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2020 – today
- 2024
[j12]Mario Garza-Fabre
, Nicolás Cortés-García, Hiram Galeana-Zapién, Ricardo Landa:
Surrogate Models and Ensemble Strategies for Expensive Evolutionary Optimization: An Industrial Case Study. IEEE Access 12: 86144-86159 (2024)- [j11]Mario Garza-Fabre, Cristian C. Erazo-Agredo, Javier Rubio-Loyola:
A memetic algorithm for improved joint route selection and split-level management in next-generation wireless communications. Memetic Comput. 16(3): 315-336 (2024) - 2023
- [j10]Cristian C. Erazo-Agredo, Luis Díez, Ramón Agüero, Mario Garza-Fabre, Javier Rubio-Loyola:
Enabling realistic experimentation of disaggregated RAN: Design, implementation, and validation of a modular multi-split eNodeB. Comput. Networks 235: 109993 (2023) - [j9]Mario Garza-Fabre, Julia Handl, Adán José García:
Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views. IEEE Trans. Evol. Comput. 27(4): 817-831 (2023) - 2022
- [j8]Mario Garza-Fabre, Aarón Leonardo Sánchez-Martínez, Edwin Aldana-Bobadilla, Ricardo Landa Becerra:
Decision Making in Evolutionary Multiobjective Clustering: A Machine Learning Challenge. IEEE Access 10: 117281-117303 (2022) - [c14]Aarón Leonardo Sánchez-Martínez, Mario Garza-Fabre, Ricardo Landa Becerra, Edwin Aldana-Bobadilla:
Machine Learning-Based Decision Making in Evolutionary Multiobjective Clustering. MICAI (1) 2022: 123-137 - [d1]Mario Garza-Fabre, Julia Handl:
Protein Structure and Synthetic Multi-view Clustering Datasets. IEEE DataPort, 2022
- 2021
- [j7]Adán José García, Julia Handl, Wilfrido Gómez-Flores, Mario Garza-Fabre:
An evolutionary many-objective approach to multiview clustering using feature and relational data. Appl. Soft Comput. 108: 107425 (2021) - [j6]Cristian C. Erazo-Agredo, Mario Garza-Fabre, Ramón Agüero Calvo, Luis Díez, Joan Serrat, Javier Rubio-Loyola:
Joint Route Selection and Split Level Management for 5G C-RAN. IEEE Trans. Netw. Serv. Manag. 18(4): 4616-4638 (2021)
2010 – 2019
- 2019
- [c13]Adán José García, Julia Handl, Wilfrido Gómez-Flores, Mario Garza-Fabre:
Many-view clustering: an illustration using multiple dissimilarity measures. GECCO (Companion) 2019: 213-214 - 2018
- [j5]Mario Garza-Fabre, Julia Handl, Joshua D. Knowles:
An Improved and More Scalable Evolutionary Approach to Multiobjective Clustering. IEEE Trans. Evol. Comput. 22(4): 515-535 (2018) - 2017
- [c12]Mario Garza-Fabre, Julia Handl, Joshua D. Knowles:
A New Reduced-Length Genetic Representation for Evolutionary Multiobjective Clustering. EMO 2017: 236-251 - [c11]Julia Handl, Mario Garza-Fabre, Shaun M. Kandathil, Simon C. Lovell:
On heuristic bias in fragment-assembly methods for protein structure prediction. GECCO (Companion) 2017: 1652-1656 - 2016
- [j4]Mario Garza-Fabre, Shaun M. Kandathil, Julia Handl, Joshua D. Knowles, Simon C. Lovell:
Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction. Evol. Comput. 24(4): 577-607 (2016) - 2015
- [j3]Mario Garza-Fabre, Eduardo Rodriguez-Tello, Gregorio Toscano Pulido:
Constraint-handling through multi-objective optimization: The hydrophobic-polar model for protein structure prediction. Comput. Oper. Res. 53: 128-153 (2015) - [j2]Mario Garza-Fabre, Gregorio Toscano Pulido, Eduardo Rodriguez-Tello:
Multi-objectivization, fitness landscape transformation and search performance: A case of study on the hp model for protein structure prediction. Eur. J. Oper. Res. 243(2): 405-422 (2015) - [c10]Mario Garza-Fabre, Shaun M. Kandathil, Julia Handl, Joshua D. Knowles, Simon C. Lovell:
Using Machine Learning to Explore the Relevance of Local and Global Features During Conformational Search in Rosetta. GECCO (Companion) 2015: 935-938 - 2013
- [j1]Mario Garza-Fabre, Eduardo Rodriguez-Tello, Gregorio Toscano Pulido:
Comparative Analysis of Different Evaluation Functions for Protein Structure Prediction Under the HP Model. J. Comput. Sci. Technol. 28(5): 868-889 (2013) - [c9]Mario Garza-Fabre, Gregorio Toscano Pulido, Eduardo Rodriguez-Tello:
Handling constraints in the HP model for protein structure prediction by multiobjective optimization. IEEE Congress on Evolutionary Computation 2013: 2728-2735 - 2012
- [c8]Mario Garza-Fabre, Eduardo Rodriguez-Tello, Gregorio Toscano Pulido:
Multiobjectivizing the HP Model for Protein Structure Prediction. EvoCOP 2012: 182-193 - [c7]Mario Garza-Fabre, Gregorio Toscano Pulido, Eduardo Rodriguez-Tello:
Locality-based multiobjectivization for the HP model of protein structure prediction. GECCO 2012: 473-480 - [c6]Mario Garza-Fabre, Eduardo Rodriguez-Tello, Gregorio Toscano Pulido:
An Improved Multiobjectivization Strategy for HP Model-Based Protein Structure Prediction. PPSN (2) 2012: 82-92 - 2011
- [c5]Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos A. Coello Coello, Eduardo Rodriguez-Tello:
Effective ranking + speciation = Many-objective optimization. IEEE Congress on Evolutionary Computation 2011: 2115-2122 - [c4]Mario Garza-Fabre, Eduardo Rodriguez-Tello, Gregorio Toscano Pulido:
Comparing alternative energy functions for the HP model of protein structure prediction. IEEE Congress on Evolutionary Computation 2011: 2307-2314 - 2010
- [c3]Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos A. Coello Coello:
Two novel approaches for many-objective optimization. IEEE Congress on Evolutionary Computation 2010: 1-8
2000 – 2009
- 2009
- [c2]Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos A. Coello Coello:
Alternative Fitness Assignment Methods for Many-Objective Optimization Problems. Artificial Evolution 2009: 146-157 - [c1]Mario Garza-Fabre, Gregorio Toscano Pulido, Carlos A. Coello Coello:
Ranking Methods for Many-Objective Optimization. MICAI 2009: 633-645
Coauthor Index
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