Herruzo-Lodeiro, CristinaRodríguez-Díaz, FrancescTroncoso, AliciaMartínez Ballesteros, María del Mar2025-07-012025-07-012025-03Herruzo-Lodeiro, C., Rodríguez-Díaz, F., Troncoso, A. y Martínez Ballesteros, M.d.M. (2025). Bioinspired evolutionary metaheuristic based on COVID spread for discovering numerical association rules. En Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (1381-144), Catania, Italy: ACM.https://hdl.handle.net/11441/174807Thesocial impact and global health crisis caused by the coronavirus since late 2019 led to the development of a novel bio-inspired al gorithm. This algorithm simulates the behavior and spread of the virus, known as the Coronavirus Optimization Algorithm. It pro vides several advantages over similar approaches and serves as a basis for generalizing pattern or association identification from nu merical datasets. In this study, essential updates and modifications are proposed to adapt the CVOA algorithm for mining numeri cal association rules. These changes involve adjustments to the encoding of individuals and the infection/mutation process. Addi tionally, parameter values are updated, and a new fitness function is proposed to be maximized. The main objective is to obtain high quality numerical association rules for any dataset regardless of the number and range of attributes in the dataset. The implemented algorithm is compared to others designed for mining quantitative association rules in order to validate the results. For this reason, different datasets from the BUFA repository are used, confirming that Coronavirus Optimization Algorithm is a promising option for discovering interesting association rules within numerical datasets.application/pdf8 p.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/EvolutionaryAlgorithmsNumerical association rulesBioinspired metaheuristicCOVIDinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1145/3672608.3707787