Observatorio de I+D+i UPM

Memorias de investigación
Bayesian Networks and Evolutionary Computation
Áreas de investigación
  • Inteligencia artificial
Three components ¿representation, inference and learning¿ are critical in constructing an intelligent system. We need a declarative representation that is a reasonable encoding of our world model. We need to be able to use this representation effectively to answer a broad range of questions that are of interest. And we need to be able to acquire this probability distribution, combining expert knowledge and accumulated data. Probabilistic graphical models support all three capabilities for a broad range of problems. Evolutionary computation has become an essential tool for solving difficult and high-dimensional optimization problems in a broad range of real problems. Genetic algorithms have been the subject of the major part of such applications. Estimation of distribution algorithms offer a recent evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. This talk will review synergies between probabilistic graphical models and evolutionary computation. First, we will show how to use evolutionary computation in inference and in learning from data problems within probabilistic graphical models. The search for the maximum a posteriori assignment and the optimal triangulation of the moral graph will exemplify inference problems. Learning from data may be carried out both in the space of directed acyclic graphs and in the space of orderings. Second, we will illustrate how to use Bayesian networks and Gaussian networks for developing estimation of distribution algorithms in discrete and continuous domains, respectively. Third, recent advances will be presented, covering regularization methods for learning probabilistic graphical models from data, multi-label classification with multidimensional Bayesian networks classifiers and estimation of distribution algorithms based on copulas and Markov networks. The talk will finish with some challenging applications in bioinformatics and neuroscience.
Entidad relacionada
Nacionalidad Entidad
Lugar del congreso
Buenos Aires
Esta actividad pertenece a memorias de investigación
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
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