Inés María Galván León

Inés María Galván León

Contact Info

Inés María
Galván León
Ciencia de la Computación e Inteligencia Artificial

Curriculum Vitae

Formación académica

  • Licenciada en Matemáticas, Facultad de Matemáticas, Universidad de Sevilla, 1989
  • Doctora en Informática, Facultad de Informática, Universidad Politécnica de Madrid, 1998

Proyectos de Investigación

Probabilistic forecasting and meta-heuristic optimization of solar/wind resources in the Iberian Peninsula for a low carbon power system (PROB-META). 2020-2023.


SEACW: Social Ecosystem for anti-aging, capacitation and wellbeing (RTD). 2013-2015.

Gestión de Movilidad Eficiente y Sostenible: MOVES (TIN2011-28336), 2012-2014.

M*: Metaheurísticas Multiobjetivo y Aplicaciones Multidisciplinares, Ministerio de Educación y Ciencia, 2008-2011.

OPLINK: Optimización y Ambientes de Red, Ministerio de Educación y Ciencia, 2005-2008.

Diseño automático de programas de control secuencial: aplicación al control de procesos térmicos en la industria láctea, Ministerio de Educación y Ciencia (CICYT), 2000- 2002.

Control and malfunction diagnosis in batch chemical reactors using neural networks, European Commission, 1992-1995.

Publicaciones más relevantes

Francisco J. Rodríguez -Benítez, Clara Arbizu-Barrenaa, Javier Huertas-Tato, Ricardo Aler-Mur, Inés Galván-León, David Pozo-Vázquez. A short-term solar radiation forecasting system for the Iberian Peninsula. Part 1: Models description and performance assessment. Solar Energy. 195, 396-412. 2020.

Javier Huertas-Tato, Ricardo Aler, Inés M. Galván, Francisco J. Rodríguez-Benítez, Clara Arbizu-Barrena, and David Pozo-Vázquez. A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning. Solar Energy, 195, 685-696, 2020.

Aler, R., Huertas-Tato, J., Valls, J. M., and Galván I.M. Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach. Energies, 12(24), 4713, 2019.

Martín, R., Aler, R., & Galván, I. M. A filter attribute selection method based on local reliable information. Applied Intelligence, 48(1), 35-45, 2018.

Labed, K., Fizazi, H., Mahi, H., & Galvan, I. M. A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction. Applied Artificial Intelligence, 32(1), 96-118, 2018.

J. Huertas-Tato, F.J. Rodríguez-Benítez, C. Arbizu-Barrena, R. Aler-Mur, I. Galvan-Leon and D. Pozo-Vázquez. Automatic cloud type classification based on the combined use of a sky camera and a ceilometer. Journal of Geophysical Research: Atmospheres, 122(20), 11,045-11,061, 2017.

Galván, I. M., Valls, J. M., Cervantes, A., & Aler, R. Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks. Information Sciences, 418, 363-382, 2017.

Aler, R., Galván, I. M., Ruiz-Arias, J. A., & Gueymard, C. A. Improving the separation of direct and diffuse solar radiation components using machine learning by gradient boosting. Solar Energy, 150, 558-569, 2017.

Martin, R., Aler, R., Valls, J. M., and Galvan, I. M. Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models. Concurrency and Computation: Practice and Experience, 28:1261–1274, 2016.

Ricardo Aler and Inés M. Galván. Optimizing the Number of Electrodes and Spatial Filters for Brain-Computer Interfaces by means of an Evolutionary Multi-objective Approach. Expert System with Applications. 42(15), 6215-6223, 2015.

García-Cuesta, E., de Castro, A. J., Galván, I. M., & López, F. Temperature Profile Retrieval in Axisymmetric Combustion Plumes Using Multilayer Perceptron Modeling and Spectral Feature Selection in the Infrared CO2 Emission Band. Applied spectroscopy, 68(8), 900-908.  2014.

Sandra García, David Quintana , Inés M. Galván and Pedro Isasi. Extended mean–variance model for reliable evolutionary portfolio optimization. AI Communications 27, 315–324 315, 2014.

Garcia S., Quintana D., Galván. I. and, Isasi P. Multiobjective Algorithms with Resampling for Portfolio Optimization. Computing and informatics.  Vol. 32, No. 4, pp. 777–796, 2013

Ricardo Aler, Inés M. Galván, José M. Valls. Applying evolution strategies to preprocessing EEG signals for brain–computer interfaces .Information Sciences 215, 53–66, 2012.

Garcia S., Quintana D., Galván. I. and, Isasi P..Time-stamped Resampling for Robust Evolutionary Portfolio Optimization  Expert Systems with Applications, 39(12), 10722-10730, 2012.

