In studying randomized search heuristics, a frequent quantity of interest is the first time a (real-valued) stochastic process obtains (or passes) a certain value. The processes under investigation commonly show a bias towards this goal, the \emph{stochastic drift}. Turning an iteration-wise expected bias into a first time of obtaining a value is the main result of \emph{drift theorems}. This thesis introduces the theory of stochastic drift, providing examples and reviewing the main drift theorems available. Furthermore, the thesis explains how these methods can be applied in various contexts, including those where drift theorems seem a counterintuitive choice. Later sections examine related methods and approaches.

翻译：在研究随机搜索启发式算法时，一个常见的关注量是（实值）随机过程首次达到（或超过）某个特定值的时间。所研究的过程通常表现出朝向该目标的偏向性，即\emph{随机漂移}。将迭代层面的期望偏向转化为首次达到某个值的时间，是\emph{漂移定理}的主要结论。本论文介绍了随机漂移理论，提供了相关示例并综述了现有的主要漂移定理。此外，论文阐述了这些方法如何应用于各种情境，包括那些漂移定理看似反直觉的选择。后续章节探讨了相关方法与研究路径。