Scopus Author ID: 8940887100
Alejandro Rosales-Pérez received the B.S. degree in Electronic Engineering from the Instituto Tecnológico de Tuxtla Gutiérrez, Chiapas, Mexico, in 2008, and the M.Sc. and Ph.D. degrees in Computer Science from the Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico, in 2011 and 2016, respectively. He was a professor-researcher at the Tecnológico de Monterrey, Monterrey, Mexico, where he was a member of the Research Group with Strategic Focus in Intelligent Systems. Since 2020, Dr. Rosales-Pérez is with Centro de Investigación en Matemáticas, A.C. (CIMAT), where he is currently a researcher associate C.
Dr. Rosales-Pérez was a recipient of the Award by the National Association of Education Institutions in Information Technology in 2016 for his Doctoral Thesis. His thesis was the first runner-up in National Contest on Artificial Intelligence, granted by the Mexican Society on Artificial Intelligence in 2016. Since 2017, Dr. Rosales-Pérez is a member of the Mexican Academy for Computing. He has been recognized by the Mexican National System of Researchers with Level I, 2017-2019, supported by the Mexican National Council for Science and Technology.
Research InterestMy research interests include the areas of Machine Learning (ML) and Evolutionary Computation (EC). On the one hand, ML tasks, such as feature selection, classification, ensemble learning, among others, can be modeled as an optimization problem, often with two or more conflicting objectives. Furthermore, many of these problems can have many local optima. Ergo, the global search habilities of EC techniques may be exploited to explore the search space more effectively and efficiently.
On the other hand, EC techniques can generate a large amount of information concerning the search space, the characteristics of the problem, the objective function, and other processes during the search. Therefore, ML techniques may empower the search capabilities of EC.