2012
R. Santana, L. Bonnet, J. Legeny, and A. Lecuyer. Introducing the use of model-based evolutionary algorithms for
EEG-based motor imagery classification. In Proceedings of the 2012 Genetic and Evolutionary Conference (GECCO-2012), Philadelphia, US. ACM Digital Library. Accepted for publication. 2012.
R. Santana, A. Mendiburu, and J. A. Lozano. Evolving NK-complexity for evolutionary solvers. In Companion Proceedings of the 2012 Genetic and Evolutionary Conference (GECCO-2012), Philadelphia, US. ACM Digital Library. Accepted for publication. 2012.
R. Santana, C. Bielza, and P. Larrañaga. Maximizing the number of polychronous groups in spiking networks. In Companion Proceedings of the 2012 Genetic and Evolutionary Conference (GECCO-2012), Philadelphia, US. ACM Digital Library. Accepted for publication. 2012.
R. Santana, A. Mendiburu and J. A. Lozano. Structural transfer using EDAs: An application to multi-marker tagging SNP selection. In Proceedings of the of the 2012 Congress on Evolutionary Computation (CEC-2012), Brisbane, Australia. IEEE Press. Accepted for publication. 2012.
R. Santana, A. Mendiburu and J. A. Lozano. An analysis of the use of probabilistic modeling for synaptic connectivity prediction from genomic data. In Proceedings of the of the 2012 Congress on Evolutionary Computation (CEC-2012), Brisbane, Australia. IEEE Press. Accepted for publication. 2012.
R. Santana and S. Shakya. Probabilistic Graphical Models and Markov Networks. Chapter 1 in Markov Networks in Evolutionary Algorithms.
Adaptation, Learning and Optimization. series Vol. 14.. Springer. 2012. In Press. Pp. 3-19. 2012.
S. Shakya and R. Santana. A Review of Estimation of Distribution Algorithms and Markov Networks Probabilistic Graphical Models and Markov Networks. Chapter 2 in Markov Networks in Evolutionary Algorithms.
Adaptation, Learning and Optimization. series Vol. 14.. Springer. 2012. In Press. Pp. 21-37. 2012.
S. Shakya and R. Santana. MOA - Markovian Optimisation Algorithm. Chapter 3 in Markov Networks in Evolutionary Algorithms.
Adaptation, Learning and Optimization. series Vol. 14.. Springer. 2012. In Press. Pp. 39-53. 2012.
R. Santana.MN-EDA and the Use of Clique-Based Factorisations in EDAs. Chapter 5 in Markov Networks in Evolutionary Algorithms.
Adaptation, Learning and Optimization. series Vol. 14.. Springer. 2012. In Press. Pp. 73-87. 2012.
R. Santana, A. Mendiburu and J. A. Lozano. Fast Fitness Improvements in Estimation of Distribution Algorithms Using Belief
Propagation. Chapter 9 in Markov Networks in Evolutionary Algorithms.
Adaptation, Learning and Optimization. series Vol. 14.. Springer. 2012. In Press. Pp. 141-155. 2012.
H. Karshenas, R. Santana, C. Bielza, and P. Larrañaga. Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian
Markov Networks. Chapter 10 in Markov Networks in Evolutionary Algorithms.
Adaptation, Learning and Optimization. series Vol. 14.. Springer. 2012. In Press. Pp. 157-173. 2012.
R. Santana, C. Bielza, and P. Larrañaga. Multi-objective optimization approach to the analysis of inter-subject and inter-session variability in BCI experiments. Accepted for poster presentation
at the International Joint Conference on Neural Networks (IJCN-2012), Brisbane, Australia. 2012.
2011
R. Santana, C. Bielza, and P. Larrañaga. Optimizing brain networks topologies using multi-objective evolutionary computation. Neuroinformatics. Vol. 9. No. 1. Pp. 3-19. 2011.
L. Lozada-Chang and R. Santana. Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints. Information Sciences. Vol. 181. Pp. 2340-2355. 2011.
R.Santana.
Estimation of distribution algorithms: from available implementations to potential developments. In Companion Proceedings
of the 2011 Genetic and Evolutionary Conference (GECCO-2011), Dublin, Ireland. ACM Digital Library. Pp. 679-686. 2011.
R.
Santana, S. Muelas, A. LaTorre, J. M. Peña. A Direct Optimization Approach to the P300 Speller. In Proceedings
of the 2011 Genetic and Evolutionary Conference (GECCO-2011), Dublin, Ireland. ACM Digital Library. Pp. 1747-1754. 2011.
R.
Santana, H. Karshenas, C. Bielza, and P. Larrañaga. Regularized k-order Markov Models in EDAs. In Companion Proceedings
of the 2011 Genetic and Evolutionary Conference (GECCO-2011), Dublin, Ireland. ACM Digital Library. Pp. 593-600. 2011.
