Intelligent Systems Group Roberto Santana Page

U. Garciarena, A. Mendiburu and R. Santana. Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components. IEEE Transactions on Neural Networks and Learning Systems. Accepted for publication. 2023.

C. Montenegro, R. Santana, and J. A. Lozano. Introducing Multi-dimensional Hierarchical Classification: Characterization, solving strategies and performance measures. Neurocomputing. Vol. 533. Pp. 141-160. 2023.

V. H. A. Ribeiro, R. Santana, and G Reynoso-Meza. Random vector functional link forests and extreme learning forests applied to UAV automatic target recognition. Engineering Applications of Artificial Intelligence. Vol. 117. Pp. 105538. Pergamon. 2023.

I. Garcia and R. Santana. Paper Unified Framework for the Analysis of the Effect of Control Strategies on On-Load Tap-Changer's Automatic Voltage Controller. IEEE Transactions on Automation Science and Engineering. Accepted for publication. 2023.

J. Vadillo, R. Santana, and J. A. Lozano. Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions. Journal of Machine Learning Research. Vol. 24. Pp. 1-42. MIT Press. 2023.

R. Santana. EML for unsupervised learning. Part I. Chapter 3 In Handbook of Evolutionary Machine Learning . W. Banzhaf, P. Machado, M. Zhang editors. 2023.

R. Santana. Other Search-Based Optimization Approaches. Part I. Chapter 2 in Introduction to Computational Intelligence: An IEEE Computational Intelligence Society Open Book. L. Minku, G. G. Cabral, M. Martins, and M. Wagner editors. Pp. 73-83. 2023.

R. Santana, I. Hidalgo-Cenalmor, U. Garciarena, A. Mendiburu, and J. A. Lozano. Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification. In Proceedings of the 2023 Genetic and Evolutionary Conference (GECCO-2023). Lisabon, Portugal. Accepted for publication. 2023.

U. Garciarena, R. Santana, and A. Mendiburu. Analyzing the interplay between transferable GANs and gradient optimizers. In Companion Proceedings of the 2023 Genetic and Evolutionary Conference (GECCO-2023). Lisabon, Portugal. Accepted for publication. 2023.

I. Prol, I. Inza, R. Santana, J. M. Sala, and A. Picallo. Predictive model of the behaviour of a building's thermal system to analyse the climate change influence. In Proceedings of the 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. Las Palmas de Gran Canaria. Spain. Accepted for publication. 2023.

V. H. A. Ribeiro, R. Santana, and G Reynoso-Meza. Random vector functional link forests and extreme learning forests applied to UAV automatic target recognition. In IFAC World Congress - 22nd WC 2023.Yokohama, Japan. Accepted for presentation as Dissemination Contribution. 2023.

A. Bidaurrazaga, A. P\'erez, and R. Santana. Structural Restricted Boltzmann Machine for image denoising and classification. arXiv e-print (arXiv:2306.09628). 2023.

R. Santana, I. Hidalgo-Cenalmor, U. Garciarena, A. Mendiburu, and J. A. Lozano .Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification. arXiv e-print (arXiv:2303.02801). 2023.

A. Bonfanti, R. Santana, M. Ellero, and B. Gholami. On the Hyperparameters influencing a PINN's generalization beyond the training domain. arXiv e-print (arXiv:2302.07557). 2023.

I. García and R. Santana. Filter method-based feature selection process for unattributed-identity multi-target regression problem. techrxiv e-print (techrxiv.20430954.v2). 2023.

2022

R. H. R. Lima, D. Magallaes, A. Pozo, A. Mendiburu, and R. Santana. A Grammar-based GP approach applied to the design of deep neural networks. Genetic Programming and Evolvable Machines. Vol. 23, No. 3, Pp. 427-452. 2022.

p style="text-align: justify"R. Santana. An embedding space for SARS-CoV-2 epitope-based vaccines. EUROPEAN JOURNAL OF CLINICAL INVESTIGATION. Special Issue:56TH ANNUAL SCIENTIFIC MEETING, 8–10 June 2022, Bari, Italy. 56ASM-0280 S4 IS. Willey. 2022.

M. Murua, D. Galar, and R. Santana. Solving the multi-objective Hamiltonian cycle problem using a Branch-and-Fix based algorithm. Journal of Computational Science. Vol. 60, 101578, 10 pages. Elsevier. 2022.

N. Mei, D. Soto, and R. Santana. Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks. Nature Human Behavior. Vol. 6. No. 5. Pp. 720-731. https://doi.org/10.1038/s41562-021-01274-7. 2022.

J. Vadillo, R. Santana, and J. A. Lozano. Analysis of Dominant Classes in Universal Adversarial Perturbations. Knowledge-Based Systems. Vol. 236. Pp. 107719. Elsevier. 2022.

J. Vadillo and R. Santana. On the human evaluation of audio adversarial examples. Computers & Security. Vol. 112. Pp. 102495. Elsevier. 2022

U. Garciarena, J. Vadillo, A. Mendiburu, and R. Santana Adversarial perturbations for evolutionary optimization. In Proceedings of the 7th International Conference on Machine Learning, Optimization and Data. Lecture Notes in Computer Science. G. Nicosia, P. Pardalos, V. Ojha et al. Editors. Springer-Verlag. Vol. 13164. Pp. 408-422. 2022.

R. Santana and M. Murua. Hybrid branch-and-fix evolutionary approaches for the Hamiltonian cycle problem on directed graphs. In Proceedings of the EURO-2022 Conference. Espoo, Finland. Accepted for publication. 2022.

