Study on Impact Factor of Sci-Tech Journal in China Using Genetic Programming

摘要

In this paper, we establish nonlinear GP model between impact factor of sci-tech journal and related indexes based on genetic programming approach. The proposed GP model utilizes average authors, number of district, number of affiliation, international paper ratio and foundation paper ratio as the inputs, and uses impact factor as the output. The journals data from Chinese S&T Journal Citation Reports in 2005 are used as experimental data. The experimental results show that impact factor is mainly related to average authors and foundation paper ratio, and nearly has nothing to do with number of district, number of affiliation and international paper ratio. Therefore, increasing the average authors and foundation paper ratio of sci-tech journal will help to promote the impact factor of journal and improve the quality of journal to some extent.