R.
Santana, C. Bielza, and P. Larrañaga. Affinity Propagation Enhanced by Estimation of Distribution Algorithms. In Proceedings
of the 2011 Genetic and Evolutionary Conference (GECCO-2011), Dublin, Ireland. ACM Digital Library. Pp. 331-338. 2011.
R.
Santana, H. Karshenas, C. Bielza, and P. Larrañaga. Quantitative
Genetics in Multi-Objective Optimization Algorithms: From Useful
Insights to Effective Methods. In Proceedings
of the 2011 Genetic and Evolutionary Conference (GECCO-2011), Dublin, Ireland. ACM Digital Library. Pp. 91-92. 2011.
A. LaTorre, S. Muelas, J. M. Pena, R. Santana, A. Merchan-Perez and J. R. Rodriguez Differential evolution algorithm for the detection of synaptic vesicles. In Proceedings of the of the 2011
Congress on Evolutionary Computation (CEC-2011), New Orleans, U.S.
IEEE Press. Pp. 1687-1694. 2011.
C. Echegoyen, Q. Zhang, A. Mendiburu, R. Santana, and J. A. Lozano. On the limits of effectiveness in estimation of distribution algorithms. In Proceedings of the of the 2011
Congress on Evolutionary Computation (CEC-2011), New Orleans, U.S.
IEEE Press.Pp. 1573-1580. 2011.
H. Karshenas, R. Santana,
C. Bielza and P. Larrañaga. Multi-objective optimization with joint probabilistic modeling of objectives and variables. In Proceedings of the Evolutionary Multi-objective Optimization Conference (EMO-2011). Lecture Notes in Computer Science. Springer-Verlag, Brazil. Pp. 298-312. 2011.
H. Karshenas, R. Santana,
C. Bielza and P. Larrañaga. Regularized model learning in estimation of distribution
algorithms for continuous optimization problems. Technical Report UPM-FI/DIA/2011-1, Department of
Artificial Intelligence, Faculty of Informatics, Technical University of Madrid. January,
2011.
2010
H. Karshenas, R. Santana, C. Bielza, and P. Larrañaga. Multi-Objective Decomposition with Gaussian Bayesian Networks. In International Conference on Metaheuristics and Nature Inspired
Computing META-2010, Djerba, Tunisia. Accepted for presentation. 2010.
R. Santana,
C. Bielza and P. Larrañaga. Network measures for re-using problem information in EDAs. Technical Report UPM-FI/DIA/2010-3, Department of
Artificial Intelligence, Faculty of Informatics, Technical University of Madrid. June,
2010.
C. Echegoyen, A. Mendiburu, R. Santana, and J.A. Lozano.
Estimation of Bayesian networks algorithms in a class of complex
networks. In Proceedings of the of the 2010 Congress on Evolutionary
Computation (CEC-2010), Barcelone, Spain. IEEE Press.
2010.
A. Cuesta-Infante, R. Santana, C. Bielza, and P. Larrañaga, and J. I. Hidalgo. Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms. In Proceedings of the of the 2010
Congress on Evolutionary Computation (CEC-2010), Barcelone, Spain.
IEEE Press.
R. Santana, C. Bielza, and P. Larrañaga. Classification of MEG data using a combined machine learning approach. Accepted for presentation at the Workshop Data analysis competition: Connectivity and multivariate classification approaches of the 17th International Conference of Biogmagnetism. Dubrovnik, Croacia. 2010.
R. Santana, C. Bielza, and P. Larrañaga. Synergies between network-based representation and probabilistic graphical models for classification, inference and optimization problems in neurosciences. In Proceedings of the Twenty Third International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2010). Lecture Notes in Computer Science. Springer-Verlag, Cordoba-Spain. Accepted for publication. 2010.
2009
C. Echegoyen, A. Mendiburu, R. Santana, J.A. Lozano.
A quantitative analysis of estimation of distribution algorithms based on Bayesian networks.
Technical Report EHU-KZAA-TR-2-2009, Department of Computer Science and Artificial Intelligence, University of the Basque Country, September 2009.
R. Santana, A. Mendiburu, N. Zaitlen, E. Eskin, and J. A. Lozano.
On the application of estimation of distribution algorithms to
multi-marker tagging SNP selection.
Technical Report EHU-KZAA-IK-4/09, Department of Computer Science and Artificial Intelligence, University of the Basque Country, July 2009.
R.
Santana,
P. Larrañaga, and J. A. Lozano. Learning factorizations in estimation of distribution algorithms using affinity propagation.
Evolutionary Computation. Accepted for publication.
R.
