Sharif Digital Repository

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... 

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...