2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/165934This study analyzes performance of several genetic and evolutionary algorithms on randomly generated NK fitness landscapes with various values of n and k. A large number of NK problem instances are first generated for each n and k, and the global optimum of each instance is obtained using the branch-and-bound algorithm. Next, the hierarchical Bayesian optimization algorithm (hBOA), the univariate marginal distribution algorithm (UMDA), and the simple genetic algorithm (GA) with uniform and two-point crossover operators are applied to all generated instances. Performance of all algorithms is then analyzed and compared, and the results are discussed.Also available at the MEDAL web site, http://medal.cs.umsl.edu/Neural and Evolutionary ComputingArtificial IntelligenceI.2.6; I.2.8; G.1.6text