Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Ten years of genetic fuzzy systems: current framework and new trends
2003591 citationsÓscar Cordón, Fernando Gomide et al.profile →
Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases
2002538 citationsÓscar Cordón, Francisco Herrera et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Óscar Cordón'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 Óscar Cordón with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Óscar Cordón more than expected).
This network shows the impact of papers produced by Óscar Cordón. 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 Óscar Cordón. The network helps show where Óscar Cordón may publish in the future.
Co-authorship network of co-authors of Óscar Cordón
This figure shows the co-authorship network connecting the top 25 collaborators of Óscar Cordón.
A scholar is included among the top collaborators of Óscar Cordón 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 Óscar Cordón. Óscar Cordón is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Trawiński, Krzysztof, Arnaud Quirin, & Óscar Cordón. (2009). Bi-criteria Genetic Selection of Bagging Fuzzy Rule-based Multiclassification Systems. European Society for Fuzzy Logic and Technology Conference. 1514–1519.3 indexed citations
11.
Ibáñez, Óscar, Óscar Cordón, Sergio Damas, Sergio Guadarrama, & José Santamaría. (2009). A new approach to fuzzy location of cephalometric landmarks in craniofacial superimposition. European Society for Fuzzy Logic and Technology Conference. 195–200.5 indexed citations
Cordón, Óscar, Francisco Herrera, & Manuel Lozano. (2007). On the Bidirectional Integration of Genetic Algorithms and Fuzzy Logic.
14.
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
15.
Cordón, Óscar, Francisco Herrera, Frank Hoffmann, & Luis Magdalena. (2001). Genetic Fuzzy Systems.405 indexed citations
16.
Casillas, Jorge, Óscar Cordón, & Francisco Herrera. (2001). A cooperative coevolutionary algorithm for jointly learning fuzzy rule bases and membership functions.. European Society for Fuzzy Logic and Technology Conference. 118–121.1 indexed citations
Cordón, Óscar, et al.. (1999). Learning queries for a fuzzy information retrieval system by means of GA-P techniques.. European Society for Fuzzy Logic and Technology Conference. 335–338.1 indexed citations
19.
Cordón, Óscar, Francisco Herrera, & Antonio Peregrín. (1999). Characterisation of implication operators in fuzzy rule based systems from basic properties.. European Society for Fuzzy Logic and Technology Conference. 163–166.
20.
Cordón, Óscar, Francisco Herrera, & María José del Jesús. (1999). Evolutionary approaches to the learning of fuzzy rule-based classification systems. CRC Press, Inc. eBooks. 107–160.4 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.