For a complete list of publications, consult the publication list at the of group's
web page.
EDITED BOOKS
EDITED PROCEEDINGS
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X. Yao, E. Burke, J.A. Lozano, J. Smith, J.J. Merelo-Guervós, J.A. Bullinaria, J. Rowe, P. Tino, A. Kabán and H.-P Schwefel (2004) Parallel Problem Solving from Nature-PPSN VIII. Lecture Notes in Computer Science, Vol. 3242. Springer-Verlag
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P. Larrañaga, J.A. Lozano, J.M. Peña and I.Inza (2003) Proceedings of the ECML/PKDD - 2003 Workshop on Probabilistic Graphical Models for Classification. Ruder Boskovic Institute
EDITED SPECIAL ISSUES
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Jose A. Lozano, Qingfu Zhang and Pedro Larrañaga (Guest editors, 2009) Special Issue on Evolutionary Algorithms Based on Probabilistic Models IEEE Transaction on Evolutionary Computation. 13(6)
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P. Larrañaga, J.A. Lozano, J.M. Peña and I.Inza (Guest editors, 2005) Special Issue on Probabilistic Graphical Models for Classification, Machine Learning, 59(3)
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P. Larrañaga and J.A. Lozano (Guest editors, 2005) Special Issue on Estimation of Distribution Algorithms, Evolutionary Computation, 13(1)
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P. Larrañaga and J.A. Lozano (2002) Special Issue in Synergies Between Probabilistic Graphical Models and Evolutionary Computation, International Journal of Approximate Reasoning, 31
REFEREED INTERNATIONAL JOURNALS
- J. Ortigosa-Hernandez, J.D. Rodriguez, L. Alzate, M. Lucania I. Inza, J.A. Lozano. (2012). Approaching Sentiment Analysis by Using Semi-supervised Learning of Multi-dimensional Classifiers. Special Issue in Data Mining Applicacions and Case Studies, Neurocomputing (Accepted for publication).
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S. Shakya, R. Santana, and J. A. Lozano A Markovianity based Optimisation Algorithm. Genetic Programming and Evolvable Machines. Accepted for publication.
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J. Ceberio, E. Irurozki, A. Mendiburu and J.A. Lozano. A review on Estimation of Distribution Algorithms in Permutation-based Combinatorial Optimization Problems. Progress in Artificial Intelligence. Accepted for publication.
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Jose A. Pascual, Jose Miguel-Alonso and Jose A. Lozano. Optimization-based Mapping Framework for Parallel Applications. Journal of Parallel and Distributed Computing. DOI: 10.1016/j.jpdc.2011.06.005.
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E. Kostem, J.A. Lozano, E. Eskin. Increasing Power of Genome-Wide Association Studies by Collecting Additional SNPs. Genetics. In press.
- I. Ibarbia, A. Mendiburu, M. Santos and J. A. Lozano.
An interactive optimization approach to a real-world oceanographic campaign planning problem. Applied Intelligence. Accepted for publication.
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C. Echegoyen, A. Mendiburu, R. Santana and J. A. Lozano. Towards understanding EDAs based on Bayesian networks through a quantitative analysis. IEEE Transactions on Evolutionary Computation. Accepted for publication.
- E. Irurozki, B. Calvo and J. A. Lozano. A Preprocessing Procedure for Haplotype Inference by Pure Parsimony (2010) IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted for publication.
- R. Santana, A. Mendiburu, N. Zaitlen, E. Eskin and J. A. Lozano. Multi-marker tagging SNP selection using estimation of distribution algorithms (2010) Artificial Intelligence in Medicine. 50, 193-201.
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R. Santana, C. Bielza, P. Larrañaga, J. A. Lozano, C. Echegoyen, A. Mendiburu, R. Armañanzas, S. Shakya (2010). MATEDA: A Matlab package for the implementation and analysis of Estimation of distribution algorithms. Journal of statistical software. American Statistical Association. 35(7), 1-30.
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R. Santana, P. Larrañaga, and J. A. Lozano (2010) Learning factorizations in estimation of distribution algorithms using affinity propagation. Evolutionary Computation. 18(4), 515-546.
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J. D. Rodríguez, A. Pérez and J. A. Lozano (2010)
Sensitivity Analysis of k-fold cross-validation in prediction error estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 (3), pp. 569--574
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Jose A. Lozano, Qingfu Zhang and Pedro Larrañaga (2009) Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models IEEE Transaction on Evolutionary Computation. 13(6), 1197-1198.
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J. A. Fernandes, X. Irigoien, N. Goikoetxea, J. A. Lozano, I. Inza, A. Perez and A. Bode (2009). Fish recruitment prediction, using robust supervised classification methods. Ecological Modelling. , 221(2): 338-352.
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D. Otaegui, S. Baranzini, R. Armañanzas, B. Calvo, M. Muñoz-Culla, P. Khankhanian, I. Inza, J. A. Lozano, A. Asensio, T. Castillo-Triviño, J. Olsacoaga, A. López de Munain. Differential microRNA expression in PBMC from multiple sclerosis patients (2009) PLoS ONE. 4(7), e6309.
