SCOPE：黑盒组合优化中策略演化的合成条件目标
SCOPE: Synthetic Conditional Objectives for Policy Evolution in Black-Box Combinatorial Optimization
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中文总结 AI 辅助
SCOPE是黑盒组合优化的通用框架，通过基于搜索历史的合成条件目标演化搜索策略，在有限评估预算下提升黑盒搜索性能并具有良好泛化性。
中文摘要 AI 辅助
黑盒组合优化需要在有限的评估预算下系统地识别高质量解决方案，但未知的目标函数几乎无法为下一步搜索应探索何处提供指导。我们提出SCOPE，这是一种用于黑盒组合优化中策略演化的合成条件目标的通用框架。SCOPE不直接优化不可访问的目标，而是基于积累的搜索历史学习一组合成目标，每个目标被设计为对候选解决方案呈现出不同且潜在有用的偏好。这些目标随后被用于演化搜索策略以生成多样化的候选解，其真实质量随后通过黑盒评估进行评估。外层循环根据其诱导的策略发现有前景区域的有效性，自适应地更新和选择合成目标；而内层循环返回一组表现最佳的策略，以降低依赖单一替代偏好的风险。这种表述将目标设计重新定义为引导策略探索的机制，使搜索过程能够利用观察到的证据，同时在离散解空间中保持结构化多样性。在多个基准问题上进行的大量实验表明，SCOPE在有限评估预算下始终能提升黑盒搜索性能，并且在不同组合结构上具有良好的泛化能力。
英文摘要
Black-box combinatorial optimization requires systematically identifying high-quality solutions under a limited evaluation budget, yet the unknown objective function provides little guidance for deciding where the search should explore next. We introduce SCOPE, a general framework for Synthetic Conditional Objectives for Policy Evolution in Black-Box Combinatorial Optimization. Rather than directly optimizing the inaccessible objective, SCOPE learns a set of synthetic objectives conditioned on the accumulated search history, where each objective is designed to expose a distinct and potentially useful preference over candidate solutions. These objectives are then used to evolve search policies that generate diverse candidates, whose true quality is subsequently assessed through black-box evaluations. The outer loop adaptively updates and selects synthetic objectives according to how effectively their induced policies discover promising regions. In contrast, the inner loop returns a portfolio of top-performing policies to reduce the risk of relying on a single surrogate preference. This formulation reframes objective design as a mechanism for guiding policy exploration, enabling the search process to exploit observed evidence while maintaining structured diversity across discrete solution spaces. Extensive experiments across multiple benchmark problems demonstrate that SCOPE consistently improves black-box search performance under limited evaluation budgets and generalizes well across diverse combinatorial structures.
