The information processing strategy of the algorithm is to use the frequency of the components in a population of candidate solutions in the construction of new candidate solutions. This is achieved by first measuring the frequency of each component in the population (the univariate marginal probability) and using the probabilities to influence the probabilistic selection of components in the componentwise construction of new candidate solutions.
The following algorithm provides a pseudocode listing of the Univariate Marginal Distribution Algorithm for minimizing a cost function.
UMDA was designed for problems where the components of a solution are independent (linearly separable).
A selection method is needed to identify the subset of good solutions from which to calculate the univariate marginal probabilities. Many selection methods from the field of Evolutionary Computation may be used.