Selection of Publications

Neural Computing

Becerra, J.A. and Santos, J. (2005), “Neural clustering analysis of macroevolutionary and genetic algorithms in the evolution of robot controllers”, Artificial Intelligence and Knowledge Engineering Applications - LNCS 3562:415-424.

Duro, R.J. and Santos, J. (2003), “Modeling Temporal Series Through Synaptic Delay Based Neural Networks”, Neural Computing and Applications 11:224-237. Web.

Duro, R.J. and Santos, J. (2002), “Chaotic time series prediction with discrete time backpropagation”, Artificial Neural Networks in Pattern Recognition, J.M. Corchado, L. Alonso, C. Fyfe (Eds.), pp. 103-115, Univ. of Paisley, UK.

Santos, J. and Duro, R.J. (2001), “Pi Units in Temporal Time Delay Based Networks Trained with Discrete Time Backpropagation”, International Journal of Computers, Systems and Signals, Vol. 2, No. 1, pp. 31-42.

Santos, J. and Duro, R.J. (2001), “Influence of Noise on Discrete Time Backpropagation Trained Networks”, Neurocomputing, Vol 41, No. 1-4, pp. 67-89. Web.

Duro, R.J., and Santos, J. (1999), “Discrete Time Backpropagation for Training Synaptic Delay Based Artificial Neural Networks”, IEEE Transactions on Artificial Neural Networks 10(4):779-789. Web.

Becerra, J.A., Santos, J. and Duro, R.J. (2002), “Self Pruning Gaussian Synapse Networks for Behavior Based Robots”, Artificial Neural Networks - Lecture Notes in Computer Science, Vol. 2415, pp. 837-843, Springer-Verlag.

Santos, J. and Duro, R.J (2001), “Π-DTB, Discrete Time Backpropagation with Product Units”, Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence - Lecture Notes in Computer Science 2084:207-214.

Duro, R.J., Crespo, J.L., and Santos, J. (1999), “Training Higher Order Gaussian Synapses”, Foundations and Tools for Neural Modeling, Lecture Notes in Computer Science, pp. 537-545, Vol. 1606, Springer-Verlag, Berlín 1999.

Duro, R.J., Santos, J., Becerra, J.A., Bellas, F., Crespo, J.L. (2000), “Using Higher Order Synapses and Nodes to Improve the Sensing Capabilities of Mobile Robots”, Procc. European Symposium on Artificial Neural Networks (ESANN 2000), pp. 81-88.

Crespo, J.L., Santos, J. and Duro, R.J. (2000), “Robust visual recognition with high-order Gaussian synapses networks”, International Joint Conference on Artificial Neural Networks (IJCNN’2000), Vol. VI:135-139.

Duro, R.J., Crespo, J.L., Santos, J. (1999), “Training Higher Order Gaussian Synapses”, Foundations and Tools for Neural Modeling - Lecture Notes in Computer Science 1606: 537-545.

Becerra, J.A., Santos J., Duro, R.J. (1999), “Using Temporal Information in ANNs for the Implementation of Autonomous Robot Controllers”, Engineering Applications of Bio-Inspired Artificial Neural Networks - Lecture Notes in Computer Science 1607:540-547.

Crespo J.L, Becerra, J.A., Duro, R.J., Santos J. (1999), “Visual Tracking in a Real Robot through Higher Order Synapses”, Wiener´s Cybernetics, 50 Years of Evolution, pp. 167-169.

Duro, R.J., Santos J. (1998), “Discrete Time Backpropagation and Synaptic Delay Based Artificial Neural Networks in Chaotic Time Series Prediction”, Perspectives in Neural Computing, Vol. 2:821-826.

Santos, J., Cabarcos, M., Otero, R.P., and Mira, J. (1997), “Parallelization of Connectionist Models Based on a Symbolic Formalism”, Biological and Artificial Computation: From Neuroscience to Technology, J. Mira, R. Moreno-Díaz and J. Cabestany (Eds.), Lecture Notes in Computer Science, Vol. 1240, pp. 304-312, Springer-Verlag.

