Many scientific and technological problems are related to optimization. Among them, black-box optimization in high-dimensional space is particularly challenging. Recent neural network-based black-box optimization studies have shown noteworthy achievements. However, their capability in high-dimensional search space is still limited. This study proposes a black-box optimization method based on the evolution strategy (ES) and the generative neural network (GNN) model. We designed the algorithm so that the ES and the GNN model work cooperatively. This hybrid model enables reliable training of surrogate networks; it optimizes multi-objective, high-dimensional, and stochastic black-box functions. Our method outperforms baseline optimization methods in this experiment, including ES, and Bayesian optimization.

翻译：许多科学和技术问题都与优化相关，其中高维空间中的黑箱优化尤为具有挑战性。近年来，基于神经网络的黑箱优化研究取得了显著进展，但在高维搜索空间中的能力仍有限。本研究提出了一种结合进化策略与生成式神经网络的黑箱优化方法，通过设计算法使两者协同工作。这种混合模型能够可靠地训练代理网络，并优化多目标、高维及随机黑箱函数。实验结果表明，该方法在性能上优于包括进化策略和贝叶斯优化在内的基准优化方法。