Resumen

MADERA QUINTANA, Julio; MARTINEZ LOPEZ, Yoan  y  FERNANDEZ PARDO, José. Estimation of distribution algorithm based on Bayesian networks learning with dependency analysis for integer optimization problems. Rev cuba cienc informat [online]. 2020, vol.14, n.4, pp. 1-19.  Epub 01-Dic-2020. ISSN 2227-1899.

From the study of the Estimation of Distribution Algorithms (EDA) based on polytrees, we propose and evaluate the class of EDA algorithms that use independence tests for learning the probabilistic model. These algorithms are known as constraint-based EDA which define a class of EDA called constraint-based estimation of distribution algorithms (CBEDA). As a result, a new CBEDA TPDA algorithm is proposed using the three-phase dependence detection method for learning Bayesian networks. The experimental results show that the new proposal has adequate numerical qualities for the solution of optimization problems with integer representation such as the deceptive functions and the problem of protein structure prediction (PSP). The results are compared with other state-of-the-art algorithms in evolutionary computation, including proposals from the EDA field.

Palabras clave : Estimation of distribution algorithms; integer optimization; protein structure prediction; independence tests.

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