Santos, J., Lorenzo, D., Gómez, S., Heras, J., and Otero, R.P. (1997), “Knowledge Refinement of an Expert System Using a Symbolic-connectionist Approach”, Artificial Intelligence in Medicine, E. Keravnou., C. Garbay, R. Baud, and J. Wyatt (Eds.), Lecture Notes in Artificial Intelligence, Vol. 1211, pp. 517-520, Springer-Verlag.

Santos, J., Duro, R.J. (1997), “Evolutionary Design of ANN Architectures for the Detection of Patterns in Signals”, Proceedings of FEA’97-Frontiers in Evolutionary Computation, Vol 1:100- 103.

Duro, R.J., Santos J. (1997), “Synaptic Delay Based Artificial Neural Networks and Discrete Time Backpropagation Applied to QRS Complex Detection”, Proceedings of International Conference on Neural Networks ICNN97, Vol. 4: 2566-2570.

Santos, J., Duro, R.J. (1997), “Design of ANN Architectures for Handling the Temporal Dimension in Signal Processing”, Computer Aided Systems Theory - Lecture Notes in Computer Science 1333:486-497.

Duro, R.J., Santos, J. (1997), “ECG Beat Classification with Synaptic Delay Based Artificial Neural Networks”, Biological and Artificial Computation: From Neuroscience to Technology - Lecture Notes in Computer Science Vol. 1240:962-970.

Santos, J., Duro, R.J., Gómez, A. (1995), “Synaptic Modulation Based Artificial Neural Networks”, From Natural to Artificial Neural Computation - Lecture Notes in Computer Science 930: 31-36.

Santos, J., Otero, R.P., and Mira, J. (1995), “NETTOOL: A Hybrid Connectionist-Symbolic Development Environment”, From Natural to Artificial Neural Computation, Lecture Notes in Computer Science, Vol. 930, pp. 658-665, Springer-Verlag.

Santos, J., Otero, R.P. (1993), “Connectionist Models for Syllabic Recognition in the Time Domain”, New Trends in Neural Computation - Lecture Notes in Computer Science 686:149-154.

Evolutionary Computing and Evolutionary Robotics

Santos, J. and Fernández, P. (2017), “Evolved synaptic delay based neural controllers for walking patterns in hexapod robotic structures”, Natural Computing. 16(2): 201-211 Web.

Santos, J. and Fernández, P. (2015), “Evolution of synaptic delay based neural controllers for implementing central pattern generators in hexapod robotic structures”, Proceedings International Work-Conference on the Interplay between Natural and Artificial Computation, LNCS 9108:30-40. Web

Santos, J. (2013), “Evolved center-crossing recurrent synaptic delay based neural networks for biped locomotion control”, Proceedings IEEE Congress on Evolutionary Computation - IEEE-CEC 2013, 142-148. Web

Palacios Leyva, R., Ricardo Cruz Alvarez, V.R., Montes-Gonzalez, F., Rascon Perez, L. and Santos, J. (2013), “Combination of reinforcement learning with evolution for automatically obtaining robot neural controllers”, Proceedings IEEE Congress on Evolutionary Computation - IEEE-CEC 2013, 119-126. Web

Cruz-Álvarez, V.R., Montes González, F., Mezura-Montes, E. and Santos, J. (2012), “Robot behavior implementation using two different Differential Evolution approaches”, Proc. MICAI 2012 - 11th Mexican International Conference on Artificial Intelligence, LNAI 7629: 216-226. Web

Santos, J. and A. Campo, A. (2012), “Biped locomotion control with evolved adaptive center-crossing continuous time recurrent neural networks”, Neurocomputing 86(1):86-96. Web.

Campo, A. and Santos, J. (2010), “Evolution of adaptive center-crossing continuous time recurrent neural networks for biped robot control”, Procc. European Symposium on Artificial Neural Networks (ESANN 2010), pp. 535-540.

Illobre, A., Gonzalez, J., Otero, R.P. and Santos, J. (2010), “Learning Action Descriptions of Opponent Behaviour in the Robocup 2D Simulation Environment”, Proc. ILP 2010 - The 20th International Conference on Inductive Logic Programming, Lecture Notes in Artificial Intelligence 6489:105-113.

