  Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness
neural networks to meta-learn configurations of traditional black-box
optimizers. Despite their success, they are inevitably restricted by the
limitations of predefined hand-crafted optimizers. In this paper, we present
\textsc{Symbol}, a novel framework that promotes the automated discovery of
black-box optimizers through symbolic equation learning. Specifically, we
propose a Symbolic Equation Generator (SEG) that allows closed-form
optimization rules to be dynamically generated for specific tasks and
optimization steps. Within \textsc{Symbol}, we then develop three distinct
strategies based on reinforcement learning, so as to meta-learn the SEG
efficiently. Extensive experiments reveal that the optimizers generated by
\textsc{Symbol} not only surpass the state-of-the-art BBO and MetaBBO
baselines, but also exhibit exceptional zero-shot generalization abilities
across entirely unseen tasks with different problem dimensions, population
sizes, and optimization horizons. Furthermore, we conduct in-depth analyses of
our \textsc{Symbol} framework and the optimization rules that it generates,
underscoring its desirable flexibility and interpretability.
