Feature Subset Selection Based on the Genetic Algorithm

Abstract

This paper presents a genetic-based feature selection algorithm for object recognition. Firstly, the proposed algorithm encodes a solution with a binary chromosome. Secondly, the initial population was generated randomly. Thirdly, a crossover operator and a mutation operator are employed to operate on these chromosomes to generate more competency chromosomes. The probability of the crossover and mutation are adjusted dynamically according to the generation number and the fitness value. The proposed algorithm is tested using the features extracted from cotton foreign fiber objects. The results indicate that the proposed algorithm can obtain the optimal feature subset, and can reduce the classification time while keeping the classification accuracy constant.