Analysis of Estimation of Distribution Algorithms and Genetic Algorithms on NK Landscapes
Neural and Evolutionary Computing
2008-07-30 v1 Artificial Intelligence
Abstract
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.
Keywords
Cite
@article{arxiv.0801.3111,
title = {Analysis of Estimation of Distribution Algorithms and Genetic Algorithms on NK Landscapes},
author = {Martin Pelikan},
journal= {arXiv preprint arXiv:0801.3111},
year = {2008}
}
Comments
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