Surrogate-Assisted Evolutionary Algorithm With Model and Infill Criterion Auto-Configuration
Lindong Xie, Genghui Li, Zhenkun Wang, Laizhong Cui, Maoguo Gong
Abstract
Surrogate-assisted evolutionary algorithms (SAEAs) have proven to be effective in solving computationally expensive optimization problems (EOPs). However, the performance of SAEAs heavily relies on the surrogate model and infill criterion used. To improve the generalization of SAEAs and enable them to solve a wide range of EOPs, this paper proposes an SAEA called AutoSAEA, which features model and infill criterion auto-configuration. Specifically, AutoSAEA formulates model and infill criterion selection as a two-level multi-armed bandit problem (TL-MAB). The first and second levels cooperate in selecting the surrogate model and infill criterion, respectively. A two-level reward (TL-R) measures the value of the surrogate model and infill criterion, while a two-level upper confidence bound (TL-UCB) selects the model and infill criterion in an online manner. Numerous experiments validate the superiority of AutoSAEA over some state-of-the-art SAEAs on complex benchmark problems and a real-world oil reservoir production optimization problem.