通过漂移分析改进固定预算结果
Improved Fixed-Budget Results via Drift Analysis
摘要：
固定预算理论关注的是在给定的适应度函数评估预算内计算或限制随机搜索算法可实现的适应度值。尽管最近在固定预算理论方面取得了进展，但缺乏得出这种结果的通用工具。我们转移漂移理论，得到预期的优化时间的关键工具，固定预算的角度来看。第一个易于使用的语句涉及在所谓的贪婪接纳场景中迭代漂移，立即转换为预期函数值的界限。之后，我们考虑一个更一般的工具，基于著名的变量漂移定理。将这种技术应用于LeadingOnes基准函数，可以产生比以前的技术更精确的语句。
英文摘要：
Fixed-budget theory is concerned with computing or bounding the fitness value achievable by randomized search heuristics within a given budget of fitness function evaluations. Despite recent progress in fixed-budget theory, there is a lack of general tools to derive such results. We transfer drift theory, the key tool to derive expected optimization times, to the fixed-budged perspective. A first and easy-to-use statement concerned with iterating drift in so-called greed-admitting scenarios immediately translates into bounds on the expected function value. Afterwards, we consider a more general tool based on the well-known variable drift theorem. Applications of this technique to the LeadingOnes benchmark function yield statements that are more precise than the previous state of the art.
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