Summary: A simple model of genetic algorithm convergence is presented. An “ideal” iteration may be specified, in terms of a random variable for the weight distribution. The behavior of an “actual” iteration may in some cases be sufficiently close to the ideal behavior that useful quantitative information is obtained. In any case, a useful framework for analyzing a genetic algorithm is provided.