A parallel genetic algorithm of feature selection for analysis of complex system

Abstract

The paper shows an approach of important features selection characterizing the evolution of the complex system. A parallel genetic algorithm is proposed to solve this problem. As a result of the proposed approach, the search for the best number of parallel evolutionary paths for the feature selection is carried out. The effectiveness of the proposed approach is demonstrated on the basis of the data analysis of production enterprise functioning. This paper also shows comparison results of parallel genetic algorithm with other algorithms of feature selection by standard deviation, Fisher criterion and multiple determination coefficient.