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An improved high-dimensional Bayesian optimization algorithm
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AI
DOI：10.1007/s10489-025-06750-5.png)
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Abstract
En 中文
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.
Keywords:
High-dimensional Bayesian optimization
Variable interaction
KPCA
MIMIC algorithm