Manuel Villalobos-Cid

TL;DR: The proposed MO-MA is able to identify a Pareto set of solutions that include new trees which were nondominated by solutions from the current state of the art single-objective optimization tools, and improves the results presented in the literature for multiobjective approaches in all of the studied data sets.

TL;DR: A new approach for evaluating the efficiency of hospitals is proposed that uses a graph-based clustering algorithm to find groups of hospitals that have similar production profiles and DEA is used to evaluate the technical efficiency of each group.

TL;DR: This work uses clustering techniques to compare both spaces by considering different topological metrics designed to contrast phylogenetic trees, and four different criteria used to infer phylogeny, and shows that the decision space is surjective but not injective, and it is not related to the objective space.

TL;DR: Three different local search strategies that were implemented as part of a memetic algorithm for the molecular docking problem showed that one of the techniques achieved the best free energy values in 90% of the tests.

TL;DR: The results show that the multi-objective decision making techniques applied over the objective space do not consider the topological features of the trees, which uncovers the need for the designing of new strategies which consider information from the tree-space in the evolution of the algorithms.