Resumen
Greedy randomized adaptive search procedure (GRASP) is a metaheuristic framework which has been extensively used for solving a wide variety of hard combinatorial optimization problems. Several diversity maximization problems have considered GRASP either as the main metaheuristic or even as a part of a hybrid algorithm, mainly due to its versatility to be adapted to any optimization problem. This chapter is focused on reviewing the most recent works considering GRASP for maximizing diversity and proposing a basic design and implementation of GRASP in the context of diversity problems. The resulting design is evaluated over the MDPLIB 2.0, which has become a de facto standard test bed for this family of problems.
Sergio Pérez-Peló
Doctor en Inteligencia Artificial
Estudiante de doctorado en la Universidad Rey Juan Carlos
Jesús Sánchez-Oro
Profesor Titular de Universidad
Profesor Titular del Departamento de Informática, siendo uno de los investigadores principales del Grupo de Investigación de Algoritmos para la Optimización GRAFO.
Abraham Duarte
Catedrático de Universidad
Mi carrera investigadora se ha centrado en el desarrollo de nuevos algoritmos y técnicas de Inteligencia Computacional (metaheurísticas) y su aplicación a diferentes problemas en Ciencia e Ingeniería desde que me incorporé a la Universidad Rey Juan Carlos (URJC) en el octubre del año 2000.