DocumentCode :
2092046
Title :
A ´non-model building´ approach to solving hierarchical functions
Author :
Díaz, Felipe Padilla ; De León, Eunice Ponce ; Padilla, Alejandro ; Meija, M.
Author_Institution :
Depto. de Sistemas Electronicos, Univ. Autonoma de Aguascalientes, Mexico
fYear :
2003
fDate :
8-12 Sept. 2003
Firstpage :
207
Lastpage :
214
Abstract :
The hierarchical Bayesian optimization algorithm (hBOA) by M. Pelikan and D.E. Goldberg (2001), used diversity preservation along with the original Bayesian optimization algorithm BOA by M. Pelikan et al. (1999) to tackle boundedly difficult hierarchical functions. However, model building can be an expensive process, and a pertinent question is the possibility of developing operators that can solve certain classes of hierarchical functions in the traditional GA domain. This study shows, that by following a three-step approach to hierarchical problem solving - effective linkage learning, merging of low-order BBs, and diversity preservation - it is possible to use competent (non-model building) selec-to-recombinative GAs to solve certain classes of hierarchical functions. Experimental bounds were found on the type of hierarchical problems that could be solved, and perturbation based linkage detection was found to be the limiting factor.
Keywords :
Bayes methods; genetic algorithms; GA domain; diversity preservation; hBOA; hierarchical Bayesian optimization algorithm; hierarchical functions; linkage detection; linkage learning; nonmodel building; operator development; selec-to-recombinative GAs; Bayesian methods; Couplings; Decision making; Merging; Perturbation methods; Problem-solving; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science, 2003. ENC 2003. Proceedings of the Fourth Mexican International Conference on
Print_ISBN :
0-7695-1915-6
Type :
conf
DOI :
10.1109/ENC.2003.1232896
Filename :
1232896
Link To Document :
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