From Search State to Control Actions: A Modular Interface for Budgeted Black-Box Optimization

In expensive black-box optimization, the central bottleneck is often how the optimizer spends its scarce oracle budget, rather than how it generates candidate solutions. When each candidate requires a costly rollout, a control rule that mismatches the current search state can consume evaluations without improving the decision. Existing parameter-control methods usually bind such decisions to a particular optimizer or a predefined schedule, leaving the control semantics difficult to separate from their implementation. We introduce Search-State Assisted Policy Orchestration (SAPO), an optimizer-side interface that maps online search states to search-step scale, relative acceptance slack, and conditional perturbation, then executes them through backbone-specific adapters. On a nine-dimensional delivery-mechanism task, the hand-crafted Expert-SAPO-DE instance significantly improves the final objective over DE, jDE, and SHADE under matched oracle, budget, and domain conditions. Its lower median than a deterministic schedule is not statistically significant, and channel ablations do not establish a benefit from joint use of all channels. Backbone-specific SAPO implementations improve on their corresponding baselines in a Standard environment, whereas CEC2020 and BBOB reveal backbone-dependent outcomes. SAPO separates search-control semantics from backbone execution, while the observed benefit remains conditional on the policy, backbone, and task.