Pedro Larrañaga received the M.Sc. degree in mathematics from the University of Valladolid, in Spain, in 1981, and the Ph.D. in computer science from the University of the Basque Country, in Spain, in 1995. He is a Full Professor at the Department of Artificial Intelligence at the Polytechnic University of Madrid. He is part of the Intelligent Systems Group.

post: Departamento de Inteligencia Artificial Facultad de Informática Universidad Politécnica de Madrid 28660 Boadilla del Monte, Madrid SPAIN

email: pedro.larranaga at fi.upm.es fax: +34-91-352-4819 / 336-7412 phone: +34 91 336 7443

For a complete list of publications consult the journals' publication list of the group.

You can also find my CV: in spanish.

• Edited books:
   
  • J.A. Lozano, P. Larrañaga, I. Inza, E. Bengoetxea (2006).
    Towards a New Evolutionary Computation. Advances in Estimation of Distribution Algorithms.
    Springer.
     
  • P. Larrañaga, J.A. Lozano (2002).
    Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation.
    Kluwer Academic Publishers.
  
  
   
• Edited Proceedings:
   
  • P. Larrañaga, J.A. Lozano, J.M. Peña, I.Inza (2003).
    Proceedings of the ECML/PKDD - 2003 Workshop on Probabilistic Graphical Models for Classification.
    Ruder Boskovic Institute.

  
   

• Edited Special Issues:
   
  • R. Guigó, F. Morán, A. Valencia, A. R. Ortiz, A. Tramontano, R. Alpweiler, J. M. Carazo, B. Oliva, B. Rost, M. Albá, P. Rouzé, A. Brazma, X. Dopazo, P. Bucher, J. Castresana, G. Laval, A. Navarro, M. Kuiper, L. Serrano, P. Larrañaga, M. Vingron, C. Blaschke, L. Hirschman (guest editors, 2005).
    Special Issue on ECCB/JBI Computational Biology.
    Bioinformatics, Vol. 21, Supplement 2.
     
  • P. Larrañaga, J.A. Lozano, J.M. Peña, I. Inza (guest editors, 2005).
    Special Issue on Probabilistic Graphical Models for Classification.
    Machine Learning, 59(3)
     
  • P. Larrañaga, J.A. Lozano (guest editors, 2005).
    Special Issue on Estimation of Distribution Algorithms.
    Evolutionary Computation.
     
  • P. Larrañaga, E. Menasalvas, J.M. Peña, V. Robles (2003).
    Special Issue in Data Mining in Genomics and Proteomics.
    Artificial Intelligence in Medicine, 31.
  
   
  • P. Larrañaga, J.A. Lozano (2002).
    Special Issue in Synergies Between Probabilistic Graphical Models and Evolutionary Computation.
    International Journal of Approximate Reasoning, 31.
  
  
  
• Refereed Journals:
  
  
  • S. J. Furney, B. Calvo, P. Larrañaga, J. A. Lozano, N. López-Bigas (2008).
    Prioritization of candidate cancer genes - an aid to oncogenomic studies.
    Nucleic Acids Research, doi:10.1093/nar/gkn482.
     
  • R. Armañanzas, I. Inza, P. Larrañaga (2008).
    Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers.
    Computer Methods and Programs in Biomedicine, 91(2), 110-121.
     
  • G. Santafé, J. A. Lozano, P. Larrañaga (2008).
    Inference of population structure using genetic markers and a Bayesian model averaging approach for clustering.
    Journal of Computational Biology, 15(2), 207-220.
     
  • D.A. Morales, E. Bengoetxea, P. Larrañaga, M. García, Y. Franco, M. Fresnada, M. Merino (2008).
    Bayesian classification for the selection of in-vitro Human embryos using morphological and clinical data.
    Computer Methods and Programs in Biomedicine, 90, 104-116.
     
  • I. Zipritia, J. Elorriaga, A. Arruarte, P. Larrañaga, R. Armañanzas (2008).
    What is behind a summary evaluation decision?
    Behavior Research Methods, 40(2), 597-612.
     
  • R. Santana, J. A. Lozano, P. Larrañaga (2007).
    Protein folding in simplified models with estimation of distribution algorithms.
    IEEE Transactions on Evolutionary Computation. Accepted for publication
     
  • T. Miquelez, E. Bengoetxea, A. Mendiburu, P. Larrañaga (2007).
    Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains.
    Connection Science, 19(4), 297-319.
     
  • Y. Saeys, I. Inza, P. Larrañaga (2007).
    A review of feature selection techniques.
    Bioinformatics, 23, 2507-2517.
     
  • B. Calvo, J. A. Lozano, P. Larrañaga (2007).
    Learning Bayesian classifiers from positive and unlabeled examples.
    Pattern Recognition Letters, 28(16), 2375-2384.
     
  • R. Santana, J. A. Lozano, P. Larrañaga (2007).
    Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem.
    Journal of Heuristics.
     
