Function Finding Using Gene Expression Programming Based Neural Network

2008 ◽  
Author(s):  
Qu Li ◽  
Weihong Wang ◽  
Xing Qi ◽  
Bo Chen ◽  
Jianhong Li
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Gene Expression Programming ◽  
Function Finding

scholarly journals Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

2020 ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

Abstract Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

Population Diversity Strategy in Gene Expression Programming

2011 ◽  
Vol 204-210 ◽  
pp. 288-292 ◽  
Author(s):  
Yong Qiang Zhang ◽  
Jing Xiao
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Population Diversity ◽  
Population Average ◽  
Population Strategy ◽  
Function Finding ◽  
Diversity Strategy ◽  
Average Fitness

Population diversity is one of the most important factors that influence the convergence speed and evolution efficiency of gene expression programming (GEP) algorithm. In this paper, the population diversity strategy of GEP (GEP-PDS) is presented, inheriting the advantage of superior population producing strategy and various population strategy, to increase population average fitness and decrease generations, to make the population maintain diversification throughout the evolutionary process and avoid “premature” to ensure the convergence ability and evolution efficiency. The simulation experiments show that GEP-PDS can increase the population average fitness by 10% in function finding, and decrease the generations for convergence to the optimal solution by 30% or more compared with other improved GEP.

scholarly journals A knowledge-guided and manual intervention-based gene expression programming for PM2.5 concentration prediction

2021 ◽  
Vol 269 ◽  
pp. 01011
Author(s):  
Chaoxue Wang ◽  
Xiaoli Jia ◽  
Fan Zhang ◽  
Yuhang Pan
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Influencing Factors ◽  
Gene Expression Programming ◽  
Learning Ability ◽  
Back Propagation Algorithm ◽  
Model Learning ◽  
Manual Intervention ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

In view of the lack of interpretation and inability to know the occurrence mechanism of PM2.5 concentration by deep learning algorithm in solving PM2.5 concentration prediction problem, this paper adopts a knowledge-guided and manual intervention-based gene expression programming (KMGEP) to solve it. The KMGEP algorithm not only has strong model learning ability, but also can obtain the explicit function relationship between PM2.5 concentration and its influencing factors. In the process of algorithm implementation, knowledge guidance and manual intervention are introduced to GEP for predicting PM2.5 concentration so as to improve its global optimization ability and convergence speed. In this paper, the daily PM2.5 concentration prediction in winter (from December to February) in Xi’an region is taken as an example, and the KMGEP algorithm is compared with the artificial neural network back propagation algorithm (BP-ANN) and the convolutional neural network and long short-term memory neural network combined model (CNN-LSTM). Experimental results show that the KMGEP algorithm not only has high prediction accuracy in solving the PM2.5 concentration prediction, but also the obtained function expression can reveal the occurrence relationship between PM2.5 concentration and its influencing factors.

scholarly journals Stock Market Forecasting Using Restricted Gene Expression Programming

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Bin Yang ◽  
Wei Zhang ◽  
Haifeng Wang
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Stock Market ◽  
Gene Expression Programming ◽  
Stock Index ◽  
System Model ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Deep Recurrent Neural Network ◽  
Prediction Problems

Stock index prediction is considered as a difficult task in the past decade. In order to predict stock index accurately, this paper proposes a novel prediction method based on S-system model. Restricted gene expression programming (RGEP) is proposed to encode and optimize the structure of the S-system. A hybrid intelligent algorithm based on brain storm optimization (BSO) and particle swarm optimization (PSO) is proposed to optimize the parameters of the S-system model. Five real stock market prices such as Dow Jones Index, Hang Seng Index, NASDAQ Index, Shanghai Stock Exchange Composite Index, and SZSE Component Index are collected to validate the performance of our proposed method. Experiment results reveal that our method could perform better than deep recurrent neural network (DRNN), flexible neural tree (FNT), radial basis function (RBF), backpropagation (BP) neural network, and ARIMA for 1-week-ahead and 1-month-ahead stock prediction problems. And our proposed hybrid intelligent algorithm has faster convergence than PSO and BSO.

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