Feature Extraction (FE) and Feature Selection (FS) are the
most important steps in classification systems. One approach in the
feature selection area is employing population-based optimization algorithms
such as Particle Swarm Optimization (PSO)-based method and
Ant Colony Optimization (ACO)-based method. This paper presents a
novel feature selection method that is based on Ant Colony Optimization
(ACO). This approach is easily implemented and because of use of
a simple classifier in that, its computational complexity is very low. The
performance of proposed algorithm is compared to the performance of
standard binary PSO algorithm on the task of feature selection in Postsynaptic
dataset. Simulation results on Postsynaptic dataset show the
superiority of the proposed algorithm.