An analysis of the performance of the mixture of trees factorized distribution algorithm when priors and adaptive learning are used

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“An analysis of the performance of the mixture of trees factorized distribution algorithm when priors and adaptive learning are used” by Roberto Santana, Institute of Cybernetics. Mathematics and Physics technical report ICIMAF 2002-180, (Havana, Cuba), Mar. 2002.

Abstract

This paper analyzes the behavior of the Mixture of Trees Factorized Distribution Algorithm (MT-FDA) when priors are incorporated. It is shown that the addition of priors that relate the rate of mutation like effect during the search. Adaptive priors that relate the rate of mutation to the quality of the search are also introduced. Additionally, the learning step of the MT-FDA is changed to avoid the overfitting fo data. The results of the experiments show that our proposals improve the trade off between exploration and exploitation displayed by the MT-FDA.

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BibTeX entry:

@techreport{Santana:2002,
   author = {Roberto Santana},
   title = {An analysis of the performance of the mixture of trees
	factorized distribution algorithm when priors and adaptive
	learning are used},
   institution = {Institute of Cybernetics, Mathematics and Physics},
   number = {ICIMAF 2002-180},
   address = {Havana, Cuba},
   month = mar,
   year = {2002},
   issn = {0138-8916}
}

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