Abstract
Diversity problems are usually studied from a single-objective point of view. However, two or more diversity functions could present opposite or divergent behavior, which requires a multi-objective point of view. To illustrate this kind of problems, this chapter presents the study of the bi-objective diversity problem (BODP), which considers the MaxSum and the MaxMin as objective functions to simultaneously maximize. Six different multi-objective algorithms have been described, analyzing their results on six performance metrics using a subset of instances from the MDPLIB 2.0 library.
J. Manuel Colmenar
Associate Professor
My research interests are focused on metaheuristics applied to optimization problems. I have worked on different combinatorial optimization problems applying trajectorial algorithms such us GRASP or VNS. Besides, I am very interested in applications of Grammatical Evolution, specifically in model and prediction domain, as alternative to machine learning approaches.
Alberto Herrán González
Associate Professor
