EDNA: Estimation of Dependency Networks Algorithm

One of the key points in Estimation of Distribution Algorithms (EDAs) is the learning of the probabilistic graphical model used to guide the search: the richer the model the more complex the learning task. Dependency networksbased EDAs have been recently introduced. On the contrary of Bayesian networks, dependency networks allow the presence of directed cycles in their structure. In a previous work the authors proposed EDNA, an EDA algorithm in which a multivariate dependency network is used but approximating its structure learning by considering only bivariate statistics. EDNA was compared with other models from the literature with the same computational complexity (e.g., univariate and bivariate models). In this work we propose a modified version of EDNA in which not only the structural learning phase is limited to bivariate statistics, but also the simulation and the parameter learning task. Now, we extend the comparison employing multivariate models based on Bayesian networks (EB...

• Type: Conference
• Year: 2007
• Where: IWINAC
• Authors: José A. Gámez, Juan L. Mateo, Jose Miguel Puerta
• Keywords: Bivariate Statistics, Dependency Network, IWINAC 2007, Learning Task