We want to analyze bio-inspired computation methods in a rigorous way with respect to their runtime behavior. As these algorithms make use of many random decisions, we treat them as randomized algorithms to study their behavior in a rigorous manner. The term stochastic search algorithms stresses this point of view and will be used in the following to point out that bio-inspired computation methods can be treated as algorithms which are based on random decisions. Mainly we will consider stochastic search algorithms belonging to the field of evolutionary computation throughout this book. These algorithms are inspired by the evolution process in nature and follow Darwin’s principle of the survival of the fittest. We take a closer look at the different approaches developed in this field in Section 3.1. Another kind of bio-inspired stochastic search algorithm is ant colony optimization, which will be introduced in Section 3.2. Here, solutions for a given problem are constructed by walks of ants on a so-called construction graph. To give a more complete picture, we describe other popular variants in Section 3.3.