Montes González, F., Santos, J. and Figueroa, H.R (2006), “Integration of Evolution with a Robot Action Selection Model”. Advances in Artificial Intelligence - Lecture Notes in Computer Science 4293:1160-1170.

Montes González, F. and Santos, J. (2005), “Evolving Robot Behavior for Centralized Action Selection”. Proceedings of the Fourth Mexican International Conference on Artificial Intelligence (MICAI 2005) - Advances in Artificial Intelligence Applications 213-222.

Becerra, J.A., Bellas, F., Santos, J., and Duro, R.J. (2005), “Complex Behaviours Through Modulation in Autonomous Robot Control”, Computational Intelligence and Bioinspired Systems - Lecture Notes in Computer Science 3512:717-724.

Becerra, J.A. and Santos, J. (2005), “Neural Clustering Analysis of Macroevolutionary and Genetic Algorithms in the Evolution Robot Controllers”, Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach - Lecture Notes in Computer Science, Vol. 3562, pp. 415-424, Springer Verlag.

J. Santos (2005), “El Efecto Baldwin en la Interrelación entre Evolución y Aprendizaje”, Revista Iberomericana de Inteligencia Artificial 9(27):21-34. Web.

Becerra, J.A., Santos, J. and Duro, R.J. (2004), “Robot Controller Evolution with Macroevolutionary Algorithms”, Information Processing with Evolutionary Algorithms (M. Graña, R.J. Duro, A. d’Anjou & P. Wang (Eds.), pp. 117-127, Springer Verlag.

Duro, R.J., Santos, J. and Becerra, J.A. (2003), “Some approaches for reusing behavior based robot cognitive architectures obtained through evolution”, Biologically Inspired Robot Behavior Engineering. Vol. 109, pp. 239-259, Physica Verlag (Springer Verlag).

Becerra, J.A., Santos, J. and Duro, R.J. (2003), “Multimodule Artificial Neural Network Architectures for Autonomous Robot Control Through Behavior Modulation”, Artificial Neural Nets. Problem Solving Methods - Lecture Notes in Computer Science 2687:169-176.

Becerra, J.A., Santos, J. and Duro, R.J. (2002), “MA vs. GA in low population evolutionary processes with mostly flat fitness landscapes”, Proc. 6th Joint Conference on Information Sciences- Frontiers in Evolutionary Algorithms, 626-630.

Santos, J., Duro, R.J., Becerra, J.A., Crespo, J.L. and Bellas, F. (2001), “Considerations in the Application of Evolution to the Generation of Robot Controllers”, Information Sciences 133:127-148. Web.

Duro, R.J., Becerra, J.A. and Santos, J. (2001), “Behavior reuse and virtual sensors in the evolution of complex behavior architectures”, Theory in Biosciences 120:188-206. Web.

Duro, R.J., Becerra, J.A., and Santos, J. (2000), “Evolving ANN Controllers for Smart Mobile Robots”, Future Directions for Intelligent Information Systems and Information Sciences, Nikola Kasabov (Ed.), pp. 34-64, Physica Verlag.

Duro, R.J., Santos, J., Bellas, F., and Lamas, A. (2000), “On Line Darwinist Cognitive Mechanism for an Artificial Organism”, Procc. Sixth International Conference on the Simulation of Adaptive Behavior (SAB2000), pp. 215-224.

Duro, R.J., Becerra, J.A. and Santos, J. (2000), “Improving Reusability of Behavior based Robot Cognitive Architectures obtained through Evolution”, Advanced Space Technologies for Robotics and Automation (ASTRA 2000).

Bellas, F., Becerra, J.A., Santos, J., Duro, R.J. (2000), “Applying Synaptic Delays for Virtual Sensing and Actuation in Mobile Robots”, Procc. International Joint Conference on Artificial Neural Networks, Vol. VI, pp 144-149.

