analysis of estimation of distribution algorithms and genetic algorithms on nk landscapes
This 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.
genetic algorithm nk fitness landscape crossover local search performance analysis branch and bound scalability hierarchical boa