Learning to optimize (L2O) has emerged as a powerful framework for black-box optimization (BBO). L2O learns the optimization strategies from the target task automatically without human intervention. This paper focuses on obtaining better performance when handling high-dimensional and expensive BBO with little function evaluation cost, which is the core challenge of black-box optimization. However, current L2O-based methods are weak for this due to a large number of evaluations on expensive black-box functions during training and poor representation of optimization strategy. To achieve this, 1) we utilize the cheap surrogate functions of the target task to guide the design of the optimization strategies; 2) drawing on the mechanism of evolutionary algorithm (EA), we propose a novel framework called B2Opt, which has a stronger representation of optimization strategies. Compared to the BBO baselines, B2Opt can achieve 3 to $10^6$ times performance improvement with less function evaluation cost. We test our proposal in high-dimensional synthetic functions and two real-world applications. We also find that deep B2Opt performs better than shallow ones.

翻译：学习优化（L2O）已成为黑箱优化（BBO）的重要框架。L2O能够自动从目标任务中学习优化策略，无需人工干预。本文聚焦于在函数评估成本极低的情况下，处理高维且昂贵的黑箱优化问题以获取更优性能，这恰是黑箱优化的核心挑战。然而，现有基于L2O的方法因在训练中需要大量评估昂贵黑箱函数，且优化策略表征能力不足，而难以应对此问题。为实现目标，本文：1）利用目标任务的廉价替代函数指导优化策略设计；2）借鉴进化算法（EA）机制，提出名为B2Opt的新框架，该框架具有更强的优化策略表征能力。与黑箱优化基线方法相比，B2Opt能以更少的函数评估成本实现3倍至$10^6$倍的性能提升。我们在高维合成函数及两项实际应用中验证了所提方法，并发现深度B2Opt的性能优于浅层版本。