Santos, J., Duro, R.J., Becerra, J.A., Crespo, J.L., Bellas, F. (2000), “Aspects of Evolution for Obtaining Real Robot Controllers”, Proceedings of Frontiers in Evolutionary Algorithms (FEA 2000), Vol 1:1021-1026.

Becerra, J.A., Santos, J., Duro, R.J. (1999), “Progressive Construction of Compound Behavior Controllers for Autonomous Robots Using Temporal Information”. Advances on Artificial Life - Lecture Notes in Artificial Intelligence 1674:324-328.

Becerra, J.A., Crespo, J.L., Santos J., Duro, R.J. (1999), “Incremental Design of Neural Controllers for Infrasensorized Autonomous Robots”, Wiener´s Cybernetics, 50 Years of Evolution, pp. 163-166.

Santos, J., and Duro, R.J. (1998), “Evolving Neural Controllers for Temporally Dependent Behaviors in Autonomous Robots (1998)”, Tasks and Methods in Applied Artificial Intelligence, A.P. del Pobil, J. Mira and M. Ale (Eds.), Lecture Notes in Artificial Intelligence, pp. 319-328, Vol. 1416, Springer-Verlag.

Becerra, J.A., Santos, J. and Duro, R.J. (1999), “Progressive construction of compound behavior controllers for autonomous robots using temporal information”, Proc 5th European Conference on Artificial Life (ECAL99)-Lecture Notes in Artificial Intelligence 1674:324-328.

Duro, R.J., Santos, J., Sarmiento, A. (1996), “GENIAL: An Evolutionary Recurrent Neural Network Designer and Trainer”, Computer Aided Systems Theory - Lecture Notes in Computer Science 1105:295-301.

Santos, J., Duro, R.J. (1994), “Evolutionary Generation and Training of Recurrent Artificial Neural Networks”, Proceedings IEEE World Congress on Computational Intelligence.

Vol. I: 759-763.

Artificial vision

Sierra, C.V., Novo, J., Santos, J. and Penedo, M.G. (2014), “Using evolved artificial neural networks for providing an emergent segmentation with an active net model”, Recent Advances in Knowledge-Based Paradigms and Applications - Advances in Intelligent Systems and Computing (Eds. Jeffrey W. Tweedale and Lakhmi C. Jain), Vol. 234: 57-72. Web

Novo, J., Santos, J. and Penedo, M.G. (2013), “Multiobjective differential evolution in the optimization of topological active models”, Applied Soft Computing 13: 3167-3177. Web.

Sierra, C.V., Novo, J., Santos, J. and Penedo, M.G. (2013), “Emergent segmentation of topological active nets by means of evolutionary obtained artificial neural networks”, Proceedings of ICAART 2013 - 5rd International Conference on Agents and Artificial Intelligence, Vol 2:44-50.

Sierra, C.V., Novo, J., Santos, J. and Penedo, M.G. (2012), “Evolved artificial neural networks for controlling Topological Active Nets deformation and for medical image segmentation”, Proceedings KES 2012 - 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. Advances in Knowledge-Based and Intelligent Information and Engineering Systems, IOS Press., 1380-1389. Web

Novo, J., Barreira, N., Penedo, M.G. and Santos, J. (2012), “Topological active volume 3D segmentation model optimized with genetic approaches”, Natural Computing. Web.

Novo, J., Santos, J. and Penedo, M.G. (2011), “Differential evolution optimization of 3D topological active volumes”, Advances in Computational Intelligence - Lecture Notes in Computer Science 6691:282-290. Web

Novo, J., Santos, J. and Penedo, M.G. (2011), “Optimization of topological active deformable models with differential evolution”, Proc. ICANNGA 2011- International Conference on Adaptive and Natural Computing Algorithms, LNCS 6593: 350-360. Web

Novo, J., Santos, J. and Penedo, M.G. (2011), “Multiobjective optimization of the 3D topological active volume segmentation model”, Proceedings of ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence.

Novo, J., Penedo, M.G. and Santos, J. (2010), “Evolutionary multiobjective optimization of topological active nets”, Pattern Recognition Letters 31(13):1781-1794. Web.

