Estimation of distribution algorithms (EDA) are optimization methods that search the solution space by building and sampling probabilistic models. The linkage tree genetic algorithm (LTGA), which can be considered an estimation of distribution algorithm, uses hierarchical clustering to build a hierarchical linkage model called the linkage tree, and gene-pool optimal mixing algorithm to generate new solutions. While the LTGA performs very well on problems with separable sub-problems, its performance deteriorates on ones with overlapping sub-problems. This thesis presents a comparison of the effect of different pre-constructed models in the LTGA's performance. Several important factors that...