Study on the Macroeconomic Model Based on the Genetic Algorithm - Sun Shihao; Yixin Zhou; Yong Wang; Wu Wang (Applied Bionics and Biomechanics)
In order to design a more reliable general push time cycle prediction software for macroeconomic indicators, a set of general software is used to serve financial transactions, bulk material transactions, international trade, macro-control and other fields, so as to improve the prediction of macroeconomic indicators. Because the macro data is one-dimensional array data, the essence of the mutation algorithm is to obtain the movement direction of the mutation of data nodes, obtain the distance between the linear programming result and the original data through the least square method, and calculate the average value in the original data, After binary t-correction, it refers to the binary t-correction results of the one-dimensional matrix before the final evaluation output factor and the one-dimensional matrix after the final evaluation output factor. In this study, genetic algorithm is introduced as the core algorithm. In the algorithm efficiency verification test, the calculation model based on genetic algorithm is constructed in Matlab environment, and the data space construction mode and genetic variation mode of genetic algorithm are explored. Finally, a high-throughput macroeconomic timing prediction scheme based on genetic algorithm is designed. This scheme is more accurate than the paid full-function 10jqka software, and has a higher prediction cycle for stock price and stock index. The simulation software composed of this algorithm has the prediction function that 10jqka software cannot complete.