R. Santana and S. Shakya. Evolutionary approaches with adaptive operators for the bi-objective TTP. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI). Singapore. IEEE Press. Pp. 1202-1209. 2022.

R. Cosson, R. Santana, B. Derbel, and A. Liefooghe. Multi-objective NK Landscapes with Heterogeneous Objectives. In Proceedings of the 2022 Genetic and Evolutionary Conference (GECCO-2022). Boston, USA. Pp. 502-510. 2022.

R. Santana, A. Liefooghe, and B. Derbel. Boomerang-shaped Neural Embeddings for NK Landscapes. In Proceedings of the 2022 Genetic and Evolutionary Conference (GECCO-2022). Boston, USA. Pp. 858-866. 2022.

I. Garcia-Ribote, R. Santana, P. Mulroy, and L. Del-Rio-Etayo. Introducing guard smart meters: voltage predictions and its implications for smart LV grid operation. In Proceedings of the CIRED E-mobility and power distribution systems workshop (CIRED-2022). Porto, Portugal. Pp. 578-582. 2022.

N. Mei, R. Santana, and D. Soto. Assessing the brain representation of conscious and unconscious visual contents using encoding based representational similarity analysis. BioRxiv. The Preprint Server for Biology. 521727. 2022.

2021

U. Garciarena, A. Mendiburu and R. Santana. Towards automatic construction of multi-network models for heterogeneous multi-task learning. ACM Transactions on Knowledge Discovery from Data. Vol. 15. No. 2. Pp. 1-23. ACM. 2021.

M-J. García-Rodriguez, V. Rodriguez-Montequini, A. Aranguren-Ubierna, R. Santana, B. Sierra-Araujo, and A. Zelaia-Jauregi. Award price estimator for public procurement auctions using machine learning algorithms: case study with tenders from Spain. Studies in Informatics and Control. Vol. 30. No. 4. Pp. 1-23. 2021.

Z. Gonzalez-Arenas, J. C. Jimenez, L-V. Lozada-Chang and R. Santana. Estimation of distribution algorithms for the computation of innovation estimators of diffusion processes. Mathematics and Computers in Simulation. Vol. 187. Pp. 67-76. Elsevier. 2021.

M. S. R. Martins, M. El Yafrani, M. Delgado, R. Lueders, R. Santana, H. V. Siqueira, H. G. Akcay, and B. Ahiod. Analysis of Bayesian network learning techniques for a hybrid multi-objective Bayesian estimation of distribution algorithm: a case study on MNK landscape. Journal of Heuristics. Vol. 27. Pp. 549-573. Springer. 2021.

I. Roman, R. Santana, A. Mendiburu and J. A. Lozano. Evolving Gaussian Process kernels from elementary mathematical expressions for time series extrapolation. Neurocomputing. Vol. 462. Pp. 426-439. Elsevier. 2021.

I. Roman, R. Santana, A. Mendiburu and J. A. Lozano. Evolution of Gaussian Process kernels for post-editing effort estimation. Annals of Mathematics and Artificial Intelligence. Vol. 89. Pp.. 835-856. Springer. 2021.

C. Montenegro, R. Santana, and J. A. Lozano. Analysis of the sensitivity of the End-Of-Turn Detection task to errors generated by the Automatic Speech Recognition process. Engineering Applications of Artificial Intelligence. Elsevier. Vol. 100. Pp. 104189. 2021.

R. H. R. Lima, A. Pozo, A. Mendiburu, and R. Santana Automatic design of deep neural networks applied to image segmentation problems. In Proceedings of the EvoStar Conference. Lecture Notes in Computer Science. Springer. Sevilla-Spain. LNCS 12691. Pp. 98-113. 2021.

J. M. Olaso, A. Vazquez, L. Ben-Letaifa, M. deVelazco, M. L. Torres, C. Pickard, C. Glackin, G. Chollet, G. Calahane, C. Montenegro, A. López-Zorrilla, R. Justo, R. Santana, J. A. Lozano, J. Tenerio-Laranga, E. González-Fraile, B. Fernandez-Ruanova, G. Cordasco, A. Esposito, K. Beck-Gjellesvik, A. Torp Johansen, M. Stylianou-Kornes A. Mtibaa, M. A. Hmani, D. Petrovska-Delacretaz, P. Bauch, C. Palmero-Cantarino, S. Escalera, O. Gordeeva, O. Deroo, A. Fernández, D. Kyslitska, and S. Schloegl. The EMPATHIC Virtual Coach: a demo. In Proceedings of the 23rd ACM International Conference on Multimodal Interaction. Montreal, Canada. Pp. 848-851. ACM Press. 2021.

U. Garciarena, A. Mendiburu, and R. Santana. On the role of the gradient optimizer on evolved GAN. In Proceedings of the Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2021). Málaga, Spain. Accepted for publication. 2021.

J. Vadillo and R. Santana. Universal Adversarial Examples in Speech Command Classification. In Proceedings of the X Simposio Teoría y Aplicaciones de Minería de Datos (TAMIDA-2021). Málaga, Spain. Accepted for publication. 2021.

A. Bidaurrazaga, A. Pérez, and R. Santana. Looking for a Synergy between Boltzmann Machines and Markov Networks. In Proceedings of the Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA-2021). Doctoral Consortium. Málaga, Spain. Accepted for publication. 2021.

