An improved Gene Expression Programming approach for symbolic regression problems

Peng, Yuzhong; Yuan, Changan; Qin, Xiao; Huang, Jiangtao; Shi, Yabing

doi:10.1016/j.neucom.2013.05.062

Cited by 59 publications

(22 citation statements)

References 9 publications

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“…Herein, the proposed algorithm is employed to estimate the relationship between the rotation capacity and other properties of the steel beam. The third problem is to predict the trend of the sunspot number time series 24 . In this case, 100 annual observations of the Wolfer sunspots series from 1770 to 1869 is employed.…”

Section: Methodsmentioning

confidence: 99%

“…We refer the reader to 31,24,4 for a more detailed discussion on GEP and its wide applications. The simulation results show that the GEP algorithm achieves a much better performance, which surpasses GA and GP by more than two orders of magnitude.…”

Section: Gep Algorithmmentioning

confidence: 99%

“…Numerical experiments demonstrate that GEP has a significantly better performance compared to genetic algorithm (GA) 27,30 and genetic programming (GP) 1,17 , and surpasses those conventional methods by more than two orders of magnitude 10,11,12 . However, GEP has also been shown to have certain disadvantages, such as slow convergence and low solution accuracy, particularly for problems with a high-dimensional and large space 4,6,24 .…”

Section: Introductionmentioning

confidence: 99%

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A hybrid gene expression programming algorithm based on orthogonal design

Yang¹,

Ma²

2016

IJCIS

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The last decade has witnessed a great interest on the application of evolutionary algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) and gene expression programming (GEP), for optimization problems. This paper presents a hybrid algorithm by combining the GEP algorithm and the orthogonal design method. A multiple-parent crossover operator is introduced for the chromosome reproduction using the orthogonal design method. In addition, an evolutionary stable strategy is also employed to maintain the population diversity during the evolution. The efficiency of the proposed algorithm is evaluated using three benchmark problems. The results demonstrate that the proposed hybrid algorithm has a better generalization ability compared to conventional algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Gep Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid gene expression programming algorithm based on orthogonal design

Yang¹,

Ma²

2016

IJCIS

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“…Ferreria (2011) developed a version of GP known as gene expression programming (GEP). Recently, Peng et al (2014) proposed an improved GEP algorithm especially suitable for dealing with SR problems. Gandomi and Roke (2015) compared the forecasting performance of artificial neural network models to that of GEP techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

2017

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“…Zhu 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, Peng 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, Karakasis et al proposed a hybrid evolutionary technique by combining GEP with artificial immune system [32].…”

Section: Function Miningmentioning

confidence: 99%

Distributed Global Function Model Finding for Wireless Sensor Network Data

Deng

Yang

et al. 2016

Applied Sciences

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Abstract:Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.

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An improved Gene Expression Programming approach for symbolic regression problems

Cited by 59 publications

References 9 publications

A hybrid gene expression programming algorithm based on orthogonal design

A hybrid gene expression programming algorithm based on orthogonal design

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

Distributed Global Function Model Finding for Wireless Sensor Network Data

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