Technical University of Madrid, Spain

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.

Dr. Pedro Larrañaga received his diploma (Mathematics) degree in 1981 from University of Valladolid, Spain, and a PhD in Computer Science in 1995 from University of the Basque Country, Spain, where he obtained an associate professor level in 1998 and a full professor level in 2004. In 2007 he joined the Technical University of Madrid as full professor at the Department of Artificial Intelligence where he leads the Computational Intelligence group.

His research interests are in the fields of probabilistic graphical models and heuristic optimization. In both fields he has proposed methodological advances and successful applications in industry, computer science and biomedicine.

He has coauthored two edited books on estimation of distribution algorithms, as well as more than 300 scientific papers in different areas. He has participated in more than 70 research projects at national, European and international levels. Since 2007 he is the expert manager of computer technology area of the Spanish Ministry of Science and Innovation.