Selection of Publications
Bioinformatics
Filgueiras, J.L., Varela, D. and Santos, J. (2025),
“Protein structure refinement with a memetic algorithm”, Natural Computing. 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
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
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
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
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.
Evolutionary Computing and Evolutionary Robotics
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
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
Ferrández J.M. and Santos, J.
(Guest Editors) (2021), Special Issue Editorial “Bio-inspired Computing
Approaches”, Natural Computing. 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.
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.
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.
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.
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.
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.