J. Vadillo, R. Santana, and Jose A. Lozano. PhD Thesis Proposal: Adversarial Machine Learning in the Audio Domain. In Proceedings of the Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA-2021). Doctoral Consortium. Málaga, Spain. Accepted for publication. 2021.

R. Santana. Semantic composition of word-embeddings with genetic programming. In: Yalaoui F., Amodeo L., Talbi EG. (eds) Heuristics for Optimization and Learning. Studies in Computational Intelligence. Vol. 906. Springer, Cham. Pp. 409-423. 2021.

I. García and R. Santana. Unified Framework for the Analysis of the Effect of Control Policies on Automatic Voltage Regulators. techrxiv e-print (techrxiv.15022611.v1). 2021.

J. Vadillo, R. Santana, and J. A. Lozano. When and How to Fool Explainable Models (and Humans) with Adversarial Examples. arXiv e-print (arXiv:2107.01943). 2021.

N. Mei, D. Soto, and R. Santana. Informative neural representations of unseen objects during higher-order processing in human brains and deep artificial networks. BioRxiv. The Preprint Server for Biology. 426428. 2021.

U. Garciarena, N. Lourenco, P. Machado, R. Santana, and A. Mendiburu. On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future. arXiv e-print (arXiv:2105.12836). 2021.

U. Garciarena, R. Santana, and A. Mendiburu. Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components. arXiv e-print (arXiv:2106.08972). 2021.

2020

U. Garciarena, A. Mendiburu and R. Santana. Analysis of the transferability and robustness of GANs evolved for Pareto set approximations. Neural Networks. Vol. 132, Pp. 281-296. 2020.

D. Soto, U. Ayub-Sheikh, N. Mei, and R. Santana. Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning. Royal Society Open Science Journal.Vol.7, No. 5, 192043. 2020.

T. Santana, J. Moreno, G. Petzold, R. Santana, and G. Saez-Trautmann. Evaluation of the Temperature and Time in Centrifugation-Assisted Freeze Concentration. Applied Sciences. Vol. 10, No. 24. Pp. 9130. 2020.

M. Murua, A. Suarez, D. Galar, and R. Santana. Tool-Path Problem in Direct Energy Deposition Metal-Additive Manufacturing: Sequence Strategy Generation. IEEE Access. Vol. 8,9093820, Pp. 91574-91585. 2020.

G. Sirbiladze, I. Khutsishvili, A. Sikharulidze, T. Manjapharashvili, and R. Santana. A new hesitant fuzzy TOPSIS approach in multi-attribute group decision making. Bulletin of the Georgian National Academy of Sciences. Vol. 14. No. 3. Pp. 17-22. 2020.

U. Garciarena, R. Santana, and A. Mendiburu. EvoFlow: A Python library for evolving deep neural network architectures in tensorflow. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI). Camberra, Australia. IEEE Press. Pp. 1-8. 2020.

H. S. Khargharia, R. Santana, S. Shakya, R. Ainslie, and G. Owusu. Investigating RNNs for vehicle volume forecasting in service stations. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI). Camberra, Australia. IEEE Press. Pp. 1-8. 2020.

J. Vadillo, R. Santana, and J. A. Lozano. Exploring Gaps in DeepFool in Search of More Effective Adversarial Perturbations. In Proceedings of the Sixth International Conference on Machine Learning, Optimization, and Data Science (LOD-2020). Tuscany, Italy. Lecture Notes in Computer Science, Vol. 12566. Springer, Cham. Pp. 215-227. 2020.

R. Lima, A. Pozo, A. Mendiburu and R. Santana. A Symmetric grammar approach for designing segmentation models. In Proceedings of the 2020 Congress on Evolutionary Computation (CEC-2020). Glasgow, UK. IEEE Press. Pp. 1-8. 2020.

U. Garciarena, A. Mendiburu, and R. Santana. Envisioning the Benefits of Back-Drive in Evolutionary Algorithms. In Proceedings of the 2020 Congress on Evolutionary Computation (CEC-2020). Glasgow, UK. IEEE Press. Pp. 1-8. 2020.

R. Santana and S. Shakya. Dynamic programming operators for bi-objective TTP problem. In Proceedings of the 2020 Congress on Evolutionary Computation (CEC-2020). Glasgow, UK. IEEE Press. Pp. 1-8. 2020.

C. Montenegro, R. Santana, and Jose A. Lozano. Transfer learning in hierarchical dialogue topic classification with neural networks. In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN-2020). Glasgow, UK. IEEE Press. Pp. 1-8. 2020.

U. Garciarena, A. Mendiburu, and R. Santana. Automatic Structural Search for Multi-TaskLearning VALP. In Proceedings of the International Workshop on Optimization and Learning: Challenges and Applications (OLA-2020). Cadiz, Spain. Communications in Computer and Information Science. CCIS Vol. 1173 CCIS. Pp. 25--36. 2020.

J. Vadillo and R. Santana. On the human evaluation of audio adversarial examples. arXiv e-print (arXiv:2001.08444). 2020.

I. Roman, A. Mendiburu, R. Santana, and J. A. Lozano. Bayesian Optimization approaches for massively multi-modal problems. In Proceedings of the Learning and Intelligent Optimization Conference (LION-2019). Chania. Greece. Lectures Notes in Computer Science. Volume 11968. Pp. 383--397. 2020.