Novo, J., Santos, J., Penedo, M.G. and Fernández, A. (2010), “Optimization of topological active models with multiobjective evolutionary algorithms”, Proceedings of ICPR 2010 - 20th International Conference on Pattern Recognition, pp. 2226-2229.

Novo, J., Penedo, M.G. and Santos, J. (2009), “Localisation of the optic disc by means of GA-optimised topological active nets”, Image and Vision Computing 27:1572-1584. Web.

Ibánez, O., Barreira, N., Santos, J. and Penedo, M.G. (2009), “Genetic Approaches for Topological Active Nets Optimization”, Pattern Recognition 42(5):907-917. Web.

Novo, J., Barreira, N., Penedo, M.G. and Santos, J. (2009), “Genetic approaches for the automatic division of topological active volumes”, Proc. IWINAC’09, Methods and Models in Artificial and Natural Computation - LNCS 5602:20-29. Web

Novo, J., Penedo, M.G. and Santos, J. (2008), “Optic disc segmentation by means of GA-optimized topological active nets”, Proc. ICIAR 2008, Image Analysis and Recognition - LNCS 5112:807-816. Web

Santos, J., Ibánez, O., Barreira, N., and Penedo, M.G. (2007), “Genetic-Greedy Hybrid Approach for Topological Active Nets Optimization”, Adaptive and Natural Computing Algorithms - Lecture Notes in Computer Science, Vol. 4431:202-210, Springer Verlag. Web

Barreira, N., Penedo, M.G., Ibánez, O. and Santos, J. (2007), “Automatic topological active net division in a genetic-greedy hybrid approach”, Proc. IBPRIA 2007, Pattern Recognition and Image Analysis - Lecture Notes in Computer Science, Vol. 4478:226-233, Springer Verlag. Web

Ibánez, O., Barreira, N., Santos, J. and Penedo, M.G. (2006), “Topological Active Nets Optimization Using Genetic Algorithms”, Lecture Notes in Computer Science, Vol. 4141:272-282, Springer Verlag. Web

Bioinformatics

Varela, D. and Santos, J. (2019), “Crowding differential evolution for protein structure prediction”, Proceedings International Work-Conference on the Interplay between Natural and Artificial Computation - IWINAC 2019, LNCS 11487:193-203. Web

Handl, J., Shehu, A. and Santos, J. (Special Issue Editorial) (2018), “Advances in the application and development of non-linear global optimization techniques in computational structural biology”, IEEE/ACM Transactions on Computational Biology and Bioinformatics 15(3):688-689. Web

Ferrández, J.M., Santos, J. and Varela, R. (Guest Editors) (2018), Special Issue Editorial “Bio-inspired Computing Applications”, Natural Computing. Web

Varela, D. and Santos (2018), “Automatically obtaining a cellular automaton scheme for modeling protein folding using the FCC model”, Natural Computing, doi: 10.1007/s11047-018-9705-y. Web

Santos, J. and Monteagudo, A. (2018), “On the use of fitness sharing in studying the genetic code optimality”, Proceedings XIII Congreso Español en Metaheurísticas y Algoritmos Evolutivos y Bioinspirados (MAEB 2018) - XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018), 722-723. Web

Santos, J. and Varela, D. (2018), “Neural cellular automata for modeling protein folding”, International Conference on Mathematical Methods and Models in Biosciences (Biomath 2018). Web

Varela, D. and Santos, J. (2017), “A hybrid evolutionary algorithm for protein structure prediction using the Face-Centered Cubic lattice model”, Proceedings International Conference on Neural Information Processing ICONIP 2017, LNCS 10634:628-638. Web

Varela, D. and Santos, J. (2017), “A protein folding model using the Face-Centered Cubic lattice model”, Proceedings Workshop Evolutionary Computation in Computational Biology, Genetic and Evolutionary Computation Conference (GECCO 2017), 1674-1678. Web

Varela, D. and Santos, J. (2017), “Protein folding modeling with neural cellular automata using the Face-Centered Cubic model”, Proceedings International Work-Conference on the Interplay between Natural and Artificial Computation. LNCS 10337: 125-134. Web

