[1]PENG Yuzhong,YUAN Changan,LI Jie,et al.Individual optimal sharing GEP algorithm and its application in forecast modeling of meteorological precipitation[J].CAAI Transactions on Intelligent Systems,2016,11(3):401-409.[doi:10.11992/tis.201603035]
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Individual optimal sharing GEP algorithm and its application in forecast modeling of meteorological precipitation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 3 Page number: 401-409 Column: 学术论文—智能系统 Public date: 2016-06-25
Title:
Individual optimal sharing GEP algorithm and its application in forecast modeling of meteorological precipitation
Author(s):
PENG Yuzhong1; 2;  YUAN Changan1;  LI Jie3;  XU Mingtao1;  CHEN Binglian1
1. College of Computer & Information Engineering, Guangxi Normal University, Nanning 530023, China;
2. Key Lab of Beibu Gulf Environment Change and Resource Use of ministry of Education, Guangxi Normal University, Nanning 530001, China;
3. Departmen
Keywords:
gene expression programming; experience sharing; time series; meteorology modeling; precipitation forecasting; evolutionary computation; evolution modeling
CLC:
TP391
DOI:
10.11992/tis.201603035
Abstract:
Gene expression programming (GEP) is characterized by slow convergence and ease of falling into a local optimum in the later stages of its evolution. Many methods are difficult to model and use to accurately forecast precipitation because of the simultaneous influence of many natural factors. In this paper, we propose an improved GEP algorithm, which has an optimal state memory function, can learn from historical experience in the process of evolution to strengthen the local search ability, and can thus promote convergence and, at the same time, control the convergence of individuals and maintain the diversity of the population. The experimental results of three groups from different regions and different actual precipitation data sets show that the proposed algorithm can improve the slow convergence problem of the traditional GEP algorithm and has better search ability. Experimental results also show that the proposed algorithm’s ability to fit and forecast precipitation data is significantly better than that of traditional GEP algorithm, as well as the BP and NAR neural network algorithms.
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