I. Roman, R. Santana, A. Mendiburu, and J. A. Lozano. Evolving Gaussian Process kernels for translation editing effort estimation. In Proceedings of the Learning and Intelligent Optimization Conference (LION-2019). Chania. Greece. Lectures Notes in Computer Science. Volume 11968. Pp. 304--318. 2020.

J. Vadillo, R. Santana, and J. A. Lozano. Analysis of Dominant Classes in Universal Adversarial Perturbations. arXiv e-print (arXiv:2012.14352). 2020.

J. Vadillo, R. Santana, and J. A. Lozano. Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions. arXiv e-print (arXiv:2004.06383). 2020.

2019

J. Vadillo and R. Santana. Universal adversarial examples in speech command classification . arXiv e-print (arXiv:1911.10182). 2019.

D. Soto, U. Ayub-Sheikh, N. Mei, and R. Santana. Decoding and encoding models reveal how the depth of processing shapes the brain representation of meaning. BioRxiv. The Preprint Server for Biology. 830448. 2019.

R. Santana, Luis Marti, and Mengjie Zhang. GP-based methods for domain adaptation: Using brain decoding across subjects as a test-case. Genetic Programming and Evolvable Machines. Vol. 11. Pp. 385-411. 2019.

D. Carrera, L. Bandeira, R. Santana, and J. A. Lozano. Detection of sand dunes on Mars using a regular vine-based classification approach. Knowledge Based Systems. Vol 163. Pp. 858-874. 2019.

I. Roman, R. Santana, A. Mendiburu, and J. A. Lozano. An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization. IEEE Access. Vol. 7, 8936460, Pp. 184294--184302. 2019.

C. Montenegro, A. Lopez-Zorrilla, J. Mikel-Olaso, R. Santana, R. Justo, J. A. Lozano, M. I. Torres. A Dialogue-Act Taxonomy for a Virtual Coach Designed to Improve the Life of Elderly. Multimodal Technologies and Interaction. Vol 3. No. 3. Pp. 52. 2019.

U. Garciarena, A. Mendiburu, and R. Santana. Towards automatic construction of multi-network models for heterogeneous multi-task learning. arXiv e-print (arXiv:1903.09171). 2019.

D. K. S. Magalhaes, A. Pozo, and R. Santana. An empirical comparison of distance/similarity measures for Natural Language Processing. In Proceedings of the 2019 Encontro Nacional de Inteligencia Artificial e Computacional (ENIAC 2019). Salvador de Bahia, Brazil. Pp. 1--12. 2019.

M. Murua, D. Galar, and R. Santana. Adaptation of a Branching Algorithm to Solve Discrete Optimization Problems. In German Conference on Operation Research (OR2019). Dresden. Germany. Accepted for presentation. 2019.

I. Roman, R. Santana, A. Mendiburu, and J. A. Lozano. Sentiment analysis with genetically evolved Gaussian kernels. In Proceedings of the 2019 Genetic and Evolutionary Conference (GECCO-2019). Prague, Czech Republic. Pp. 1328--1336. 2019.

A. Cherriet and R. Santana. Optimizing permutation-based problems with a discrete vine-copula as a model for EDA. In Companion Proceedings of the 2019 Genetic and Evolutionary Conference (GECCO-2019). Prague, Czech Republic. Pp 143-144. 2019.

C. Montenegro, R. Santana, and J. A. Lozano. Data generation approaches for topic classification in multilingual spoken dialog system. In Proceedings of the PErvasive Technologies Related to Assistive Environments (PETRA) Conference. Rhodes, Greece. Pp. 211-217. 2019.

N. Mei, U. Sheikh, R. Santana, and D. Soto. How the brain encodes meaning: Comparing word embedding and computer vision models to predict fMRI data during visual word recognition. In 2019 Conference on Cognitive Computational Neuroscience. Berlin. Germany. Accepted for publication. 2019.

M. L. Torres, J. M. Olaso, C. Montenegro, R. Santana, A. Vazquez, R. Justo, J. A. Lozano, S. Schloegl, G. Chollet, N. Dugan, M. Irvine, N. Glackin, C. Pickard, A. Esposito, G. Cordasco, A. Troncone, D. Petrovska-Delacretaz, A. Mtibaa, M. A. Hmani, M. S. Korsnes, L. J. Martinussen, S. Escalera, C. Palmero-Cantarino, O. Deroo, O. Gordeeva, J. Tenerio-Laranga, E. Gonzalez-Fraile, B. Fernandez-Ruanova, and A. Gonzalez-Pinto. The EMPATHIC Project: Mid-term Achievements. In Proceedings of the PErvasive Technologies Related to Assistive Environments (PETRA) Conference. Rhodes, Greece. Pp. 629-638. 2019.

I. Roman, R. Santana, A. Mendiburu, and J. A. Lozano. Sentiment analysis with genetically evolved Gaussian kernels. arXiv e-print (arXiv:1904.00977). 2019.

I. Roman, R. Santana, A. Mendiburu, and J. A. Lozano. Evaluation of sentence embeddings transformations for estimating translation editing effort with Gaussian kernels. Accepted as poster presentation in IWCS Workshop on Vector Semantics for Dialogue and Discourse (VSDD). Gothenburg. Sweden. 2019.

2018

M. S. R. Martins, M. Delgado, R. Lueders, R. Santana, R. A. Goncalves, and C. P. de Almeida. Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: A comparative analysis for the multi-objective knapsack problem. Journal of Heuristics. Vol. 24. No. 1. Pp. 25-47. 2018.

