Analysis of Estimation of Distribution Algorithms and Genetic Algorithms on NK Landscapes
| dc.creator | Pelikan, Martin | |
| dc.date | 2008-01-21 | |
| dc.date.accessioned | 2026-07-07T09:53:23Z | |
| dc.date.available | 2026-07-07T09:53:23Z | |
| dc.description | 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. | |
| dc.description | Also available at the MEDAL web site, http://medal.cs.umsl.edu/ | |
| dc.identifier | https://arxiv.org/abs/0801.3111 | |
| dc.identifier | http://arxiv.org/abs/0801.3111 | |
| dc.identifier | Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008), ACM Press, 1033-1040 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/165934 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.subject | Artificial Intelligence | |
| dc.subject | I.2.6; I.2.8; G.1.6 | |
| dc.title | Analysis of Estimation of Distribution Algorithms and Genetic Algorithms on NK Landscapes | |
| dc.type | text |