Selection of Publications
Martínez, D.,
Santos, J. (2026), “Clustering the latent space in variational autoencoders
for image generation”. Bioinspired Intelligent Systems: From Robotics
and Computer Vision to Trustworthy Applications. IWINAC 2026. Lecture Notes in
Computer Science 16575: 195-205. Web
Beade, A., Santos,
J. and Rodríguez, M. (2026), “Analysis
of economic environment incidence in Genetic Programming-evolved multi-period
bankruptcy prediction models”, Intelligent
Systems in Accounting, Finance and Management 33:e70034. Web
Beade, A., Santos,
J. and Rodríguez, M. (2026), “Using
genetic programming as a feature selector and classifier to implement
bankruptcy prediction models”, Computer Science and Information Systems
23(1): 513-534. Web.
Filgueiras,
J.L. and Santos, J. (2025), “Protein structure refinement with a memetic
algorithm”, Natural Computing 24:619-635. Web
Filgueiras,
J.L. and Santos, J. (2024), “Refinement of protein structures
with a memetic algorithm. Examples with SARS-CoV-2 proteins”, Proceedings International Work-Conference on the
Interplay between Natural and Artificial Computation - IWINAC/ICINAC 2024,
Lecture Notes in Computer Science 14675:129-139. Web
Beade, A., Rodríguez, M. and Santos, J. (2024), “Business failure
prediction models with high and stable predictive power over time using genetic
programming”, Operational
Research 24:52. Web
Beade, A., Rodríguez, M. and Santos, J. (2024), "Genetic
programming for feature selection in business failure prediction. Comparison of
the use of financial variables and economic environment variables," 2024 International
Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1-6, IEEE
Xplore. Web.
Beade,
A., Rodríguez, M. and Santos, J. (2024), “Variable selection in the prediction
of business failure using genetic programming”, Knowledge-Based Systems 289. Web
Filgueiras,
J.L., Varela, D. and Santos, J. (2023), “Protein structure prediction with
energy minimization and deep learning approaches”, Natural Computing 22:655-670. Web
Ferrández, J.M., Santos, J. and Varela, R. (Guest Editors) (2023),
Special Issue Editorial “Bio-inspired Computing Approaches for Problem
Solving”, Natural Computing 22:613-614. Web
Beade, A., Rodríguez, M. and Santos, J. (2023), “Multiperiod bankruptcy
prediction models with interpretable single models”, Computational Economics. Web
Beade, A., Rodríguez, M. and Santos, J. (2023), “Evolutionary feature
selection approaches for insolvency business prediction with genetic
programming”, Natural Computing 22:705-722.
Web
Górriz, J.M., Álvarez-Illán, J.,
Álvarez-Marquina. ... Santos, J. … (2023), “Computational
approaches to Explainable Artificial Intelligence: Advances in theory,
applications and trends”, Information
Fusion 100. Web
Framil, M., Cabalar, P., Santos, J. (2022), “A MaxSAT Solver Based
on Differential Evolution (Preliminary Report)”, Progress in Artificial Intelligence. Proceedings EPIA 2022. Lecture
Notes in Computer Science 13566: 676–687. Web
Santos, J., Sestayo, Ó., Beade, Á., Rodríguez, M. (2022), “Automatic
selection of financial ratios by means of differential evolution
and for predicting business insolvency”, Proceedings International Work-Conference on the Interplay between
Natural and Artificial Computation - IWINAC 2019, Lecture Notes in Computer
Science 13259: 534-544. Web
Varela, D. and Santos, J. (2022), “Niching methods
integrated with a differential evolution memetic algorithm for protein structure
prediction”, Swarm and Evolutionary Computation 71, Art. 101062. Web
Varela, D. and Santos, J. (2022), “Evolving cellular
automata schemes for protein folding modeling using the Rosetta atomic
representation”, Genetic Programming and Evolvable Machines. Web
Filgueiras, J.L., Varela, D., Santos, J. (2022), “Energy minimization vs.
deep learning approaches for protein structure prediction”, Proceedings International Work-Conference on
the Interplay between Natural and Artificial Computation - IWINAC 2022, Lecture
Notes in Computer Science 13259:109-118. Web
Ferrández J.M. and Santos, J. (Guest Editors) (2021), Special Issue
Editorial “Bio-inspired Computing Approaches”, Natural Computing. Web
Santos, J. and Rivas, H. (2021), “Evolution of amino acid properties in
the context of protein secondary structure prediction”, Proceedings IEEE Congress on Evolutionary Computation - IEEE-CEC 2021,
426-433. Web
Varela, D. and Santos, J. (2020), “Protein structure prediction in an
atomic model with differential evolution integrated with the crowding niching
method”, Natural Computing. Web
Górriz J.M. et al. (2020), “Artificial intelligence within the interplay
between natural and artificial computation: Advances in data science, trends
and applications”, Neurocomputing
410:237-270. Web
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
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
Ferrández, J.M., Santos, J. and Varela, R. (Guest Editors) (2018),
Special Issue Editorial “Bio-inspired Computing Applications”, Natural Computing. Web
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.
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. 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., 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
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
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. (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
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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
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., Ibánez, O., Barreira, N., and Penedo, M.G.
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.
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.
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.
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. and Santos, J. (2003), “Modeling Temporal Series Through
Synaptic Delay Based Neural Networks”, Neural Computing and Applications
11:224-237. Web.
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.
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.
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), “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.
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.
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., 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.
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.
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.
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.
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.
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. (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., 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., 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.
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.
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., 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., Duro, R.J. (1994),
“Evolutionary Generation and Training of Recurrent Artificial Neural Networks”,
Proceedings IEEE World Congress on
Computational Intelligence.
Vol. I: 759-763.
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.