Santana, C. Bielza, J. A. Lozano, and P. Larrañaga. Mining probabilistic models learned by EDAs in the optimization of multi-objective problems. In Proceedings
of the 2009 Genetic and Evolutionary Conference (GECCO-2009) , Montreal,
Canada. ACM Digital Library. 2009. Pp. 445-452.
R. Santana,
C. Echegoyen, A. Mendiburu, C. Bielza, J. A. Lozano, P. Larrañaga, R. Armañanzas and S. Shakya.
MATEDA: A suite of EDA programs in Matlab.
Technical Report EHU-KZAA-IK-2/09, Department of Computer Science and
Artificial Intelligence, University of the Basque Country, February 2009. MATEDA code is available
here.
C. Echegoyen, A. Mendiburu, R. Santana,
and J. A. Lozano. Analyzing the probability of the optimum in EDAs based on Bayesian networks. In Proceedings
of the 2009
Congress on Evolutionary Computation (CEC-2009), Norway, 2009.
IEEE Press. Pp. 1652-1659.
C. Echegoyen, A. Mendiburu, R. Santana,
and J. A. Lozano. Estudio de la probabilidad del optimo en EDAs basados en redes Bayesianas. In Proceedings
of the VI Congreso Español sobre Metaheurísticas,
Algoritmos Evolutivos y Bioinspirados (MAEB-2009), Malaga, Spain, 2009.
R.
Santana,
P. Larrañaga, and J. A. Lozano. Research topics in discrete
estimation of distribution algorithms. Memetic Computing. Vol 1. No. 1. Pp. 35-54.
2008
S. Shakya and R.
Santana.
A Markovianity based Optimisation Algorithm.
Technical Report EHU-KZAA-IK-3/08, Department of Computer Science and
Artificial Intelligence, University of the Basque Country, September 2008.
R. Armañanzas, I. Inza, R.
Santana, Y. Saeys, J. L. Flores, J. A. Lozano, Y. Van de Peer, R. Blanco, V. Robles, C. Bielza and P. Larrañaga.
A review of estimation of distribution algorithms in bioinformatics. BioData Mining. 1:6.
R.
Santana, P. Larrañaga, and Jose A. Lozano. Adding probabilistic dependencies to the search of protein
side chain configurations using EDAs. In
Proceedings of the 10th International Conference on Parallel Problem Solving From Nature (PPSM-2008). . Dortmund, Germany. 2008. Pp. 1120-1129.
R.
Santana, A. Mendiburu and Jose A. Lozano. An empirical analysis of loopy belief propagation in three topologies: grids, small-world networks and random graphs.
In
Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM-2008). Hirtshals, Denmark. 2008. Pp. 249-256.
S. Shakya and R.
Santana. An EDA based on local Markov property and Gibbs sampling. In Proceedings
of the 2008
Genetic and Evolutionary Conference (GECCO-2008), Atlanta,
US. ACM Digital Library. 2008. Pp. 475-476.
C. Echegoyen, R.
Santana,
J. A. Lozano, and P. Larrañaga. The impact of probabilistic learning algorithms in EDAs based on Bayesian networks.
In Linkage in Evolutionary Algorithms. Studies in Computational Intelligence Series. Springer. Y.-P. Chen and M.-H. Lim editors. Pp. 109-139. 2008.
R. Santana,
P. Larrañaga and J. A. Lozano. Component weighting functions for adaptive search with EDAs. In Proceedings
of the 2008
Congress on Evolutionary Computation (CEC-2008), Hong Kong, 2008.
IEEE Press. Pp 4067-4074.
R.
Santana,
P. Larrañaga, and J. A. Lozano. Protein folding in simplified
models with estimation of distribution algorithms. IEEE Transactions on Evolutionary Computation. Vol. 12. No. 4. Pp. 418-438.
R.
Santana,
J. A. Lozano, and P. Larrañaga. Adaptive estimation of distribution algorithms. In Adaptive Metaheuristics. Studies in Computational Intelligence Series. Springer. C. Cotta, M. Sevaux and K. Soerensen editors. Pp. 177-197.
R. Santana,
P. Larrañaga, and J. A. Lozano.
Learning factorizations in estimation of distribution algorithms using affinity propagation.
Technical Report EHU-KZAA-IK-1/08, Department of Computer Science and
Artificial Intelligence, University of the Basque Country, January 2008.
R. Santana, J. A. Lozano and P. Larrañaga. Combining
variable neighborhood search and estimation of distribution algorithms
in the protein side chain placement problem. Journal of Heuristics. Vol. 14. Pp 519-547.
2007
R. Hoens, R. Santana,
P. Larrañaga, and J. A. Lozano. Optimization by Max-Propagation Using Kikuchi Approximations.
Technical Report EHU-KZAA-IK-2/07, Department of Computer Science and
Artificial Intelligence, University of the Basque Country, November 2007.
