A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms
In this paper a new method for proving lower bounds on the expected running time of evolutionary algorithms (EAs) is presented. It is based on fitness-level partitions and an additional condition on transition probabilities between fitness levels. The method is versatile, intuitive, elegant, and very powerful. It yields exact or near-exact lower bounds for LO, OneMax, long k-paths, and all functions with a unique optimum. Most lower bounds are very general; they hold for all EAs that only use bit-flip mutation as variation operator, i.e., for all selection operators and population models. The lower bounds are stated with their dependence on the mutation rate. These results have very strong implications. They allow us to determine the optimal mutation-based algorithm for LO and OneMax, i.e., the algorithm that minimizes the expected number of fitness evaluations. This includes the choice of the optimal mutation rate.
evolutionary algorithms fitness-level method runtime analysis theory