Genetic Algorithm for Feature Selection

Version 1.3 (62.1 KB) by Jingwei Too

Simple algorithm shows how the genetic algorithm (GA) used in the feature selection problem.

https://github.com/JingweiToo/Genetic-Algorithm-for-Feature-Selection

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Updated 19 Dec 2020

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Simple genetic algorithm (GA) for feature selection tasks, which can select the potential features to improve the classification accuracy.

The < Main.m file > illustrates the example of how GA can solve the feature selection problem using a benchmark data-set.

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Cite As

Too, Jingwei, and Abdul Rahim Abdullah. “A New and Fast Rival Genetic Algorithm for Feature Selection.” The Journal of Supercomputing, Springer Science and Business Media LLC, July 2020, doi:10.1007/s11227-020-03378-9.

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Too, Jingwei, et al. “EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization.” Computation, vol. 7, no. 1, MDPI AG, Feb. 2019, p. 12, doi:10.3390/computation7010012.

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MATLAB Release Compatibility

Created with R2018a

Compatible with any release

Platform Compatibility

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Version	Published	Release Notes
1.3	19 Dec 2020	See release notes for this release on GitHub: https://github.com/JingweiToo/Genetic-Algorithm-for-Feature-Selection/releases/tag/1.3	Download
1.2.1	22 Oct 2020	Solve one bug in the fitness function	Download
1.2	22 Oct 2020	Improve code for the fitness function	Download
1.1.0	23 Jul 2020	change to hold-out	Download
1.0.0	22 Jul 2020		Download

To view or report issues in this GitHub add-on, visit the GitHub Repository.

MLA	Too, Jingwei, and Abdul Rahim Abdullah. “A New and Fast Rival Genetic Algorithm for Feature Selection.” The Journal of Supercomputing, Springer Science and Business Media LLC, July 2020, doi:10.1007/s11227-020-03378-9.
APA	Too, J., & Abdullah, A. R. (2020). A new and fast rival genetic algorithm for feature selection. The Journal of Supercomputing. Springer Science and Business Media LLC. Retrieved from https://doi.org/10.1007%2Fs11227-020-03378-9
BibTeX	@article{Too_2020, doi = {10.1007/s11227-020-03378-9}, url = {https://doi.org/10.1007%2Fs11227-020-03378-9}, year = 2020, month = {jul}, publisher = {Springer Science and Business Media {LLC}}, author = {Jingwei Too and Abdul Rahim Abdullah}, title = {A new and fast rival genetic algorithm for feature selection}, journal = {The Journal of Supercomputing} }

MLA	Too, Jingwei, et al. “EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization.” Computation, vol. 7, no. 1, MDPI AG, Feb. 2019, p. 12, doi:10.3390/computation7010012.
APA	Too, J., Abdullah, A., Saad, N. M., & Tee, W. (2019). EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization. Computation, 7(1), 12. MDPI AG. Retrieved from https://doi.org/10.3390%2Fcomputation7010012
BibTeX	@article{Too_2019, doi = {10.3390/computation7010012}, url = {https://doi.org/10.3390%2Fcomputation7010012}, year = 2019, month = {feb}, publisher = {{MDPI} {AG}}, volume = {7}, number = {1}, pages = {12}, author = {Jingwei Too and Abdul Abdullah and Norhashimah Mohd Saad and Weihown Tee}, title = {{EMG} Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization}, journal = {Computation} }