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This study analyzes performance of several genetic and evolutionaryalgorithms on randomly generated NK fitness landscapes with various values of nand k. A large number of NK problem instances are first generated for each nand k, and the global optimum of each instance is obtained using thebranch-and-bound algorithm. Next, the hierarchical Bayesian optimizationalgorithm (hBOA), the univariate marginal distribution algorithm (UMDA), andthe simple genetic algorithm (GA) with uniform and two-point crossoveroperators are applied to all generated instances. Performance of all algorithmsis then analyzed and compared, and the results are discussed.

Analysis of Estimation of Distribution Algorithms and Genetic Algorithms on NK Landscapes