Black-box optimization often relies on evolutionary and swarm algorithms whose performance is highly problem dependent. We view an optimizer as a short program over a small vocabulary of search operators and learn this operator program separately for each problem instance. We instantiate this idea in Operator-Programmed Algorithms (OPAL), a landscape-aware framework for continuous black-box optimization that uses a small design budget with a standard differential evolution baseline to probe the landscape, builds a $k$-nearest neighbor graph over sampled points, and encodes this trajectory with a graph neural network. A meta-learner then maps the resulting representation to a phase-wise schedule of exploration, restart, and local search operators. On the CEC~2017 test suite, a single meta-trained OPAL policy is statistically competitive with state-of-the-art adaptive differential evolution variants and achieves significant improvements over simpler baselines under nonparametric tests. Ablation studies on CEC~2017 justify the choices for the design phase, the trajectory graph, and the operator-program representation, while the meta-components add only modest wall-clock overhead. Overall, the results indicate that operator-programmed, landscape-aware per-instance design is a practical way forward beyond ad hoc metaphor-based algorithms in black-box optimization.

翻译：黑盒优化通常依赖于进化算法和群智能算法，其性能高度依赖于具体问题。我们将优化器视为基于小型搜索算子词汇表的短程序，并针对每个问题实例分别学习该算子程序。我们在算子编程算法（OPAL）中实现了这一思想，这是一个面向连续黑盒优化的景观感知框架。该框架利用标准差分进化基线配合少量设计预算对问题景观进行探测，基于采样点构建$k$近邻图，并通过图神经网络对该轨迹进行编码。随后，元学习器将所得表征映射为分阶段的探索、重启与局部搜索算子调度方案。在CEC~2017测试集上，单一元训练OPAL策略在统计意义上与最先进的自适应差分进化变体具有竞争力，并在非参数检验下较简单基线取得显著改进。在CEC~2017上进行的消融实验验证了设计阶段、轨迹图与算子程序表征的设计合理性，而元学习组件仅带来适度的实际计算开销。总体而言，结果表明基于算子编程的景观感知单实例设计是超越黑盒优化中临时性隐喻驱动算法的可行发展方向。