A lexicographic cooperative co-evolutionary approach for feature selection González Peñalver, Jesús Ortega Lopera, Julio Escobar Pérez, Juan José Damas Hermoso, Miguel Cooperative co-evolution Multi-objective optimization Lexicographic optimization Feature selection Classification This work was supported by project PGC2018-098813-B-C31 (Spanish "Ministerio de Ciencia, Innovacion y Universidades") , and by European Regional Development Funds (ERDF) . Funding for open access charge: Universidad de Granada/CBUA. We would like to thank Dr. Daniel Castillo-Secilla and Dr. John Q. Gan for making available to us, respectively, the lung cancer dataset and the University of Essex BCI data files. This paper starts with two hypotheses. The first one is that the simultaneous optimization of the hyperparameters regulating the classifier within a wrapper method, while the best subset of features is being determined, should improve the results with respect to those obtained with a preparameterized classifier. The second one is that solving these two problems can be formulated as a lexicographic optimization problem, allowing the use of a simple single-objective evolutionary algorithm to solve this multi-objective problem. The fitness function is of key importance for such wrapper methods. It is responsible for guiding the search towards potentially good solutions and it also consumes most of the runtime. Having these issues in mind, this paper also proposes a new lexicographic fitness function, designed to minimize the runtime of the algorithm and also to avoid over-fitting. Furthermore, the execution time and the quality of the results obtained by the wrapper procedure also depend on some algorithmic hyperparameters: the similarity thresholds used when comparing two different solutions lexicographically and the percentage of data samples used for validation during the training process. Thus, an experimental analysis has been carried out to find adequate values for these hyperparameters. Finally, the lexicographic cooperative co-evolutionary wrapper approach, using the new fitness function proposed in this paper, has been tested with several datasets belonging to the University of California, Irvine (UCI) repository and also with some real high-dimensional datasets, obtaining quite good results, compared to other state-of-the-art wrapper methods. The comparison has also been made lexicographically, with a new methodology proposed in this paper. 2021-10-19T12:29:14Z 2021-10-19T12:29:14Z 2021-08-11 journal article Jesús González... [et al.]. A lexicographic cooperative co-evolutionary approach for feature selection, Neurocomputing, Volume 463, 2021, Pages 59-76, ISSN 0925-2312, [https://doi.org/10.1016/j.neucom.2021.08.003] http://hdl.handle.net/10481/70979 10.1016/j.neucom.2021.08.003 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ open access Atribución-NoComercial-SinDerivadas 3.0 España Elsevier