# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 2 Related Work
### Function Mining  
At present, research on GEP focused on the basic theory of algorithm, symbolic regression, function finding, prediction, security assessment, other application areas, and so forth. In algorithm theory, in order to solve the problem that fitness distance correlation could hardly predict the evolution difficulty of gene expression programming, [PERSON] _et al._ proposed gene expression programming evolution difficulty prediction based on posture model [28]. [PERSON] _et al._ simplified operators of GEP and proposed a robust gene expression programming algorithm [29]. [PERSON] _et al._ present naive gene expression programming (NGEP) based on genetic neutrality that combined with neutral theory of molecular evolution [30]. In symbolic regression and function mining, [PERSON] _et al._ proposed an improved GEP algorithm named S_GEP, which is especially suitable for dealing with symbolic regression problems [31]. To better improve efficiency and accuracy of classification, [PERSON] _et al._ proposed a hybrid evolutionary technique by combining GEP with artificial immune system [32]. In order to better model the compressive strength of different types of geopolymers, GEP had been employed. The model showed that GEP had a strong potential for predicting the compressive strength of different types of geopolymers [33]. In view of insufficiency of the existing forecasting model on highway construction cost forecasting, highway construction cost forecasting model was proposed based on the GEP according to the characteristic of highway construction cost forecasting [34]. [PERSON] proposed a function finding algorithm by gene expression programming for strength and elastic properties of clay treated with bottom ash in order to understand the treatment of a marginal soil well [35]. [PERSON] _et al._ treated image registration as a formula discovery problem, and proposed two-stage gene expression programming and the improved cooperative particle swarm optimizer used to identify the registration formula for the reference image and the floating image [36]. In prediction, [PERSON] _et al._ posed gene expression programming on Taiwan stock investment [37]. [PERSON] _et al._ proposed the prediction of electricity demand based on GEP [38]. [PERSON] _et al._ applied parallel hyper-cubic gene expression programming to estimate the slump flow of high-performance concrete [39].  
[PERSON] _et al._ applied gene expression programming to short-term load forecasting on power systems, and proposed the model error cycling compensation [40]. Forecasting results indicated that the model was of high prediction efficiency. [PERSON] _et al._ used gene expression programming to design a new model for the prediction of compressive strength of high performance concrete (HPC) mixes [41]. Experiments showed that prediction performance of the optimal GEP model is better than the regression models. In security assessment and other application areas, [PERSON] _et al._ introduced gene expression programming into power system static security assessment [42]. To better design sensor equivalent circuit, [PERSON] _et al._ used GEP to determine a suitable equivalent circuit and choose a circuit component [43]. For combinatorial optimization problems, [PERSON] _et al._ present a dynamic multiarmed bandit-gene expression programming hyper-heuristic [44]. [PERSON] _et al._ provided revised gene expression programming to construct the model for music emotion recognition [45]. However, these algorithms do not involve distributed function mining.