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B. Calvo, P. Larrañaga and J.A. Lozano (2009) Feature subset selection from positive and unlabelled examples. Pattern Recognition Letters, 30, 1027-1036.
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J. A. Fernandes, X. Irigoyen, J. A. Lozano, I. Inza (2009) Optimizing the number of classes in automated zooplankton classification. Journal of Plankton Research, 31(1), 19-29.
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R. Armañanzas, I. Inza, R. Santana, Y. Saeys, J. L. Flores, J.A. Lozano, Y. Van de Peer, R. Blanco, V. Robles, C. Bielza, P. Larrañaga (2008). A review of estimation of distribution algorithms in bioinformatics. BioData Mining, 1:6.
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R. Santana, P. Larrañaga and J.A. Lozano (2008) Research topics in discrete estimation of distribution algorithms. Memetic Computing, 1(1), 35-54.
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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, 36(18), e115.
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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.
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R. Sagarna, J.A. Lozano (2008) Dynamic Search Space Transformations for Software Test Data Generation. Computational Intelligence, 24(1), 23-61.
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R. Armañanzas, I. Inza, R. Santana, Y. Saeys, J. L. Flores, J. A. Lozano, Y. Van de Peer, R. Blanco, V. Robles, C. Bielza, P. Larrañaga (2008) A review of estimation of distribution algorithms in bioinformatics. BioData Mining, 1:6.
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R. Santana, P. Larrañaga and J.A. Lozano (2008) Protein folding in simplified models with estimation of distribution algorithms. IEEE Transactions On Evolutionary Computation. Vol. 12. No. 4. Pp. 418-438.
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R. Santana, P. Larrañaga and J.A. Lozano (2008) Combining variable neighborhood search and estimation of distribution algorithms in the protein side placement problem. Journal of Heuristics. Vol 14. Pp 519-547.
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B. Calvo, J.A. Lozano and P. Larrañaga (2007) Learning Bayesian classifiers from positive and unlabeled examples. Pattern Recognition Letters, 28(16), 2375-2384.
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B. Calvo, N. López-Bigas, S.J. Fureny, P. Larrañaga and 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.
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R. Santana, P. Larrañaga, and J.A. Lozano (2007) Side chain placement using estimation of distribution algorithms. Artificial Intelligence in Medicine, 39(1), 49-63.
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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.
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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.
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A. Mendiburu, J. Miguel-Alonso, J.A. Lozano, M. Ostra, C. Ubide (2006) Parallel EDAs to create multivariate calibration models for quantitative chemical applications. Journal of Parallel and Distributed Computing, 66(8), 1002-1013.
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A. Mendiburu, J. Miguel-Alonso, J.A. Lozano (2006) Implementation and Performance evaluation of a parallelization of Estimation of Bayesian Network Algorithms. Parallel Processing Letters, 16(1), 133-148.
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R. Sagarna, J.A. Lozano (2006) Scatter search in software testing, comparison and collaboration with Estimation of Distribution Algorithms. European Journal of Operational Research, 169(2), 392-412.
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P. Larrañaga, J.A. Lozano, J.M. Peña, I. Inza (2005) Special Issue on Probabilistic Graphical Models for Classification. Machine Learning, 59(3).
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P. Larrañaga, J.A. Lozano(2005) Special Issue on Estimation of Distribution Algorithms. Evolutionary Computation, 13(1), V-VI.
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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.
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A. Mendiburu, J.A. Lozano, J. Miguel-Alonso (2005) Parallel implementation of EDAs based on probabilistic graphical models. IEEE Transactions On Evolutionary Computation, 9(4), 406-423.
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R. Sagarna, J.A. Lozano (2005) On the performance of Estimation of Distribution Algorithms applied to software testing. Applied Artificial Intelligence, 19(5), 457-489.
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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 (1), 63-82.
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P. Larrañaga, J.A. Lozano (2002) Synergies between evolutionary computation and probabilistic graphical models. International Journal of Approximate Reasoning, 31, 155-156.
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C. González, J.A. Lozano, P. Larrañaga (2002) Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions. International Journal of Approximate Reasoning, 31(3), 313-340.
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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.
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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.
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J.M. Peña, J.A. Lozano, P. Larrañaga, I. Inza (2002) Dimensionality reduction in unsupervised learning of conditional Gaussian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 590-603.
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C. González, J.A. Lozano, P. Larrañaga (2000) Analyzing the PBIL algorithm by means of discrete dynamical systems. Complex Systems, 12(4), 465-479.
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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.
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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.
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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.
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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.
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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.
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J.A. Lozano, P. Larrañaga, M. Graña, F.X. Albizuri (1999) Genetic algorithms: bridging the convergence gap. Theoretical Computer Science, 229, 11-22.
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F.X. Albizuri, A. D'Anjou, M. Graña, J.A. Lozano (1996) Convergence Properties of High-order Boltzmann Machines. Neural Networks, 9(9), 1561-1567.