E. Irurozki, J. Ceberio, J. Santamaria, R. Santana, and A. Mendiburu. Algorithm 989: perm_mateda: A Matlab Toolbox of Estimation of Distribution Algorithms for Permutation-based Combinatorial Optimization Problems. ACM Transactions on Mathematical Software (TOMS). Vol. 44(4): Pp. 1-47. 2018.

M. Murua, A. Suárez, N. López de Lacalle, R. Santana, and A. Wretland. Feature extraction based prediction of tool wear of Inconel 718 in face turning. Insight. Non-Destructive Testing and Condition Monitoring. Vol. 60. No. 8. Pp. 1-8 Publisher: The British Institute of Non-Destructive Testing. 2018.

A. Strickler, O. Rodrigues-Castro, A. Pozo, and R. Santana. An investigation of the selection strategies impact on MOEDAs: CMA-ES and UMDA. Applied Soft Computing. Vol. 62. Pp. 963-973. 2018.

M. S. R. Martins, M. Delgado, R. Lueders, R. Santana, R. A. Goncalves, and C. P. de Almeida. Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm. Applied Soft Computing. Vol. 73. Pp. 328-343. 2018.

D. Carrera, R. Santana and J. A. Lozano. The Relationship Between Graphical Representations of Regular Vine Copulas and Polytrees. In Proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU-2018). Cadiz, Spain. Pp. 678-690. 2018.

U. Garciarena, R. Santana and A. Mendiburu. Evolved GANs for generating Pareto set approximations. In Proceedings of the 2018 Genetic and Evolutionary Conference (GECCO-2018) . Kyoto, Japan. Pp. 434-441. 2018.

U. Garciarena, R. Santana and A. Mendiburu. Expanding variational autoencoder for learning and exploiting latent representations in search distributions. In Proceedings of the 2018 Genetic and Evolutionary Conference (GECCO-2018). Kyoto, Japan. Pp. 849-856. 2018.

A. Cherriet and R. Santana. Modeling dependencies between decision variables and objectives with copula models. In Companion Proceedings of the 2018 Genetic and Evolutionary Conference (GECCO-2018). Kyoto, Japan. Pp. 175-176. 2018.

M. S. R. Martins, M. El-Yafrani, R. Santana, M. Delgado, R. Lueders, and B. Ahiod. On the performance of multi-objective estimation of distribution algorithms for combinatorial problems. In Proceedings of the 2018 Congress on Evolutionary Computation (CEC-2018). Rio de Janeiro, Brazil. IEEE Press. Pp. 1-8. 2018.

U. Garciarena, R. Santana, and A. Mendiburu. Analysis of the complexity of the automatic pipeline generation problem. In Proceedings of the 2018 Congress on Evolutionary Computation (CEC-2018). Rio de Janeiro, Brazil. IEEE Press. Pp. 1-8. 2018.

U. Garciarena, A. Mendiburu, and R. Santana. Towards a more efficient representation of imputation operators in TPOT. arXiv e-print (arXiv:1801.04407). 2018.

Z. Gonzalez, J. C. Jimenez, L-V. Lozada-Chang, and R. Santana . An estimation of distribution algorithm for the computation of innovation estimators of diffusion processes. arXiv e-print (arXiv:1804.02459). 2018.

2017

R. Santana. Gray-box optimization and factorized distribution algorithms: where two worlds collide. arXiv e-print (arXiv:1707.03093v1). 2017.

Unai Garciarena, R. Santana, and Alexander Mendiburu. Evolving imputation strategies for missing data in classification problems with TPOT. arXiv e-print (arXiv:1706.01120v1). 2017.

R. Santana. Reproducing and learning new algebraic operations on word embeddings using genetic programming. arXiv e-print (arXiv:1702.05624v1). 2017.

Unai Garciarena and R. Santana. An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers. Expert Systems and Applications. Vol. 89. Pp. 52-65. 2017.

O. Rodrigues-Castro, A. Pozo, J. A. Lozano, and R. Santana. An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study. Swarm Intelligence. Vol. 11. No. 2. Pp. 101-130. 2017.

M. Zangari, A. Pozo, R. Santana, and A. Mendiburu. A decomposition-based binary ACO algorithm for the multiobjective UBQP. Neurocomputing. Vol. 246. Pp. 58-68. 2017.

M. Zangari, A. Mendiburu, R. Santana, and A. Pozo. Multiobjective decomposition-based Mallows Models estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem. Information Sciences. Vol. 397-398. Pp. 137-154. 2017.

O. Rodrigues-Castro, A. Pozo, J. A. Lozano, and R. Santana. Transfer weight functions for injecting problem information in the Multi-Objective CMA-ES. Memetic Computing.Vol. 9, No. 2, Pp. 153-180. 2017.

M. Zangari, R. Santana, A. Mendiburu, and A. Pozo. Not all PBILs are the same: Unveiling the different learning mechanisms of PBIL variants. Applied Soft Computing. Vol. 53. Pp. 88-96. 2017.

R. Santana and J. A. Lozano. Different scenarios for survival analysis of evolutionary algorithms. In Proceedings of the 2017 Genetic and Evolutionary Conference (GECCO-2017), Berlin, Germany. Pp. 825-832. 2017.

R. Santana, G. Sirbiladze, B. Ghvaberidze and B. Matsaberidze. A comparison of probabilistic-based optimization approaches for vehicle routing problems. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC-2017), San Sebastian, Spain. IEEE Press. Pp. 2606-2613. 2017.

