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有关,其中,高维空间的黑箱优化特别具有挑战性。最近的神经网络黑箱优化研究表明了值得注意的成就。然而,他们在高维搜索空间的能力仍然有限。本研究根据进化战略(ES)和基因神经网络(GNN)模型提出了黑箱优化方法。我们设计了算法,使ES和GNN模型能够合作工作。这种混合模型能够对代理网络进行可靠的培训;它优化了多目标、高维和随机黑箱功能。我们的方法超过了这一实验的基线优化方法,包括ES和Bayesian优化。