Mario Garza-Fabre
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 5%
- Molecular Biology
- Materials Chemistry
- Electrical and Electronic Engineering
- Co-authors
- Gregorio Toscano‐PulidoEduardo Rodríguez-TelloJulia HandlJoshua KnowlesCarlos A. Coello CoelloSimon C. LovellShaun M. KandathilAdán José-García
- Topics
- Metaheuristic Optimization Algorithms Research (8 papers)Protein Structure and Dynamics (6 papers)Evolutionary Algorithms and Applications (6 papers)
- Cited by
- Computational Theory and MathematicsArtificial IntelligenceManagement Science and Operations Research
- Partner nations
- MexicoUnited KingdomSpain
In The Last Decade
Mario Garza-Fabre
18 papers receiving 201 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 116
- Computational Theory and Mathematics 93
- Molecular Biology 67
- Materials Chemistry 20
- Electrical and Electronic Engineering 16
Countries citing papers authored by Mario Garza-Fabre
This map shows the geographic impact of Mario Garza-Fabre's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mario Garza-Fabre with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Garza-Fabre more than expected).
Fields of papers citing papers by Mario Garza-Fabre
This network shows the impact of papers produced by Mario Garza-Fabre. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mario Garza-Fabre. The network helps show where Mario Garza-Fabre may publish in the future.
Co-authorship network of co-authors of Mario Garza-Fabre
This figure shows the co-authorship network connecting the top 25 collaborators of Mario Garza-Fabre. A scholar is included among the top collaborators of Mario Garza-Fabre based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mario Garza-Fabre. Mario Garza-Fabre is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 10 | |
| 5 | 14 | |
| 6 | 4 | |
| 7 | 12 | |
| 8 | 33 | |
| 9 | 24 | |
| 10 | 1 | |
| 11 | 14 | |
| 12 | 15 | |
| 13 | 13 | |
| 14 | 6 | |
| 15 | 12 | |
| 16 | 17 | |
| 17 | 3 | |
| 18 | 25 |
About Mario Garza-Fabre
Mario Garza-Fabre is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Biochemistry, having authored 18 papers that have together received 207 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (8 papers), Protein Structure and Dynamics (6 papers) and Evolutionary Algorithms and Applications (6 papers). The work is most often cited by research in Computational Theory and Mathematics (93 citations), Artificial Intelligence (116 citations) and Management Science and Operations Research (14 citations). Mario Garza-Fabre has collaborated with scholars based in Mexico, United Kingdom and Spain. Frequent co-authors include Gregorio Toscano‐Pulido, Eduardo Rodríguez-Tello, Julia Handl, Joshua Knowles, Carlos A. Coello Coello, Simon C. Lovell, Shaun M. Kandathil, Adán José-García, Wilfrido Gómez‐Flores and Luis Díez. Their work appears in journals such as Scientific Reports, European Journal of Operational Research and IEEE Access.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.