@misc{10481/31886,
year = {2014},
url = {http://hdl.handle.net/10481/31886},
abstract = {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.},
organization = {This work was supported in part by the Consejería de Innovación, Ciencia y Empresa of the Spanish Junta de Andalucía, under Project TIC2906 and in part by the Spanish Ministry of Science and Innovation
under Project SAF2010-20558.},
publisher = {MDPI},
keywords = {General purpose computing on graphics processing units (GPGPU)},
keywords = {Feature selection},
keywords = {Variable selection},
keywords = {Big data},
title = {Fast feature selection in a GPU cluster using the Delta Test},
doi = {10.3390/e16020854},
author = {Guillén Perales, Alberto and García Arenas, María Isabel and Heeswijk, Mark van and Sovilj, Dusan and Lendasse, Amaury and Herrera Maldonado, Luis Javier and Pomares Cintas, Héctor Emilio and Rojas Ruiz, Ignacio},
}