A. Mendiburu, R. Santana,
and J. A. Lozano. Introducing Belief Propagation in Estimation of Distribution Algorithms: A Parallel Approach.
Technical Report EHU-KAT-IK-11-07, Department of Computer Science and
Artificial Intelligence, University of the Basque Country, October 2007.
R. Santana,
P. Larrañaga, and J. A. Lozano. Challenges and open problems in discrete EDAs.
Technical Report EHU-KZAA-IK-1/07, Department of Computer Science and
Artificial Intelligence, University of the Basque Country, October 2007.
C. Echegoyen, R.
Santana, J.
A.
Lozano, and P. Larrañaga. Aprendizaje exacto de redes
Bayesianas en algoritmos de estimación de
distribuciones. In Memorias de las Jornadas de Algoritmos Evolutivos y Metaheurísticas (JAEM I). Zaragoza, 2007. Pp. 277-284.
C. Echegoyen, J. A.
Lozano, R.
Santana, and P. Larrañaga. Exact Bayesian network
learning in estimation of distribution algorithms. In Proceedings
of the 2007
Congress on Evolutionary Computation (CEC-2007), Singapore, 2007.
IEEE Press. Pp. 1051-1058.
A. Mendiburu, R.
Santana, J. A. Lozano, and E. Bengoetxea. A parallel
framework for loopy belief propagation. In Workshops Proceedings
of the 2007
Genetic and Evolutionary Conference (GECCO-2007), London,
UK. ACM Digital Library. 2007. Pp. 2843-2850.
R. Santana, J. A. Lozano
and P. Larrañaga. The role of a priori information in the
minimization of contact potentials by means of estimation of
distribution algorithms. Proceedings of the Fifth
European Conference on Evolutionary Computation, Machine Learning and
Data Mining in Bioinformatics. Lecture Notes in Computer Science.
Valencia, 2007. Pp. 247-257.
R. Santana, J. A. Lozano
and P. Larrañaga. Algoritmos de Estimación de
Distribuciones para el problema de la determinación de la cadena
lateral de una proteína. Memorias del V Congreso
Español de Algoritmos Evolutivos y Bioinspirados.
Tenerife, 2007. Pp. 663-670.
2006
P.
Larrañaga, B. Calvo, R.
Santana, C. Bielza, J.
Galdiano, I. Inza, J. A. Lozano, R.
Armañanzas, G. Santafé, A. Pérez and
V. Robles. Machine learning in Bioinformatics. Briefings
in Bioinformatics. 7. 2006. Pp. 86-112. [ bib]
R.
Santana, P. Larrañaga, and J. A. Lozano. Mixtures of
Kikuchi approximations. Proceedings of the 17th European
Conference on Machine Learning ( ECML-2006). Lecture Notes in
Artificial Intelligence. Berlin, Germany. 2006. Pp. 365-376
2005
R.
Santana.
Estimation of distribution algorithms with Kikuchi approximations. Evolutionary
Computation, 13(1):67-97,
2005.[ link
]
Y. M. Alvarez-Ginarte, R. Crespo, L. A.
Montero-Cabrera, J. A. Ruiz-García, Y. M.
Ponce, R.
Santana,
E. Pardillo-Fontdevila, and E. Alonso-Becerra. A
novel in-silico approach for QSAR studies of anabolic and androgenic
activities in the 17-hydroxy-5-androstane steroid family. QSAR
& Combinatorial Science,
4:218-226, 2005.[ link
]
R. Santana,
P. Larrañaga, and J. A. Lozano.
Interactions and dependencies in Estimation of Distribution Agorithms.
In Proceedings of the 2005
Congress on Evolutionary Computation (CEC-2005),
Edinburgh, U.K., 2005. IEEE Press. Pp. 1418-1425.[ bib
]
R. Santana,
P. Larrañaga, and J. A. Lozano.
Aprendizaje y muestreo de la aproximación Kikuchi. In Proceedings
of the III Taller Nacional de Minería de Datos y Aprendizaje
(TAMIDA-2005), Granada, Spain,
2005. Thomson. Pp. 97-105.[ bib
]
R. Santana,
P. Larrañaga, and J. A. Lozano. Protein
structure prediction in simplified models with estimation of
distribution algorithms. In Proceedings
of the IV Congreso Español sobre Metaheurísticas,
Algoritmos Evolutivos y Bioinspirados (MAEB-2005),
Granada, Spain, 2005. Thomson. Pp. 245-252.[ bib
]
R. Santana,
P. Larrañaga, and J. A. Lozano. Properties
of Kikuchi approximations constructed from clique based decompositions.
Technical Report EHU-KZAA-IK-2/05, Department of Computer Science and
Artificial Intelligence, University of the Basque Country, April 2005.
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