  In evolutionary optimization, it is important to understand how fast
evolutionary algorithms converge to the optimum per generation, or their
convergence rate. This paper proposes a new measure of the convergence rate,
called average convergence rate. It is a normalised geometric mean of the
reduction ratio of the fitness difference per generation. The calculation of
the average convergence rate is very simple and it is applicable for most
evolutionary algorithms on both continuous and discrete optimization. A
theoretical study of the average convergence rate is conducted for discrete
optimization. Lower bounds on the average convergence rate are derived. The
limit of the average convergence rate is analysed and then the asymptotic
average convergence rate is proposed.
