parameter cross-validation and early-stopping in univariate marginal distribution algorithm
In this paper, a cross-validation and early-stopping algorithm is devised for parameter updating in the Univariate Marginal Distribution Algorithm (UMDA) to reduce overftting. Our hypothesis is that the well-known problem of diversity loss in UMDA is a consequence of overfitting during the parameter estimation step at each generation. It is tested by experiments on two different optimization problems.
cross-validation early-stopping overfitting