Although Evolutionary Algorithms (EAs) have been successfully applied to optimization in discrete search spaces, theoretical developments remain weak, in particular for population-based EAs. This paper presents a first rigorous analysis of the (μ + 1) EA on pseudo-Boolean functions. Using three well-known example functions fromthe analysis of the (1 + 1) EA, we derive bounds on the expected runtime and success probability. For two of these functions, upper and lower bounds on the expected runtime are tight, and on all three functions, the (μ + 1) EA is never more efficient than the (1 + 1) EA. Moreover, all lower bounds growwith μ. On a more complicated function, however, a small increase of μ provably decreases the expected runtime drastically.
This paper develops a newproof technique that bounds the runtime of the (μ + 1) EA. It investigates the stochastic process for creating family trees of individuals; the depth of these trees is bounded. Thereby, the progress of the population towards the optimum is captured. This new technique is general enough to be applied to other population-based EAs.
The author was supported by the Deutsche Forschungsgemeinschaft (DFG) as a part of the Collaborative Research Center “Computational Intelligence” (SFB 531).