An improved high-dimensional Bayesian optimization algorithm - Guan, Juan; Wang, Yanhua (Applied intelligence (Dordrecht, Netherlands), 2025)
The Bayesian Optimization Algorithm, as an effective approach to addressing non-linear global optimization problems, is widely embraced in a myriad of machine learning application domains. With the development of big data, the presence of computational and statistical challenges in high-dimensional settings means that, despite the proposed improvements and enhancements, the applicability of the Bayesian Optimization Algorithm is still restricted to low-dimensional problems. Our algorithm (1) extracts an interesting nonlinear latent structure in the function by Kernal Principal Component Analysis(KPCA) to reduce the computational complexity, and (2) uses an improved Mutual-Information-Maximizing Input Clustering (MIMIC) algorithm to optimize only a low-dimensional subspace each iteration for more efficient and effective BO. The experiments demonstrate that the proposed algorithm can achieve a clear improvement in optimization accuracy and speed in high-dimensional space and can efficiently solve high-dimensional problems for Bayesian optimization algorithm.