- This toolbox offers an Equilibrium Optimizer ( EO ) method
- The
Main
file illustrates the example of how EO can solve the feature selection problem using benchmark data-set.
feat
: feature vector ( Instances x Features )label
: label vector ( Instances x 1 )N
: number of particlesmax_Iter
: maximum number of iterationsa1
: Parametera2
: ParameterGP
: Generation rate control parameter
sFeat
: selected featuresSf
: selected feature indexNf
: number of selected featurescurve
: convergence curve
% Benchmark data set
load ionosphere.mat;
% Set 20% data as validation set
ho = 0.2;
% Hold-out method
HO = cvpartition(label,'HoldOut',ho);
% Parameter setting
N = 10;
max_Iter = 100;
a1 = 2; % constant
a2 = 1; % constant
GP = 0.5; % generation probability
% Equilibrium Optimizer
[sFeat,Sf,Nf,curve] = jEO(feat,label,N,max_Iter,a1,a2,GP,HO);
% Plot convergence curve
plot(1:max_Iter,curve);
xlabel('Number of iterations');
ylabel('Fitness Value');
title('EO'); grid on;
- MATLAB 2014 or above
- Statistics and Machine Learning Toolbox
@article{too2020general,
title={General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification},
author={Too, Jingwei and Mirjalili, Seyedali},
journal={Applied Artificial Intelligence},
pages={1--17},
year={2020},
publisher={Taylor \& Francis}
}