Selection of Publications
Bioinformatics
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
Ferrández J.M. and Santos, J.
(Guest Editors) (2021), Special Issue Editorial “Bio-inspired Computing Approaches”, Natural Computing. 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
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.
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
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
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.
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.