V. Fontoura, A. Pozo and R. Santana. Automated Design of Hyper-Heuristics Components to Solve the PSP Problem with HP Model. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC-2017), San Sebastian, Spain. IEEE Press. Pp. 1848-1855. 2017.

O. Rodrigues-Castro, J. A. Lozano, R. Santana and A. Pozo. Combining CMA-ES and MOEA/DD for many-objective optimization. In Proceedings of the 2017 Congress on Evolutionary Computation (CEC-2017), San Sebastian, Spain. IEEE Press. Pp. 1451-1458. 2017.

M. S. R. Martins, M. Delgado, R. Lueders, R. Santana, R. A. Goncalves, and C. P. de Almeida. Probabilistic analysis of Pareto front approximation for a hybrid multi-objective Bayesian estimation of distribution algorithm. In Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS 2017), Uberlandia, Brazil. Accepted for publication. 2017.

R. H. R. Lima, V. Fontoura, A. Pozo and R. Santana. Multi-objective approach to the Protein Structure Prediction Problem. Chapter 8 in Evolutionary Multi-Objective System Design: Theory and Applications. Computer and Information Science Series. Chapman & Hall/CRC. In Press. 2017.

2016

D. Carrera, R. Santana, and J. A. Lozano. Vine copula classifiers for the mind reading problem. Progress in Artificial Intelligence. Vol 5 (4). Pp. 289-305. 2016.

O. Rodrigues-Castro, R. Santana, A. Pozo. C-Multi: a competent multi-swarm approach for many-objective problems. Neurocomputing. Vol. 180, Pp. 68-78. 2016.

R. Santana, A. Mendiburu, and J. A. Lozano. A review of message passing algorithms in estimation of distribution algorithms. Natural Computing. Vol. 15 (1), Pp. 165-180. 2016.

A. Astigarraga, A. Arruti, J. Muguerza, R. Santana, J. I. Martin, and B. Sierra. User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection based on Estimation of Distributed Algorithms. Mathematical Problems in Engineering. Article ID 1435321, 12 pages. 2016.

S. Picek, R. Santana, D. Jakobovic. Maximal Nonlinearity in Balanced Boolean Functions with Even Number of Inputs, Revisited. In Proceedings of the 2016 Congress on Evolutionary Computation (CEC-2016), Vancouver, Canada. IEEE Press. Pp. 3222-3229. 2016.

M. Zangari, R. Santana, A. Mendiburu, and A. Pozo. On the design of hard mUBQP instances. In Proceedings of the The 2016 Genetic and Evolutionary Conference (GECCO-2016), Denver, CO., USA. Pp. 421-428. 2016.

R. Santana, Z. Zhu, and H. Katzgraber. Evolutionary approaches to optimization problems in Chimera topologies. In Proceedings of the The 2016 Genetic and Evolutionary Conference (GECCO-2016), Denver, CO., USA. . Pp. 397-404. 2016.

M. S. R. Martins, M. Delgado, R. Santana, R. Lueders, R. A. Goncalves, and C. P. de Almeida. HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm. In Proceedings of the The 2016 Genetic and Evolutionary Conference (GECCO-2016), Denver, CO., USA. Pp 357-364. 2016.

U. Garciarena and R. Santana. Evolutionary optimization of compiler flag selection by learning and exploiting flags interactions. In Workshop on the Repair and Optimisation of Software using Computational Search (Genetic Improvement - 2016). Companion proceedings of the 2016 Genetic and Evolutionary Conference (GECCO-2016), Denver, CO., USA. Pp. 1159-1166. 2016.

A. Strickler, O. Rodrigues-Castro, R. Santana, and A. Pozo. Investigating selection strategies in multi-objective probabilistic model based algorithms. In Proceedings of the 2016 5th Brazilian Conference on Intelligent Systems (BRACIS 2016). Recife, Pernambuco. Pp. 7-12. IEEE press. 2016.

2015

C. Echegoyen, R. Santana, A. Mendiburu, and J. A. Lozano. Comprehensive Characterization of the Behaviors of Estimation of Distribution Algorithms. Theoretical Computer Science. Accepted for publication. 2015.

R. Santana, A. Mendiburu, and J. A. Lozano. Multi-view classification of psychiatric conditions based on saccades. Applied Soft Computing. Vol. 31. Pp. 308-316. 2015.

S. Picek, B. McKay, R. Santana, T. Gedeon. Fighting the Symmetries: The Structure of Cryptographic Boolean Function Spaces. In Proceedings of the The 2015 Genetic and Evolutionary Conference (GECCO-2015), Madrid, Spain. Pp. 457-464. 2015.

R. Santana, A. Mendiburu, and J. A. Lozano. Multi-objective NM-landscapes. In Proceedings of the The 2015 Genetic and Evolutionary Conference (GECCO-2015), Madrid, Spain. Pp. 1477-1478. 2015.

W.-L Zheng, R. Santana, B.-L Lu. Comparison of Classification Methods for EEG-based Emotion Recognition. In World Congress on Medical Physics and Biomedical Engineering , Toronto, Canada. Pp. 1184-1187. 2015.

J. Ceberio, R. Santana, A. Mendiburu, and J. A. Lozano. Mixtures of Generalized Mallows models for solving the Quadratic Assignment Problem. In Proceedings of the 2015 Congress on Evolutionary Computation (CEC-2015), Sendai, Japan. IEEE Press. Pp. 2050-2057. 2015.

