Rank-based selection in dynamic environments acts on order information that becomes stale while it is being used. Tournaments, elitism, truncation, and Pareto selection may therefore consume rankings that no longer match the current fitness order, while full re-evaluation competes with search for the same budget. This paper formulates the missing information layer as a data-structure problem. A hidden total order on $n$ items drifts by adjacent transpositions, while a maintainer receives one truthful pairwise comparison per step and must answer rank queries continuously. We introduce the comparison patrol, a constant-time maintained-order structure using $3n+O(1)$ words, one comparison per update, deterministic verification-age bounds, and per-item displacement certificates. We prove lower bounds showing that oblivious and location-oblivious maintainers incur expected Kendall error $Ω(\min(α,1)n)$, and show that the patrol operates at the same order. A bump invariant yields exact self-stabilization after drift-free corruption: if the maximum rank overstatement is $L$, recovery takes at most $L$ aligned cycles and cannot finish before $L-1$. This gives a deterministic shock-recovery calculus and a crossover with full rebuild near $L\approx \log_2 n$. The maintained order is then transferred to evolving planar maxima and to evolutionary selection rules, giving deterministic bounds for truncation, tournament, elitist, and two-objective Pareto decisions under drifting fitness. Experiments up to $n=65{,}536$ audit the certificates, recovery laws, equilibrium behavior, and equal-budget dynamic evolutionary loops, identifying when certified local rank maintenance outperforms global re-evaluation and when it should hand over.

翻译：在动态环境中基于秩的选择作用于使用过程中逐渐过时的序信息。锦标赛、精英策略、截断选择和帕累托选择因此可能消耗不再匹配当前适应度顺序的排名，而完全重评估与搜索竞争同一预算。本文将该缺失的信息层形式化为一个数据结构问题。n个物品上的隐藏全序通过相邻置换发生漂移，同时维护者每步获取一个成对比较结果并需持续回答秩查询。我们提出比较巡逻结构，该结构使用3n+O(1)字空间实现常时维护、每次更新仅需一次比较、提供确定性验证时限及每项位移证书。我们证明下界表明，无记忆和位置无记忆维护者产生的期望Kendall误差为Ω(min(α,1)n)，并证明巡逻结构以相同阶数运行。一个碰撞不变量可在无漂移损坏后实现精确自稳定：若最大秩高估为L，恢复至少需要L个对齐周期且可在L-1之前完成。这给出了确定性冲击恢复演算，并在L≈log_2 n处出现与全重建的交叉。维护的序随后被迁移至演化平面最大值与进化选择规则，为漂移适应度下的截断、锦标赛、精英策略及双目标帕累托决策提供确定性界。在n=65,536规模上的实验审计了证书、恢复律、均衡行为及等预算动态进化循环，识别了认证局部秩维护何时优于全局重评估、何时应切换策略。