  • J.L. Flores, I. Inza, P. Larrañaga (2007).
    Wrapper discretization by means of estimation of distribution algorithms.
    Intelligent Data Analysis Journal, 11(5), 525-545.
     
  • B. Calvo, N. López-Bigas, S.J. Furney, P. Larrañaga, J. A. Lozano (2007).
    A partially supervised classification approach to dominant and recessive human disease gene prediction.
    Computer Methods and Programs in Biomedicine. 85(3), 229-237.
     
  • R. Santana, P. Larrañaga, J. A. Lozano (2007).
    Side chain placement using estimation of distribution algorithms.
    Artificial Intelligence in Medicine, 39(1), 49-63
     
  • G. Santafé J. A. Lozano, P. Larrañaga (2006).
    Bayesian model averaging of naive Bayes for clustering
    IEEE Transactions  on Systems, Man and Cybernetics, Part B. 36(5), 1149-1161
     
  • A. Perez, P. Larrañaga, I. Inza (2006).
    Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes.
    International Journal of Approximate Reasoning, 43, 1-25.
     
  • P. Larrañaga, B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armañanzas, G. Santafé, A. Perez, V. Robles (2006).
    Machine Learning in Bioinformatics.
    Briefings in Bioinformatics, 7(1), 86-112.
     
  • R.M. Cesar-Jr., E. Bengoetxea, I. Bloch, P. Larrañaga (2005).
    Inexact graph matching for mdel-based recognition: Evaluation and comparison of optimization algorithms.
    Pattern Recognition, Vol. 38, Issue 11, 2099-2113.
     
  • R. Blanco, I. Inza, M. Merino, J. Quiroga and P. Larrañaga (2005).
    Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS.
    Journal of Biomedical Informatics, Vol. 38, Issue 5, 376-388
     
  • P. Larrañaga, J.A. Lozano, J.M. Peña, I. Inza (2005).
    Special Issue on Probabilistic Graphical Models for Classification.
    Machine Learning, 59, 211-212.
     
  • P. Larrañaga, J.A. Lozano (2005).
    Special Issue on Estimation of Distribution Algorithms.
    Evolutionary Computation, 13, 1, V-VI.
     
  • J.M. Peña, J.A. Lozano, P. Larrañaga (2005).
    Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks.
    Evolutionary Computation, 13, 1, 43-66.
     
  • T. Miquélez, E. Bengoetxea, P. Larrañaga (2004).
    Evolutionary computation based on Bayesian classifiers.
    International Journal of Applied Mathematics and Computer Science, 14(3), 101-115.
     
  • T. Romero, P. Larrañaga, B. Sierra (2004).
    Learning Bayesian networks in the space of orderings with estimation of distribution algorithms.
    International Journal of Pattern Recognition and Artificial Intelligence, 18(4), 607-625.
     
  • J.M. Peña, J.A. Lozano, P. Larrañaga (2004).
    Unsupervised learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering.
    International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12, 63-82.
     
  • I. Inza, P. Larrañaga, R. Blanco, A.J. Cerrolaza (2004).
    Filter versus wrapper gene selection approaches in DNA microarray domains.
    Artificial Intelligence in Medicine, special issue in "Data mining in genomics and proteomics", vol.31, issue 2, 91-103.
     
  • R. Blanco, P. Larrañaga, I. Inza, B. Sierra (2004).
    Gene selection for cancer classification using wrapper approaches.
    International Journal of Pattern Recognition and Artificial Intelligence, 18(8), 1373-1390.
     
  • V. Robles, P. Larrañaga, J.M. Peña, E. Menasalvas, M.S. Pérez, V. Herves (2004).
    Bayesian networks as consensed voting system in the construction of a multi-classifier for protein secondary structure prediction.
    Artificial Intelligence in Medicine, special issue in "Data mining in genomics and proteomics", 31, 117-136.
  
   
  • R. Blanco, I. Inza, P. Larrañaga (2003).
    Learning Bayesian networks in the space of structures by estimation of distribution algorithms.
    International Journal of Intelligent Systems, 18, 205-220.
     
  • P. Larrañaga, E. Menasalvas, J.M. Peña, V. Robles (2003).
    Special Issue in Data Mining in Genomics and Proteomics.
    Artificial Intelligence in Medicine, 31, III-IV.
  
   
  • P. Larrañaga, J.A. Lozano (2002).
    Special Issue in Synergies Between Probabilistic Graphical Models and Evolutionary Computation.
    International Journal of Approximate Reasoning, 31, 155-156.
  
   
  • C. González, J.A. Lozano, P. Larrañaga (2002).
    Mathematical modeling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions.
    International Journal of Approximate Reasoning, 31, 313-340.
  
   
  • J. M. Peña, J. A. Lozano, P. Larrañaga (2002).
    Learning recursive Bayesian multinets for clustering by means of constructive induction.
    Machine Learning, 47(1), 63-90.
  
