Duration: 2 hours
Start: September 05, 16:30

Location: Aula A


Abstract:

Grouping problems are a family of combinatorial optimization problems that seek to identify an efficient distribution of an element set. We can find grouping problems in a wide range of everyday life situations, e.g., in industry, transportation, health, education, economics, and telecommunications, to mention a few examples. The Grouping Genetic Algorithm (GGA) is a variant of the traditional Genetic Algorithm, developed especially to address grouping problems, that uses a representation scheme based on groups and genetic variation operators that work at the group level. Many grouping problems belong to the NP-hard class, i.e., no solution method optimally solves all the possible cases of a grouping problem. Therefore, this is an open research area. The specialized literature includes different GGAs, which incorporate different genetic variation operators.

In this tutorial, we will start with a general introduction to grouping combinatorial optimization and GGAs. In the second part, we will focus on grouping variation operators. For this purpose, we will present different crossover and mutation operators to analyze their algorithm procedure and algorithmic behavior in solving the R||Cmax grouping problem. We close the tutorial discussing possible future research paths in this direction.

Literature:

Ramos-Figueroa, O., Quiroz-Castellanos, M., Mezura-Montes, E., & Kharel, R. (2021). Variation operators for grouping genetic algorithms: A review. Swarm and Evolutionary computation, 60, 100796.

Contact:

Dr. Marcela Quiroz-Castellanos This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Octavio Ramos Figueroa This email address is being protected from spambots. You need JavaScript enabled to view it.

Marcela Quiroz is a Full-Time Researcher with the Artificial Intelligence Research Institute at the Universidad Veracruzana in Xalapa City, Mexico. Her research interests include: combinatorial optimization, metaheuristics, experimental algorithms, characterization and data mining. She received her Ph.D. in Computer Science from the Instituto Tecnologico de Tijuana, Mexico. She studied engineering in computer systems and received the degree of master in computer science at the Instituto Tecnológico de Ciudad Madero, Mexico. She is a member of the Mexican National Researchers System (SNI), and also a member of the directive committees of the Mexican Computing Academy (AMexComp) and the Mexican Robotics Federation (FMR).

Octavio Ramos-Figueroa holds a postdoctoral position at the Artificial Intelligence Research Institute at Universidad Veracruzana (IIIA-UV) in Xalapa City, Mexico. His research interests include continuous and combinatorial optimization, experimental study of metaheuristic and hyper heuristic algorithms, characterization, data mining, and data science pipelines. He received his Ph.D. and the degree of master in Artificial Intelligence from the Artificial Intelligence Research Institute at the Universidad Veracruzana, Mexico. He studied engineering in information and communications technologies at the Instituto Tecnológico de Tepic, Mexico. He is a member of the Mexican National Researchers System (SNI)