  The Estimation of Distribution Algorithm is a new class of population based
search methods in that a probabilistic model of individuals is estimated based
on the high quality individuals and used to generate the new individuals. In
this paper we compute 1) some upper bounds on the number of iterations required
for global convergence of EDA 2) the exact number of iterations needed for EDA
to converge to global optima.
