• A MapReduce-based Framework to Perform Full Model Selection in Very Large Datasets. Angel Diaz-Pacheco, Jesus A. Gonzalez, Carlos Reyes-Garcia. IADIS International Journal on Computer Science & Information Systems, 13 (1), 2018.
  • Full Model Selection in Big Data. Angel Diaz-Pacheco, Jesus A. Gonzalez, Carlos Reyes-Garcia. Mexican International Conference on Artificial Intelligence, 279-289, 2017.
  • MOPG: A Multi-Objective Evolutionary Algorithm for Prototype Generation. HJ Escalante, M Marin-Castro, A. Morales-Reyes, M. Graff, Alejandro Rosales-Perez, Manuel Montes, Carlos Reyes, Jesus A. Gonzalez. Pattern Analysis and Applications, 2017 (20:1), pp. 33 – 47.
  • An Evolutionary Multi-Objective Model and Instance Selection for Support Vector Machines with Pareto-based Ensembles. A Rosales-Perez, S Garcia, JA Gonzalez, CAC Coello, F Herrera. IEEE Transactions on Evolutionary Computation 2017, 21 (6), 863 – 877.
  • EMOPG+ FS: Evolutionary Multi-objective Prototype Generation and Feature Selection. Alejandro Rosales-Pérez, Jesus A Gonzalez, Carlos A Coello Coello, Carlos A Reyes-Garcia, Hugo Jair Escalante. Intelligent Data Analysis, 20, 2016.
  • Surrogate Modeling based on an Adaptive Network and Granular Computing. I Cruz-Vega, HJ Escalante, CA Reyes, Jesus A Gonzalez, A Rosales. Soft Computing, 2016, 20 (4), pp. 1549 – 1563.
  • Surrogate-Assisted Multi-Objective Model Selection for Support Vector Machines. A. Rosales-Perez, Jesus A. Gonzalez, CAC Coello, HJ Escalante, CA Reyes-Garcia. Neurocomputing 2015, 150, pp. 163 – 172.
  • Classifying Infant Cry Patterns by the Genetic Selection of a Fuzzy Model. A Rosales-Perez, CA Reyes-Garcia, JA Gonzalez, OF Reyes Galaviz. 2015 Biomedical Signal Processing and Control, 17, 38 – 46.
  • Towards a Surrogate-Assisted Multi-Objective Full Model Selection. Alejandro Rosales-Perez, Jesus A. Gonzalez, Carlos Reyes-Garcia, Carlos Coello. Research in Computer Science, 71, pp. 95-105, 2014.
  • Evolutionary Multi-Objective Approach for Prototype Generation and Feature Selection. A Rosales-Perez, Jesus A Gonzalez, Carlos A Coello-Coello, CA Reyes-Garcia. Iberoamerican Congress on Pattern Recognition, 2014, pp. 424 – 431.
  • Dimension Reduction Parameters for Leukemia Diagnostic based in Subspace Arrangement Segmentation. Leticia Flores-Pulido, G. Rodriguez-Gomez, Jesus A. Gonzalez. Applied Mathematics & Information Sciences, 8 (6), 2014.
  • An Evolutionary Multi-Objective Approach for Prototype Generation. A Rosales-Perez, HJ Escalante, CA Coello-Coello, Jesus A Gonzalez, Carlos Reyes-Garcia. IEEE Congress on Evolutionary Computation, 2014, pp. 1100 – 1107.
  • Multi-Objective Model Type Selection. A Rosales-Perez, JA Gonzalez, CAC Coello, HJ Escalante, 2014, Neurocomputing 146, 83 – 94.
  • Synthetic Oversampling of Instances Using Clustering. AI Sanchez, EF Morales, Jesus A Gonzalez. International Journal on Artificial Intelligence Tools, 2013, 22 (02).
  • On the Application of Genetic Selection of a Customized Fuzzy Model for the Classification of Infant Cry Patterns. Alejandro Rosales-Perez, Carlos Reyes-Garcia, Jesus A. Gonzalez, Orion Reyes-Galaviz. Models and Analysis of Vocal Emissions for Biomedical Applications, 2013.
  • Bias and Variance Multi-Objective Optimization for Support Vector Machines Model Selection. A Rosales-Perez, HJ Escalante, Jesus A Gonzalez, CA Reyes-Garcia, Carlos A Coello-Coello, Iberian Conference on Pattern Recognition and Image Analysis, 2013, pp. 108 – 116.
  • Bias and Variance Optimization for SVMs Model Selection. A Rosales-Perez, HJ Escalante, Jesus A Gonzalez, CA Reyes-Garcia. The Twenty-Sixth International FLAIRS Conference, 2013.
  • A Hybrid Surrogate-based Approach for Evolutionary Multi-Objective Optimization. A Rosales-Perez, CAC Coello, JA Gonzalez, CA Reyes-Garcia. 2013 IEEE Congress on Evolutionary Computation, 2548 – 2555.
  • Acute Leukemia Classification by Ensemble Particle Swarm Model Selection. HJ Escalante, M Montes-y-Gomez, JA Gonzalez, P Gomez-Gil. Artificial Intelligence in Medicine. 2012.
  • Learning Temporal Nodes Bayesian Networks. P Hernandez-Leal, LE Sucar, JA Gonzalez. 2011, The 24th Florida Artificial Intelligence Research Society Conference (FLAIRS 24).
  • Genetic Selection of Fuzzy Model for Acute Leukemia Classification. A Rosales-Perez, CA Reyes-Garcia, P Gomez-Gil, Jesus A Gonzalez, Leopoldo Altamirano. Mexican International Conference on Artificial Intelligence, 2011, pp. 537 – 548.
  • Symbolic One-Class Learning from Imbalanced Datasets: Application in Medical Diagnosis. L Mena, JA Gonzalez. International Journal on Artificial Intelligence Tools, 2009 18 (02), 273 – 309.
  • Selecting Minority Examples from Misclassified Data for Over-Sampling. Jorge de la Calleja, Olac Fuentes, Jesus A. Gonzalez. FLAIRS Conference, 2008, 276-281.
  • Weighted Instance-based Learning using Representative Intervals. Octavio Gomez, Eduardo Morales, Jesus A. Gonzalez. Mexican International Conference on Artificial Intelligence, 2007, pp. 420 – 430.
  • Machine Learning for Imbalanced Datasets: Application in Medical Diagnostic. LJ Mena, JA Gonzalez. FLAIRS 2006, 574-579.
  • Defining New Argumentation-based Semantics by Minimal Models. JC Nieves, U Cortes, M Osorio, I Olmos, JA Gonzalez. Seventh Mexican International Conference on Computer Science, 2006, pp. 210 – 220.
  • Data Mining with Decision Trees and Neural Networks for Calcification Detection in Mammograms. BA Flores, JA Gonzalez. Mexican International Conference on Artificial Intelligence, 2004, 232 – 241.