Fast feature selection in a GPU cluster using the Delta Test Guillén Perales, Alberto García Arenas, María Isabel Heeswijk, Mark van Sovilj, Dusan Lendasse, Amaury Herrera Maldonado, Luis Javier Pomares Cintas, Héctor Emilio Rojas Ruiz, Ignacio General purpose computing on graphics processing units (GPGPU) Feature selection Variable selection Big data Feature or variable selection still remains an unsolved problem, due to the infeasible evaluation of all the solution space. Several algorithms based on heuristics have been proposed so far with successful results. However, these algorithms were not designed for considering very large datasets, making their execution impossible, due to the memory and time limitations. This paper presents an implementation of a genetic algorithm that has been parallelized using the classical island approach, but also considering graphic processing units to speed up the computation of the fitness function. Special attention has been paid to the population evaluation, as well as to the migration operator in the parallel genetic algorithm (GA), which is not usually considered too significant; although, as the experiments will show, it is crucial in order to obtain robust results. 2014-05-23T11:01:46Z 2014-05-23T11:01:46Z 2014 info:eu-repo/semantics/article Guillén, A.; et al. Fast feature selection in a GPU cluster using the Delta Test. Entropy, 16: 854-869 (2014). [http://hdl.handle.net/10481/31886] 1099-4300 http://hdl.handle.net/10481/31886 10.3390/e16020854 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License MDPI