Copula theory is introduced into estimation of distribution algorithms(EDA). This algorithm estimates the
probability model of selected population in two steps: the first is to estimate the margins of each variable, the second is
to construct a empirical copula or a Gaussian Copula. The new population is sampled from the copula and the margins. Thus
the computational cost is simplified, which shows the full dependencies of the variables. The experimental results show the
feasibility and effectiveness of the proposed algorithm.