Evolutionary Fuzzy Systems

Biedma-Rdguez, C. ., Gacto, M. J., Anguita-Ruiz, A. ., Alcalá, R. ., Aguilera, C. M., & Alcala-Fdez, J. . (2023). Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy. Fuzzy Sets and Systems, 465, 108511. https://doi.org/https://doi.org/10.1016/j.fss.2023.03.014
Rodríguez, C. B., Gacto, M. J., Ruiz, A. A., Fernández, J. A., & Fernández, R. A. (2022). Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension. Springer. https://doi.org/10.1007/s40815-022-01324-w (Original work published)
Padilla, D. ., Padilla Rascón, M. A. . ., Cámara, R. ., & Carmona, C. J. . (2023). A First Evolutionary Fuzzy Approach for Change Mining with Smart Bands. 14113 LNAI, 171-181. Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-42935-4_14
Fernández, A. ., López, V. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2015). Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges. Knowledge-Based Systems, 80, 109-121. https://doi.org/10.1016/j.knosys.2015.01.013