Gracia Sánchez

695 total citations
28 papers, 468 citations indexed

About

Gracia Sánchez is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Control and Systems Engineering. According to data from OpenAlex, Gracia Sánchez has authored 28 papers receiving a total of 468 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 11 papers in Computational Theory and Mathematics and 6 papers in Control and Systems Engineering. Recurrent topics in Gracia Sánchez's work include Evolutionary Algorithms and Applications (11 papers), Advanced Multi-Objective Optimization Algorithms (10 papers) and Metaheuristic Optimization Algorithms Research (9 papers). Gracia Sánchez is often cited by papers focused on Evolutionary Algorithms and Applications (11 papers), Advanced Multi-Objective Optimization Algorithms (10 papers) and Metaheuristic Optimization Algorithms Research (9 papers). Gracia Sánchez collaborates with scholars based in Spain, Italy and Malaysia. Gracia Sánchez's co-authors include Fernando Jiménez, Guido Sciavicco, José Palma, Antonio Skármeta, Kalyanmoy Deb, José M. Juárez, José M. Garcı́a, Pandian Vasant, David Hervás and José Luís Verdegay and has published in prestigious journals such as IEEE Access, Information Sciences and IEEE Transactions on Fuzzy Systems.

In The Last Decade

Gracia Sánchez

27 papers receiving 451 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gracia Sánchez Spain 11 282 130 50 39 38 28 468
Roman Neruda Czechia 11 330 1.2× 146 1.1× 46 0.9× 35 0.9× 79 2.1× 92 474
Rafael Blanquero Spain 13 165 0.6× 62 0.5× 71 1.4× 63 1.6× 29 0.8× 35 488
Alexander Karlsson Sweden 10 132 0.5× 130 1.0× 40 0.8× 44 1.1× 22 0.6× 37 418
Shenglei Chen China 9 272 1.0× 46 0.4× 50 1.0× 19 0.5× 94 2.5× 24 443
Tuve Löfström Sweden 12 284 1.0× 51 0.4× 18 0.4× 49 1.3× 37 1.0× 56 467
Yang-Geng Fu China 13 273 1.0× 119 0.9× 114 2.3× 40 1.0× 84 2.2× 37 451
Chang Lü China 7 268 1.0× 158 1.2× 31 0.6× 21 0.5× 18 0.5× 22 463
Zhaolu Guo China 12 294 1.0× 154 1.2× 83 1.7× 49 1.3× 14 0.4× 29 500
Alejandro Rosete Suárez Cuba 11 189 0.7× 119 0.9× 62 1.2× 29 0.7× 150 3.9× 68 413
An‐Da Li China 8 196 0.7× 91 0.7× 21 0.4× 49 1.3× 19 0.5× 16 355

Countries citing papers authored by Gracia Sánchez

Since Specialization
Citations

This map shows the geographic impact of Gracia Sánchez'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 Gracia Sánchez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gracia Sánchez more than expected).

Fields of papers citing papers by Gracia Sánchez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gracia Sánchez. 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 Gracia Sánchez. The network helps show where Gracia Sánchez may publish in the future.

Co-authorship network of co-authors of Gracia Sánchez

This figure shows the co-authorship network connecting the top 25 collaborators of Gracia Sánchez. A scholar is included among the top collaborators of Gracia Sánchez 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 Gracia Sánchez. Gracia Sánchez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sánchez, Gracia, et al.. (2025). Multi-objective evolutionary feature selection for ensemble learning with random forests in time series forecasting. Swarm and Evolutionary Computation. 99. 102211–102211.
2.
Jiménez, Fernando, et al.. (2021). Multi-Objective Evolutionary Simultaneous Feature Selection and Outlier Detection for Regression. IEEE Access. 9. 135675–135688. 3 indexed citations
3.
Jiménez, Fernando, Gracia Sánchez, José Palma, & Guido Sciavicco. (2021). Three-objective constrained evolutionary instance selection for classification: Wrapper and filter approaches. Engineering Applications of Artificial Intelligence. 107. 104531–104531. 7 indexed citations
4.
Jiménez, Fernando, et al.. (2020). Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction. Artificial Intelligence in Medicine. 104. 101818–101818. 39 indexed citations
5.
Jiménez, Fernando, et al.. (2019). Predicting the Risk of Academic Dropout With Temporal Multi-Objective Optimization. IEEE Transactions on Learning Technologies. 12(2). 225–236. 21 indexed citations
6.
Jiménez, Fernando, et al.. (2019). Multiobjective Evolutionary Feature Selection for Fuzzy Classification. IEEE Transactions on Fuzzy Systems. 27(5). 1085–1099. 55 indexed citations
7.
Jiménez, Fernando, et al.. (2019). Multiobjective evolutionary feature selection and fuzzy classification of contact centre data. Expert Systems. 36(3). 5 indexed citations
8.
Campos, Manuel, Fernando Jiménez, Gracia Sánchez, et al.. (2019). A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy. Artificial Intelligence in Medicine. 102. 101751–101751. 4 indexed citations
9.
Jiménez, Fernando, et al.. (2018). Multi-Objective Evolutionary Rule-Based Classification with Categorical Data. Entropy. 20(9). 684–684. 6 indexed citations
10.
Jiménez, Fernando, et al.. (2018). Towards semi-automatic human performance evaluation: The case study of a contact center. Intelligent Data Analysis. 22(4). 867–880. 2 indexed citations
11.
Jiménez, Fernando, et al.. (2016). Unsupervised feature selection for interpretable classification in behavioral assessment of children. Expert Systems. 34(4). 14 indexed citations
12.
Martin, Michael K., et al.. (2016). Multi-Objective Evolutionary Computation Based Feature Selection Applied To Behaviour Assessment Of Children. Zenodo (CERN European Organization for Nuclear Research). 6 indexed citations
13.
Jiménez, Fernando, Gracia Sánchez, & José M. Juárez. (2014). Multi-objective evolutionary algorithms for fuzzy classification in survival prediction. Artificial Intelligence in Medicine. 60(3). 197–219. 49 indexed citations
14.
Jiménez, Fernando, Gracia Sánchez, & Pandian Vasant. (2013). A multi-objective evolutionary approach for fuzzy optimization in production planning. Journal of Intelligent & Fuzzy Systems. 25(2). 441–455. 24 indexed citations
15.
16.
Sánchez, Gracia, Fernando Jiménez, & Antonio Skármeta. (2004). Multi-objective evolutionary algorithms based fuzzy optimization. 1. 1–7. 2 indexed citations
17.
Carse, Brian, Tony Pipe, Antonio Skármeta, et al.. (2003). Current issues and future directions in evolutionary fuzzy systems research.. European Society for Fuzzy Logic and Technology Conference. 81–87. 1 indexed citations
18.
Jiménez, Fernando, José Manuel Cadenas Figueredo, José Luís Verdegay, & Gracia Sánchez. (2003). Solving fuzzy optimization problems by evolutionary algorithms. Information Sciences. 152. 303–311. 17 indexed citations
19.
Jiménez, Fernando, Gracia Sánchez, Antonio Skármeta, Hans Roubos, & Robert Babuška. (2003). Fuzzy modeling with multi-objective neuro-evolutionary algorithms. vol.3. 6–6. 5 indexed citations
20.
Jiménez, Fernando, Gracia Sánchez, Antonio Skármeta, & José Luís Verdegay. (2002). A Multi-Objective Neuro-Evolutionary Algorithm for Fuzzy Modeling.. 1423–1426. 1 indexed citations

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.

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