Alejandra Casado
Universidad Rey Juan Carlos
Madrid, Spain
Predoctoral Researcher
Bio
Alejandra Casado is a predoctoral researcher at Universidad Rey Juan Carlos (URJC) since October 2021 and a member of the Group for Research in Algorithms For Optimization (GRAFO). She graduated in Videogame Design and Development with a degree in Computer Engineering. Her profile is completely programming-oriented to artificial intelligence and, specifically, to the resolution of combinatorial optimization problems.
She has 6 research works with 20 citations, focusing on optimization algorithms and metaheuristics. She completed her Master in Artificial Intelligence Research at Universidad Internacional Menéndez Pelayo in collaboration with AEPIA (Spanish Association for Artificial Intelligence).
Research Focus
Metaheuristics: Her research focuses on developing and improving metaheuristic algorithms, particularly GRASP (Greedy Randomized Adaptive Search Procedure) algorithms with Tabu Search improvements for solving complex optimization problems.
Graph Theory Applications: She specializes in dominating set problems in graphs, monitor placement problems, and maximum diversity problems, applying various optimization techniques to these challenging domains.
Genetic Algorithms: Her work includes research on biased random key genetic algorithms and variable neighborhood search approaches for optimization problems.
Artificial Intelligence: She applies AI techniques to solve real-world optimization problems, combining theoretical research with practical applications.
Education
- MEng in Artificial Intelligence Research - Universidad Internacional Menéndez Pelayo (2022)
- BSc in Videogame Design and Development - Universidad Rey Juan Carlos (2021)
Key Publications
- “A GRASP algorithm with Tabu Search improvement for solving the maximum intersection of k-subsets problem” - Journal of Heuristics
- “Variable neighborhood search approach with intensified shake for monitor placement” - Networks
- Research on biased random key genetic algorithms for domination problems
Current Research Projects
- CYNAMON-CM Project - Cybersecurity, Network Analysis and Monitoring for the Next Generation Internet (R&D project funded by the Community of Madrid)
Research Applications
- Maximum Intersection of k-subsets Problem - GRASP and Tabu Search hybrid approaches
- Monitor Placement Problems - Variable neighborhood search methodologies
- Dominating Set Problems - Graph-based optimization techniques
- Maximum Diversity Problems - Combinatorial optimization applications
Affiliations
- GRAFO Research Group - Group for Research in Algorithms For Optimization, URJC
- AEPIA - Spanish Association for Artificial Intelligence (through master’s collaboration)
Interests
Artificial Intelligence | Metaheuristics | Combinatorial Optimization | Graph Theory | GRASP Algorithms | Genetic Algorithms
