AbstractThe main hurdle for EDAs is how to estimate and sample the joint probability distribution, especially in d-dimensiona case (d>2). Copula theory provides a useful tool for multivariate probability analysis, which separates joint probability distribution function into product of marginal distributions. We introduce a new paradigm, called Ncopula-EDAs, which integrates EDAs with nested Archimedean copulas, and indicates an innovative way to solve the multivariate and multiple dependences optimization problem. The case of three-dimensional problem is studied in detail. The sampling method of three-dimensional Nested Archimedean copula is illustrated, and the procedure of three-dimensional Ncopula-EDAs is described. The experiment results validate the feasibility and efficiency of our algorithm.