R. Santana, A. Mendiburu, and J. A. Lozano. Evolving MNK-landscapes with structural constraints. In Proceedings of the 2015 Congress on Evolutionary Computation (CEC-2015), Sendai, Japan. IEEE Press. Pp. 1364-1371. 2015.

R. Santana. Supervised classification of vowel speech imagery. In Memorias Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA-2015), Albacete, Spain. Accepted for publication. 2015.

G. Fritsche, A. Strickler, A. Pozo, and R. Santana. Capturing Relationships in Multi-Objective Optimization. In Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS 2015), Natal, Brazil. Accepted for publication. 2015.

J. Santamaría, J. Ceberio, R. Santana, A. Mendiburu, and J. A. Lozano. Introducing Mixtures of Generalized Mallows in Estimation of Distribution Algorithms. In Proceedings of the X Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2015), Merida, Spain. Pp. 97-102. 2015.

I. Roman, R. Santana, A. Mendiburu, and J. A. Lozano. Kernel hautapen dinamikoa Optimizazio Bayesiarrean. Ikertzaile Euskaldunen Lehen Kongresua, Durango, Spain. Accepted for presentation. 2015.

R. Santana, A. Mendiburu, and J. A. Lozano. Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions. In Memorias Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA-2015), Albacete, Spain. Accepted for publication. 2015.

M. Zangari, R. Santana, A. Mendiburu, and A. Pozo. PBIL: un mismo nombre para distintos algoritmos. Un caso de estudio sobre un problema de optimización multi-objetivo. In Memorias de las II Jornadas sobre Algoritmos Evolutivos y Metaheuristicas (JAEM 2015), Albacete, Spain. Accepted for publication. 2015.

V. D. Da Fontoura, R. Remes de Lima, A. Pozo, and R. Santana. Algoritmos multiobjetivos aplicados ao problema de predicao de estruturas proteínas. In Memorias del 12 Congresso Brasileiro de Inteligencia Computacional (CBIC 2015), Curitiba, Brazil. Accepted for publication. 2015.

2014

H. Karshenas, R. Santana, C. Bielza, and P. Larrañaga. Multi-objective estimation of distribution algorithm based on joint modeling of objectives and variables. IEEE Transactions on Evolutionary Computation. Vol. 18. No. 4. Pp. 519-542. 2014. IEEE press.

R. Santana, A. Mendiburu, and J. A. Lozano. Customized selection in estimation of distribution algorithms. In Proceedings of the Tenth International Conference on Simulated Evolution And Learning (SEAL-2014). Dunedin, New Zealand. Lecture Notes in Computer Science Volume 8886, pp 94-105. 2014

R. Santana, R. B. McDonald, and H. G. Katzgraber. A probabilistic evolutionary optimization approach to compute quasiparticle braids." In Proceedings of the Tenth International Conference on Simulated Evolution And Learning (SEAL-2014). Dunedin, New Zealand. Lecture Notes in Computer Science Volume 8886, pp 13-24. 2014.

Ibai Roman, R. Santana, A. Mendiburu, and J. A. Lozano. Dynamic Kernel Selection Criteria for Bayesian Optimization. In Accepted for presentation at NIPS 2014 workshop on Bayesian optimization (NIPS-BayesOpt-2014). Nevada, USA.

2013

R. Santana, L. McGarry, C. Bielza, P. Larrañaga, and R. Yuste. Classification of neocortical interneurons using affinity propagation. Frontiers in Neural Circuits. 2013. Accepted for publication.

R. Santana, R. Armañanzas, C. Bielza, and P. Larrañaga. Network measures for information extraction in evolutionary algorithms. International Journal of Computational Intelligence Systems. Vol. 6. No. 6. Pp. 1163-1188. 2013.

C. Echegoyen, A. Mendiburu, R. Santana, and J. A. Lozano. On the Taxonomy of Optimization Problems under Estimation of Distribution Algorithms. Evolutionary Computation. Vol. 3. Number 3. Pp. 471-495. 2013.

H. Karshenas, R. Santana, C. Bielza, and P. Larrañaga. Regularized Continuous Estimation of Distribution Algorithms. Applied Soft Computing. Vol. 13. No. 5. Pp. 2412-2432. 2013.

P. Larrañaga, H. Karshenas, C. Bielza, and R. Santana. A Review on Evolutionary Algorithms in Bayesian Network Learning and Inference Tasks. Information Sciences. Vol. 233. No. 1. Pp. 109-125. 2013.

R. Santana, A. Mendiburu, and J. A. Lozano. Analyzing probabilistic models generated by EDAs for simplified protein folding problems. In Proceedings of the NIPS Workshop on Constructive Machine Learning (NIPS-CML-2013). Nevada, USA. Accepted for publication.

R. Santana, A. Mendiburu, and J. A. Lozano. Extending the use of message passing algorithms to problems with unknown structure. In Accepted for presentation at NIPS 2013 workshop on Bayesian optimization (NIPS-BayesOpt-2013). Nevada, USA.

R. Santana. Multi-objective optimization approach to detecting extremal patterns in social networks. In Proceedings of the Third World Congress on Information and Communication Technologies (WICT-2013), Hanoi, Vietnam. Accepted for publication. IEEE-Press 2013.

R. Santana, A. Mendiburu, and J. A. Lozano. Model-based template-recombination in Markov network estimation of distribution algorithms for problems with discrete representation. In Proceedings of the Third World Congress on Information and Communication Technologies (WICT-2013), Hanoi, Vietnam. Accepted for publication. IEEE-Press 2013.

