Carlos Rey Barra
PhD in Biomedical, Electrical and System Engineering. Alma Mater Studiorum Università di
Bologna, Italia.
Magíster en Ingeniería Informática. Universidad de Santiago.
Ingeniero Informático. Universidad de Santiago.
Licenciado en Ingeniería Aplicada. Universidad de Santiago.
Líneas de investigación:
La aplicación de la Computación Evolutiva para la Generación Autónoma de Algoritmos en el ámbito de los desafíos de optimización combinatoria. Este enfoque integral puede incorporar elementos de estrategias heurísticas, metaheurísticas, soluciones exactas y algoritmos avanzados de Machine Learning. Todo ello con el objetivo de promover una rápida convergencia hacia soluciones óptimas y eficientes.
Docencia
Machine Learning e Inteligencia Artificial (Magister)
Algoritmos y Programación para Ingeniería Industrial (Pregrado)
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Galli, L., Martello, S., Rey, C., & Toth, P. (2023). Lagrangian matheuristics for the Quadratic Multiple Knapsack Problem. Discrete Applied Mathematics, 335, 36-51.
Derpich, I., & Rey, C. (2023). Drone Optimization in Factory: Exploring the Minimal Level Vehicle Routing Problem for Efficient Material Distribution. Drones, 7(7), 435.
Osorio?Mora, A., Rey, C., Toth, P., & Vigo, D. (2023). Effective metaheuristics for the latency location routing problem. International Transactions in Operational Research.
Silva-Muñoz, M., Contreras-Bolton, C., Rey, C., & Parada, V. (2023). Automatic generation of a hybrid algorithm for the maximum independent set problem using genetic programming. Applied
Soft Computing, 110474.
Galli, L., Martello, S., Rey, C., & Toth, P. (2021). Polynomial-size formulations and relaxations for the quadratic multiple knapsack problem. European Journal of Operational Research, 291(3), 871-
882.
Rey, C., Toth, P., & Vigo, D. (2020, November). An Iterated Local Search for the Traveling Salesman Problem with Pickup, Delivery and Handling Costs. In 2020 39th International Conference of the Chilean Computer Science Society (SCCC) (pp. 1-8). IEEE.
Acevedo, N., Rey, C., Contreras-Bolton, C., & Parada, V. (2020). Automatic design of specialized algorithms for the binary knapsack problem. Expert Systems with Applications, 141, 112908.
Bertolini, V., Rey, C., Sepulveda, M., & Parada, V. (2018). Novel methods generated by geneti programming for the guillotine-cutting problem. Scientific Programming, 2018.
Contreras-Bolton, C., Gatica, G., Barra, C. R., & Parada, V. (2016). A multi-operator genetic algorithm for the generalized minimum spanning tree problem. Expert Systems with applications, 50, 1-8.Contreras-Bolton, C., Rey, C., Ramos-Cossio, S., Rodríguez, C., Gatica, F., & Parada, V. (2016). Automatically produced algorithms for the generalized minimum spanning tree problem. Scientific Programming, 2016.