Ricardo Aler, Alicia Vega, Inés M. Galván and Antonio J. Nebro. Multi-objective metaheuristics for preprocessing EEG data in brain–computer interfaces. Engineering optimization, Vol.44, No.3, pp. 373–390, 2012.

Inés M. Galván, Joś M. Valls, Miguel García and Pedro Isasi. A lazy learning approach for building classification models . Internacional Journal of Intelligent Systems, Volume 26, Issue 8, pages 773–786, 2011.

Esteban Garcia-Cuesta, Inés M. Galvan, and Antonio J. de Castro. Recursive Discriminant Regression Analysis to Find Homogeneous Structures. Internacional Journal of Neural Systems, Vol. 21, No. 1, 1–7, 2011.

Alejandro Cervantes, Inés M. Galván and Pedro Isasi. AMPSO: A new Particle Swarm Method for Nearest Neighborhood Classification. IEEE Transactions on Systems, Man, and Cybernetics: Part B. vol. 39, n. 5, p. 1082 – 1091, 2009.

A. CERVANTES and I. GALVAN and P. ISASI. Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems. New Generation Computing. Vol. 27. Number 3. pp 239-257. 2009.

José M. Valls,, Inés M. Galván  and Pedro Isasi. Learning radial basis neural networks in a lazy way: A comparative study. Neurocomputing, Volume 71, Issues 13-15, August 2008, Pages 2529-2537, 2008.

Esteban García-Cuesta, Inés M. Galván and Antonio J. de Castro. Multilayer Perceptron as Inverse Model in a Ground-Based Remote Sensing Temperature Retrieval Problem. Engineering Applications of Artificial Intelligence, 2, 26–34, 2008.

José M. Valls, Inés M. Galván, Pedro Isasi. LRBNN: A Lazy RBNN Model. AI Communications,  Volumen: 20, Number 2 / 2007. pp 71--86, 2007.

JM Valls., Galván, I., Isasi P. Improving the generalization ability of RBNN using a selective strategy based on the Gaussian Kernel functions. Computers and Informatics, Volumen: 25, 1- 15, 2006.

A.Cervantes, A., Galván, I., Isasi P. Binary particle swarm optimization in classification. Neural Network World, Volumen: 15, 229- 241, 2005

G. Gutiérrez, A. Sanchis, P. Isasi, J.M. Molina and I. M. Galván. Non-direct Encoding method based on Cellular Automata to design Neural Network Architectures. Computer and Informatics Volumen: 24, Nº 3. 225-247, 2005. 

José M. Valls, Inés M. Galván, Pedro Isasi. Lazy learning in Radial Basis Neural Networks: a way of achieving more accurate models. Neural Processing Letters, Volumen: 20-2, 105- 124, 2004.

J. M. Molina, I.M. Galván, J.M. Valls and A. Leal. Optimizing the Number of Learning Cycles in the Design of Radial Basis Neural Networks. Computing and Informatics Volumen: 20, 429- 449, 2001.

I.  M. Galván, P. Isasi,R. Aler and J. M. Valls. A Selective Learning Method to Improve the Generalization of Multilayer Feedforward Neural Networks. International Journal of Neural Systems Volumen: 11, 167- 157, 2001.

I.M. Galván and P. Isasi. Multi-step learning rule for recurrent neural models: An application to time series forecasting. Neural Processing Letters, Volumen: 13, 115-133, 2001.

I.M. Galván, P. Isasi and J. M. Zaldívar.  PNNARMA model: an alternative to phenomenological models in chemical reactors. Engineering Applications of Artificial Intelligence, Volumen 14, 139-154, 2001.

J. M. Zaldívar, E Gutiérrez, I.M. Galván, F. Strozzi and A, Tomasin. Forecasting high waters at Venice Lagoon using Chaotic time series analisys and nonlinear neural netwoks. Journal of Hydroinformatics, Volumen: 02.1, 61- 84, 2000.

I. M. Galván and J.M. Zaldívar. Applications of recurrent neural networks in Bath Reactors. Part II: Nonlinaer inverse and predictive control of the heat transfer fluid temperature. Chemical Engineering and Processing, Volumen: 37, 161-175, 1997.

I. M. Galván and J.M. Zaldívar. Applications of recurrent neural networks in Bath Reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid. Chemical Engineering and Processing, Volumen: 36, 505- 518, 1997.

I.M. Galván, J.M. Zaldívar, H. Hernández and E. Molga. The use of neural networks for fitting complex kinetic data. Computer and Chemical Engineering, Volumen: 20, 1451-1465, 1996 .

Research Themes

  • Aprendizaje Automático / Minería de Datos
  • Redes Neuronales Artificiales
  • Computación evolutiva
  • Optimización Multi-objetivo
  • Energías Renovables
  • Estimación de incertidumbre de valores continuos


Redes de Neuronas Artificiales


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