A novel PPGA-based clustering analysis method for business cycle indicator selection

A new clustering analysis method based on the pseudo parallel genetic algorithm (PPGA) is proposed for business cycle indicator selection. In the proposed method, the category of each indicator is coded by real numbers, and some illegal chromosomes are repaired by the identification and restoration of empty class. Two mutation operators, namely the discrete random mutation operator and the optimal direction mutation operator, are designed to balance the local convergence speed and the global convergence performance, which are then combined with migration strategy and insertion strategy. For the purpose of verification and illustration, the proposed method is compared with the K-means clustering algorithm and the standard genetic algorithms via a numerical simulation experiment. The experimental result shows the feasibility and effectiveness of the new PPGA-based clustering analysis algorithm. Meanwhile, the proposed clustering analysis algorithm is also applied to select the business cycle indicators to examine the status of the macro economy. Empirical results demonstrate that the proposed method can effectively and correctly select some leading indicators, coincident indicators, and lagging indicators to reflect the business cycle, which is extremely operational for some macro economy administrative managers and business decision-makers.