Local search-embedded genetic algorithms for feature selection

This paper proposes a novel hybrid genetic algorithm for the feature selection. Local search operations used to improve chromosomes are defined and embedded in hybrid GAs. The hybridization gives two desirable effects: improving the final performance significantly and acquiring control of subset size. For the implementation reproduction by readers, we provide detailed information of GA procedure and parameter setting. Experimental results reveal that the proposed hybrid GA is superior to a classical GA and sequential search algorithms.