   
  • E. Bengoetxea, P. Larrañaga, I. Bloch, A. Perchant, C. Boeres (2002).
    Learning and simulation of Bayesian networks applied to inexact graph matching.
    Pattern Recognition, 35(12), 2867-2880.
  
   
  • I. Inza, B. Sierra, R. Blanco, P. Larrañaga (2002).
    Gene selection by sequential wrapper approaches in microarray cancer class prediction.
    Journal of Intelligent and Fuzzy Systems, 12(1), 25-33.
  
   
  • J. M. Peña, J. A. Lozano, P. Larrañaga (2001).
    Performance Evaluation of Compromise Conditional Gaussian Networks for Data Clustering.
    International Journal of Approximate Reasoning, 28(1), 23-50.
     
  • J. M. Peña, J. A. Lozano, P. Larrañaga, I. Inza (2001).
    Dimensionality reduction in unsupervised learning of conditional Gaussian networks.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 590-603.
     
  • B.Sierra, N. Serrano, P.Larranaga, E.J. Plasencia, I. Inza, J.J. Jiménez, P. Revuelta, M.L. Mora (2001).
    Using Bayesian networks in the construction of a bi-level multi-classifier.
    Artificial Intelligence in Medicine, 22, 233-248.
     
  • I. Inza, M. Merino, P. Larrañaga, J. Quiroga, B. Sierra, M. Girala (2001).
    Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS.
    Artificial Intelligence in Medicine, 23/2, 187-205.
     
  • I. Inza, P. Larrañaga, B. Sierra (2001).
    Feature Subset Selection by Bayesian Networks: a comparison with genetic and sequential algorithms.
    International Journal of Approximate Reasoning, 27/2, 143-164.
     
  • C. González, J.A. Lozano, P. Larrañaga (2000).
    Analyzing the PBIL algorithm by means of discrete dynamical systems.
    Complex Systems 12(4), 465-479.
     
  • J.M. Peña, J.A. Lozano, P. Larrañaga (2000).
    An improved Bayesian Structural EM algorithm for learning Bayesian Networks for clustering.
    Pattern Recognition Letters 21(8), 779-786.
     
  • I. Inza, P. Larrañaga, R. Etxeberria, B. Sierra (2000).
    Feature Subset Selection by Bayesian networks based optimization.
    Artificial Intelligence 123(1-2), 157-184.
     
  • J.M. Peña, J.A. Lozano, P. Larrañaga (1999).
    Learning Bayesian networks for clustering by means of constructive induction.
    Pattern Recognition Letters 20 (11-13), 1219-1230.
     
  • I. Inza, P. Larrañaga, B. Sierra, R. Etxeberria, J.A. Lozano, J.M. Peña (1999).
    Representing the joint behaviour of Machine Learning inducers by Bayesian Networks.
    Pattern Recognition Letters 20 (11-13), 1201-1209.
     
  • J. M. Peña, J. A. Lozano, P. Larrañaga (1999).
    An empirical comparison of four initialization methods for the k-means algorithm.
    Pattern Recognition Letters 20 (10), 1027-1040.
     
  • J. A. Lozano, P. Larrañaga (1999).
    Applying genetic algorithms to search for the best hierarchical clustering of a dataset.
    Pattern Recognition Letters 20 (9), 911-918.
     
  • J. A. Lozano, P. Larrañaga, M. Graña, F. X. Albizuri (1999).
    Genetic algorithms: bridging the convergence gap.
    Theoretical Computer Science 229, 11-22.
  
   
  • P. Larrañaga, C. Kuijpers, R. Murga, I. Inza, S. Dizdarevich (1999).
    Genetic algorithms for the travelling salesman problem: A review of representations and operators.
    Artificial Intelligence Review. No. 13, 129-170.
     
  • B. Sierra, P. Larrañaga (1998).
    Predicting the survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparision between different approaches.
    Artificial Intelligence in Medicine, Vol. 14, Nos. 1, 2, 215-230.
  
   
  • P. Larrañaga, C. Kuijpers, M. Poza, R. Murga (1997).
    Decomposing Bayesian networks by genetic algorithms.
    Statistics and Computing. Vol. 7. No. 1, 19-34.
  
   
  • R. Etxeberria, P. Larrañaga, J.M. Pikaza (1997).
    Analysis of the behaviour of the genetic algorithms when searching Bayesian networks from data.
    Pattern Recognition Letters. Vol. 18, No. 11-13, 1269-1273.
  
   
  • P. Larrañaga, C. Kuijpers, R. Murga, Y. Yurramendi (1996).
    Learning Bayesian network structures by searching for the best ordering with genetic algorithms.
    IEEE Transactions on System, Man and Cybernetics. Vol 26. No. 4, 487-493.
  
   
  • P. Larrañaga, M. Poza, Y. Yurramendi, R. Murga, C. Kuijpers (1996).
    Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters.
    IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 18. No. 9, 912-926.