New stopping criterion for genetic algorithms

Genetic Algorithms have been successfully applied in a wide variety of problems. Although widely used, there are few theoretical guidelines for determining when to terminate the search. One result by Aytug and Koehler provides a loose bound on the number of GA generations needed to see all populations (and hence, an optimal solution) with a specified probability. In this paper we derive a tighter bound. This new bound is on the number of iterations required to achieve a level of confidence to guarantee that a Genetic Algorithm has seen all strings (and, hence, an optimal solution).