PROJECT TITLE :

Average Convergence Rate of Evolutionary Algorithms

ABSTRACT:

In evolutionary optimization, it is necessary to understand how briskly evolutionary algorithms converge to the optimum per generation, or their convergence rates. This letter proposes a new live of the convergence rate, called the common convergence rate. It is a normalized geometric mean of the reduction ratio of the fitness difference per generation. The calculation of the common convergence rate is very straightforward and it's applicable for many evolutionary algorithms on both continuous and discrete optimization. A theoretical study of the typical convergence rate is conducted for discrete optimization. Lower bounds on the average convergence rate are derived. The limit of the typical convergence rate is analyzed and then the asymptotic average convergence rate is proposed.