R. Santana, C. Bielza, and P. Larrañaga. Changing conduction delays to maximize the number of polychronous groups with an estimation of distribution algorithm. Technical Report UPM-FI/DIA/2013-1, Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid. September, 2013.

R. Santana, A. Mendiburu, and J. A. Lozano. Critical issues in model-based surrogate functions in estimation of distribution algorithms.. In Proceedings of the International Conference on Swarm Evolutionary and Memetic Computing (SEMCCO-2013), Chennai, India. Lecture Notes in Computer Science. Volume 8298. pp 1-13. 2013.

R. Santana, A. Mendiburu, and J. A. Lozano. Message passing methods for estimation of distribution algorithms based on Markov networks.. In Proceedings of the International Conference on Swarm Evolutionary and Memetic Computing (SEMCCO-2013), Chennai, India. Lecture Notes in Computer Science. Volume 8298. pp 419-430. 2013.

R. Santana, R. I. McKay, and J. A. Lozano. Symmetry in evolutionary and estimation of distribution algorithms. In Proceedings of the 2013 Congress on Evolutionary Computation (CEC-2013), Cancun, Mexico. IEEE Press. Pp. 2053-2060. 2013.

2012

S. Shakya and R. Santana. (Eds.). Markov Networks in Evolutionary Algorithms. Adaptation, Learning and Optimization series. Vol. 14. Springer. 2012.

R. Santana, C. Bielza, and P. Larrañaga. Regularized logistic regression and multi-objective variable selection for classifying MEG data. Biological Cybernetics. Vol. 106. No. 6-7. Pp. 389-405. 2012.

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. 2012. Vol. 16. No. 2. Pp. 173-189.

P. Larrañaga, H. Karshenas, C. Bielza, and R. Santana. A review on probabilistic graphical models in evolutionary computation. Journal of Heuristics. Vol. 18. No. 5. Pp. 785-819. 2012.

S. Shakya, R. Santana, and J. A. Lozano. A Markovianity based Optimisation Algorithm. Genetic Programming and Evolvable Machines. Springer. Vol. 13. No. 2. Pp. 159-195. 2012

R. Santana, C. Bielza, and P. Larrañaga. Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models. BMC Neuroscience (Suppl). Vol. 13. Suppl. 1. P100. 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. Pp. 1159-1166. 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. Pp. 1473-1474. 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. Pp. 1499-1500. 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 2012 Congress on Evolutionary Computation (CEC-2012), Brisbane, Australia. IEEE Press. Pp. 3484-3491. 2012. (Best CEC Paper)

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 2012 Congress on Evolutionary Computation (CEC-2012), Brisbane, Australia. IEEE Press. Pp. 3221--3228. 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. 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. 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. 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. 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. 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. 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.

H. Karshenas R. Santana, C. Bielza, and P. Larrañaga. Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables. Technical Report UPM-FI/DIA/2012-2, Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid. September, 2012.

R. Santana, A. Mendiburu, and J. A. Lozano. New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination. Technical Report EHU-KZAA-TR-5/2012, Department of Computer Science and Artificial Intelligence, University of the Basque Country, December 2012.

R. Santana, A. Mendiburu, and J. A. Lozano. Using network measures to test evolved NK-landscapes. Technical Report EHU-KZAA-TR-3/2012, Department of Computer Science and Artificial Intelligence, University of the Basque Country, July 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 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.

C. Echegoyen, Q. Zhang, A. Mendiburu, R. Santana, and J. A. Lozano. Analyzing limits of effectiveness in different implementations of estimation of distribution algorithms. Technical Report EHU-KZAA-TR-2/2011, Department of Computer Science and Artificial Intelligence, University of the Basque Country, January 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

R. Santana, A. Mendiburu, N. Zaitlen, E. Eskin and J. A. Lozano. Multi-marker tagging SNP selection using estimation of distribution algorithms. Artificial Intelligence in Medicine. Vol. 5 No. 3. Pp. 193-201. 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 2010 Congress on EvolutionaryComputation (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 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.

R. Santana, C. Bielza, and P. Larrañaga. Using probabilistic dependencies improves the search of conductance-based compartmental neuron models. In Proceedings of the Seventh European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EVO-BIO). Lecture Notes in Computer Science. Springer-Verlag Istambul-Turkey. Accepted for publication. 2010.

R. Santana, C. Bielza, P. Larrañaga, J. A. Lozano, C. Echegoyen, A. Mendiburu,   R. Armañanzas, and S. Shakya. Mateda2.0: Estimation of distribution algorithms in MATLAB. Journal of Statistical Software. Vol. 35 No. 7 Pp. 1-30. 2010.

C. Echegoyen, A. Mendiburu, R. Santana, and J. A. Lozano. Analyzing the k Most Probable Solutions in EDAs based on Bayesian Networks. In Exploitation of linkage learning in evolutionary algorithms. Evolutionary Learning and Optimization. Springer. Y.-P. Chen editor.

R. Santana, P. Larrañaga, and J. A. Lozano. Learning factorizations in estimation of distribution algorithms using affinity propagation. Evolutionary Computation. Vol. 18. No. 4. Pp. 515-546. 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.

R. Santana, P. Larrañaga, and J. A. Lozano. Side chain placement using estimation of distribution algorithms. Artificial Intelligence in Medicine. 39(1). 2007. Pp. 49-63.

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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