For a complete list of publications, consult the publication list at the of group's web page.

EDITED BOOKS

Jose A. Lozano, Pedro Larrañaga, Iñaki Inza and Endika Bengoetxea (Editors, 2006) Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms. Springer
Pedro Larrañaga and Jose A. Lozano (Editors, 2002) Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers

EDITED PROCEEDINGS

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
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

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)
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)
P. Larrañaga and J.A. Lozano (Guest editors, 2005) Special Issue on Estimation of Distribution Algorithms, Evolutionary Computation, 13(1)
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).
S. Shakya, R. Santana, and J. A. Lozano A Markovianity based Optimisation Algorithm. Genetic Programming and Evolvable Machines. Accepted for publication.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
B. Calvo, P. Larrañaga and J.A. Lozano (2009) Feature subset selection from positive and unlabelled examples. Pattern Recognition Letters, 30, 1027-1036.
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.
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.
R. Santana, P. Larrañaga and J.A. Lozano (2008) Research topics in discrete estimation of distribution algorithms. Memetic Computing, 1(1), 35-54.
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.
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.
R. Sagarna, J.A. Lozano (2008) Dynamic Search Space Transformations for Software Test Data Generation. Computational Intelligence, 24(1), 23-61.
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.
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.
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.
B. Calvo, J.A. Lozano and P. Larrañaga (2007) Learning Bayesian classifiers from positive and unlabeled examples. Pattern Recognition Letters, 28(16), 2375-2384.
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.
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.
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.
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.
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.
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.
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.
P. Larrañaga, J.A. Lozano, J.M. Peña, I. Inza (2005) Special Issue on Probabilistic Graphical Models for Classification. Machine Learning, 59(3).
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.
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.
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.
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.
P. Larrañaga, J.A. Lozano (2002) Synergies between evolutionary computation and probabilistic graphical models. International Journal of Approximate Reasoning, 31, 155-156.
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.
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.
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 (2002) Dimensionality reduction in unsupervised learning of conditional Gaussian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 590-603.
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.
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.
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.