Abstract
In this paper, a new Gene Expression Programming (GEP) algorithm is proposed, which increase “inverted series” and “extract” operator. The new algorithm can effectively increase the rate of utilization of genes, with convergence speed and solution precision is higher. Taking the Chinese vegetables price change trend of mooli, scallion as example, and discuss the way to solve the forecasting modeling problem by adopting GEP. The experimental results show that the new GEP Algorithm can not only increase the diversity of population but overcome the shortage of primitive GEP. In addition, it can improve convergence accuracy compared to original GEP.
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Acknowledgment
This work was partially supported by Science and Technology Project of Guangdong Province of China (Grant Nos. 2015A020209119 and 2014A020208087), National Natural Science Foundation of China (Grant No. 61573157), and Science and Technology Project of Guangdong Province of China (Grant No. 2012BM0500054), Fund of Natural Science Foundation of Guangdong Province of China (Grant No. S2013040015755), the National Spark Technology Project (Grant Nos. 2014GA780051 and 2013GA780044). We would like to thank the anonymous reviewers for their valuable comments that greatly helped us to improve the contents of this paper.
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Yang, L., Li, K., Zhang, W., Kong, Y. (2016). A New GEP Algorithm and Its Applications in Vegetable Price Forecasting Modeling Problems. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_14
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DOI: https://doi.org/10.1007/978-981-10-0356-1_14
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