Feature Selection via Genetic Optimization

Sancho Salcedo-Sanz, Mario de Prado-Cumplido, Fernando Pérez-Cruz, Carlos Bousoño-Calzón

Abstract: In this paper we present a novel Genetic Algorithm (GA) for feature selection in machine learning problems. We introduce a novel genetic operator which fixes the number of selected features. This operator, we will refer to it as m-features operator, reduces the size of the search space and improves the GA performance and convergence. Simulations on synthetic and real problems have shown very good performance of the m-features operator, improving the performance of other existing approaches over the feature selection problem.