Binary Grey Wolf Optimization for Feature Selection

Versión 1.3 (62,1 KB) por Jingwei Too
Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.
1,7K descargas
Actualizado 19 dic 2020

This toolbox offers two types of binary grey wolf optimization (BGWO) methods

The < Main.m file > demos the examples of how BGWO solves the feature selection problem using benchmark data-set.

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Please consider citing my article
[1] Too, Jingwei, et al. “A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification.” Computers, vol. 7, no. 4, MDPI AG, Nov. 2018, p. 58, DOI:https://doi.org/10.3390/computers7040058

[2] Too, Jingwei, and Abdul Rahim Abdullah. “Opposition Based Competitive Grey Wolf Optimizer for EMG Feature Selection.” Evolutionary Intelligence, Springer Science and Business Media LLC, July 2020, DOI: https://doi.org/10.1007/s12065-020-00441-5

Compatibilidad con la versión de MATLAB
Se creó con R2018a
Compatible con cualquier versión
Compatibilidad con las plataformas
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Versión Publicado Notas de la versión
1.3

See release notes for this release on GitHub: https://github.com/JingweiToo/Binary-Grey-Wolf-Optimization-for-Feature-Selection/releases/tag/1.3

1.2

Improve code for the fitness function

1.1.0

Change to hold-out

1.0.6

-

1.0.5

-

1.0.4

-

1.0.3

Simplify BGWO1 program.

1.0.2

-

1.0.1

-

1.0.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.