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. © 2014 by the Authors.
Recommended Citation
A. Guillén et al., "Fast Feature Selection in a GPU Cluster using the Delta Test," Entropy, vol. 16, no. 2, pp. 854 - 869, MDPI, Jan 2014.
The definitive version is available at https://doi.org/10.3390/e16020854
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Big data; Feature selection; General-purpose computing on graphics processing units (GPGPU); Variable selection
International Standard Serial Number (ISSN)
1099-4300
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2014
