The core challenge of high-dimensional and expensive black-box optimization (BBO) is how to obtain better performance faster with little function evaluation cost. The essence of the problem is how to design an efficient optimization strategy tailored to the target task. This paper designs a powerful optimization framework to automatically learn the optimization strategies from the target or cheap surrogate task without human intervention. However, current methods are weak for this due to poor representation of optimization strategy. To achieve this, 1) drawing on the mechanism of genetic algorithm, we propose a deep neural network framework called B2Opt, which has a stronger representation of optimization strategies based on survival of the fittest; 2) B2Opt can utilize the cheap surrogate functions of the target task to guide the design of the efficient optimization strategies. Compared to the state-of-the-art BBO baselines, B2Opt can achieve multiple orders of magnitude performance improvement with less function evaluation cost. We validate our proposal on high-dimensional synthetic functions and two real-world applications. We also find that deep B2Opt performs better than shallow ones.

翻译：高维且昂贵的黑箱优化（BBO）的核心挑战在于如何以更小的函数评估代价更快地获得更优性能。该问题的本质在于如何为目标任务设计高效的优化策略。本文设计了一个强大的优化框架，能够无需人工干预便自动从目标或廉价替代任务中学习优化策略。然而，现有方法因优化策略表示能力不足而难以实现此目标。为此，1）借鉴遗传算法的机制，我们提出了一种名为B2Opt的深度神经网络框架，该框架基于适者生存原则具有更强的优化策略表示能力；2）B2Opt可利用目标任务的廉价替代函数来指导高效优化策略的设计。与最先进的BBO基线方法相比，B2Opt能以更少的函数评估代价实现多个数量级的性能提升。我们在高维合成函数和两个实际应用上验证了所提方案，并发现深度B2Opt的性能优于浅层版本。