Santos, J. and Monteagudo, A. (2017), “Inclusion of the fitness sharing technique in an evolutionary algorithm to analyze the fitness landscape of the genetic code adaptability”, BMC Bioinformatics 18:195, doi: 10.1186/s12859-017-1608-x. Web

Varela, D. and Santos, J. (2016), “Protein folding modeling with neural cellular automata using Rosetta”, GECCO 2016 Proceedings Companion, Workshop Evolutionary Computation in Computational Structural Biology, 1307- 1312. Web

Monteagudo, A. and Santos, J. (2015), “Evolutionary optimization of cancer treatments in a cancer stem cell context”, Proceedings Genetic and Evolutionary Computation Conference - GECCO 2015, 233-240. Web.

Varela, D. and Santos, J. (2015), “Combination of differential evolution and fragment-based replacements for protein structure prediction” GECCO 2015 Proceedings Companion, Workshop Evolutionary Computation in Computational Structural Biology. Web.

Monteagudo, A. and Santos, J. (2015), “Treatment analysis in a cancer stem cell context using a tumor growth model based on cellular automata”, Plos One, doi: 10.1371/journal.pone.0132306. Web

Santos, J., Villot, P., Diéguez, M. (2014), “Emergent protein folding modeled with evolved neural cellular automata using the 3D HP model”, Journal of Computational Biology 21(11):823-845. Web.

Santos, J. and Monteagudo, A. (2014), Analysis of behaviour transitions in tumour growth using a cellular automaton simulation, IET Systems Biology 9(3):75-87. Web.

Monteagudo, A. and Santos, J. (2014), “Studying the capability of different cancer hallmarks to initiate tumor growth using a cellular automaton simulation. Application in a cancer stem cell context”, Biosystems 115:46-58. Web

Monteagudo, A. and Santos, J. (2013), “Cancer stem cell modeling using a cellular automaton”, Proc. International Work-Conference on the Interplay between Natural and Artificial Computation, LNCS 7931:21-31. Web

Santos, J., Villot, P. and Diéguez, M. (2013), “Cellular automata for modeling protein folding using the HP model”, Proceedings IEEE Congress on Evolutionary Computation - IEEE-CEC 2013, 1586-1593. Web

Santos, J. and Monteagudo, A. (2012), “Study of cancer hallmarks relevance using a cellular automaton tumor growth model”, Proceedings PPSN 2012 - Parallel Problem Solving from Nature, LNCS 7491:489-499. Web

Monteagudo, A. and Santos, J. (2012), “A cellular automaton model for tumor growth simulation”, Advances in Intelligent and Soft-Computing. Proceedings 6th International Conference on Practical Applications of Computational Biology & Bioinformatics, Vol. 154: 147-155.

Santos, J. and Diéguez, M. (2011), “Differential evolution for protein structure prediction using the HP model”, Proc. IWINAC 2011, 4th. International Work-conference on the Interplay between Natural and Artificial Computation, LNCS 6686:323-333. Web

Santos, J., Monteagudo, A. (2011), “Simulated evolution applied to study the genetic code optimality using a model of codon reassignments”, BMC Bioinformatics 2011, 12:56. Web

Santos, J. Monteagudo, A. (2010), “Study of the genetic code adaptability by means of a genetic algorithm”, Journal of Theoretical Biology 264:854-865. Web.

Santos, J. and Monteagudo, A. (2009), “Genetic code optimality studied by means of simulated evolution and within the coevolution theory of the canonical code organization”, Natural Computing 8(4):719-738. Web.

Monteagudo, A. and Santos, J. (2007), “Simulated evolution of the adaptability of the genetic code using genetic algorithms”, Proc. IWINAC’07, Bio-Inspired Modeling of Cognitive Tasks - Lecture Notes in Computer Science 4527:478-487. Web

Santos, J. (2004), “Codon Based Amino Acid Encoding for the Neural Network Prediction of Protein Secondary Structure”, Proceedings of the 5th Spanish Bioinformatics Conference, pp. 101-106, Barcelona, 2004.