Binary Grey Wolf Optimization for Feature Selection

Demonstration on how binary grey wolf optimization (BGWO) applied in the feature selection task.

https://github.com/JingweiToo/Binary-Grey-Wolf-Optimization-for-Feature-Selection

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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

General Information

MATLAB Release Compatibility

  • Compatible with any release

Platform Compatibility

  • Windows
  • macOS
  • Linux
Version Published Release Notes Action
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

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