A New GEP Algorithm and Its Applications in Vegetable Price Forecasting Modeling Problems
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 precisi
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College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China [email protected], [email protected] 2 Institute of Automation, Chinese Academy of Sciences, Beijing, China [email protected]
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 conver‐ gence 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. Keywords: Gene expression programming · Vegetable prices prediction · Utilization of gene · Gene extraction
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Introduction
Vegetable price forecast can improve the forecast of the price of vegetables, take meas‐ ures to slow the price fluctuation and keep the market price stable. From the point of domestic and foreign research dynamic, short-term prediction in the social economy, the power load [1] and other fields has made great progress. In agricultural products market price prediction, Henry had carried on the regression prediction on American cotton yield, the prediction results are more accurate than the United States department of agriculture forecast [2]. Sarle studied the relationship of market price influence factor of live pig, and established the prediction equation of hog price by using sample data, and the goodness of fit was up to 0.75 [3]. Jarrett uses the exponential smoothing method to estimate the price of wool in Australia [4]. Schmitz and Watts predict the price of live pigs with expo‐ nential smoothing and Box - Jenkins method [5]. Cui Guoli uses the chaotic neural network model and ARIMA model to build model and forecast the price trend of Chinese cabbage in the next 10 days. The results show that the average relative error between the chaotic neural network model and the actual price is relatively small [6]. Zhu Xiaoxia verified the vegetable price fluctuation cycle of predictability by using markov chain simulation analysis [7]. In summary, the research on vegetable price forecasting is less, and the existing forecasting methods mainly adopt the econometric methods, and © Springer Science+Business Media Singapore 2016 K. Li et al. (Eds.): ISICA 2015, CCIS 575, pp. 139–149, 2016. DOI: 10.1007/978-981-10-0356-1_14
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domestic researches mainly use neural networks, the prediction based on intelligent algo‐ rithm is not much. GEP is a new evolutionary algorithm based on genotype and phenotype, it automat‐ ically creates a function expression by using the function set and the terminator, and the evolution process is more easy to operate, and has the stronger ability to solve problems. Gene
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