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

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

2008-01-21

arxiv.org/abs/0801.3111v1

Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), ACM Press, 1033-1040

Computer Science

Neural and Evolutionary Computing

Also available at the MEDAL web site, http://medal.cs.umsl.edu/

Scientific paper

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.

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