Evolutionary algorithms are a type of stochastic optimization techniques influenced by genetics and natural evolution. Once the set of candidate solutions has been selected, a new generation is sampled by using recombination (crossover) and mutation operators to the candidate solutions. Public, fixed, problem independent mutation and recombination operators frequently lead to missing building blocks, knowledge of the relationship between variables and result in converging to a local optimum. A method to prevent disruption of building blocks is using the estimation of distribution algorithms (EDAs). The experimental results show that EDAs is capable to identify correct linkage between the...