CAO, Zhiguang, ZHANG, Jun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Data-driven evolutionary algorithm; large-scale optimization problems; diverse surrogate models; semi-supervised learning; adaptive knowledge transfer; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/9746; https://ink.library.smu.edu.sg/context/sis_research/article/10746/viewcontent/Island_based_EC_av_cc_by.pdf
• Institution: Singapore Management University

In recent years, there has been a growing interest in data-driven evolutionary algorithms (DDEAs) employing surrogate models to approximate the objective functions with limited data. However, current DDEAs are primarily designed for lower-dimensional problems and their performance drops significantl...