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Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar,India

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Information Processing in Agriculture 2023年第4期10卷 547-563页

作者： Pangam Heramb Pramod Kumar Singh K.V.Ramana Rao A.Subeesh Division of Irrigation and Drainage Engineering ICAR-Central Institute of Agricultural EngineeringBhopal 462038India Department of Irrigation and Drainage Engineering G.B.Pant University of Agriculture and TechnologyPantnagar 263145India Division of Agricultural Mechanization ICAR-Central Institute of Agricultural EngineeringBhopal 462038India

Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource planning.Its quantification is helpful in irrigation scheduling,water balance studies,water allocation,etc.Modelling of reference evapotranspiration(ET0)using both gene expression programming(GEP)and artificial neural network(ANN)techniques was done using the daily meteorological data of the Pantnagar region,India,from 2010 to 2019.A total of 15 combinations of inputs were used in developing the ET0 models.The model with the least number of inputs consisted of maximum and minimum air temperatures,whereas the model with the highest number of inputs consisted of maximum air temperature,minimum air temperature,mean relative humidity,number of sunshine hours,wind speed at 2mheight and extra-terrestrial radiation as inputs and with ET0 as the output for all the models.All the GEP models were developed for a single functional set and pre-defined genetic operator values,while the best structure in each ANN model was found based on the performance during the testing phase.It was found that ANN models were superior to GEP models for the estimation purpose.It was evident from the reduction in RMSE values ranging from 2%to 56%during training and testing phases in all the ANN models compared with GEP models.The ANN models showed an increase of about 0.96%to 9.72%of R2 value compared to the respective GEP models.The comparative study of these models with multiple linear regression(MLR)depicted that the ANN and GEP models were superior to MLR models.

关键词： Artificial Neural Networks Evolutionary algorithms gene expression programming Machine Learning Regression Analysis Reference evapotranspiration models

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Rules Mining-Based gene expression programming for the Multi-Skill Resource Constrained Project Scheduling Problem

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2815-2840页

作者： Min Hu Zhimin Chen Yuan Xia Liping Zhang Qiuhua Tang Key Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan430081China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and TechnologyWuhan430081China Precision Manufacturing Institute Wuhan University of Science and TechnologyWuhan430081China China Ship Development and Design Center Wuhan430060China

Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a real project and production environment.To solve MS-RCPSP,it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme.This paper proposes an improved gene expression programming(IGEP)approach to explore newly dispatching rules that can broadly solve MS-RCPSP.A new backward traversal decoding mechanism,and several neighborhood operators are applied in IGEP.The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process,and improves the algorithm’s performance.Several neighborhood operators improve the exploration of the potential search space.The experiment takes the intelligent multi-objective project scheduling environment(iMOPSE)benchmark dataset as the training set and testing set of IGEP.Ten newly dispatching rules are discovered and extracted by IGEP,and eight out of ten are superior to other typical dispatching rules.

关键词： Project scheduling multi-skill gene expression programming dispatching rules

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Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

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Chinese Journal of Chemical Engineering 2021年第4期34卷 431-445页

作者： Farzaneh Rezaei Saeed Jafari Abdolhossein Hemmati-Sarapardeh Amir H.Mohammadi Department of Petroleum Engineering Shahid Bahonar University of KermanKermanIran Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam Faculty of Environment and Chemical Engineering Duy Tan UniversityDa Nang 550000Vietnam Institut de Recherche en Genie Chimique et Petrolier(IRGCP) Paris CedexFrance Discipline of Chemical Engineering School of EngineeringUniversity of KwaZulu-NatalHoward College CampusKing George V AvenueDurban 4041South Africa

Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated.

关键词： Gas Viscosity High pressure high temperature Group method of data handling gene expression programming

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Hybrid gene expression programming-Based Sensor Data Correlation Mining

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China Communications 2017年第1期14卷 34-49页

作者： Lechan Yang Zhihao Qin Kun Wang Song Deng International Institute for Earth System Science Nanjing University Nanjing 210093 China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and TelecommunicationsNanjing 210023 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China

This paper deals with the reflectance estimation model issue to improve the estimation accuracy. We propose a model containing two core procedures: dimensionality reduction and model mining. First, the dimensionality reduction algorithm of hyperspectral data based on dependence degree(DRNDDD) is proposed to reduce the redundant hyperspectral band. DRND-DD solves the selection of suitable hyperspectral band via rough set theory. Furthermore, to improve the computation speed and accuracy of the model, based on DRND-DD, this paper proposes reflectance estimation model mining of leaf nitrogen concentration(LNC) for hyperspectral data by using hybrid gene expression programming(REMLNC-HGEP). Experimental results on three datasets demonstrate that the DRND-DD algorithm can obtain good results with a very short running time compared with principal component analysis(PCA), singular value decomposition(SVD), a dimensionality reduction algorithm based on the positive region(AR-PR) and a dimensionality reduction algorithm based on a discernable matrix(ARDM), and REMLNC-HGEP has low average time-consumption, high model mining success ratio and estimation accuracy. It was concluded that the REMLNC-HGEP performs better than the regression methods.

关键词： reflectance estimation dimensionality reduction gene expression programming model mining

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Prediction of Enhancement Effect of Nitroimidazoles on Irradiation by gene expression programming

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Chemical Research in Chinese Universities 2013年第3期29卷 519-525页

作者： LONG Wei ZHANG Xiao-dong WANG Hao SHEN Xiu SI Hong-zong Key Laboratory of Tianjin Molecular Nuclear Medicine Institute of Radiation Medicine Peking Union Medical College Chinese Academic of Medical Sciences Tianjin 300192 P. R. China Growing Base for State Key Laboratory of New Fibrous Materials and Modern Textile Institute for Computational Science and Engineering Qingdao University Qingdao 266071 P. R. China Department of Chemistry Lanzhou University Lanzhou 730000 P. R. China

A novel machine learning method, gene expression programming（GEP）, was employed to build quatitative structure-activity relationship（QSAR） models for predicting the enhancement effect of nitroimidazole compounds on irradiation. The models were based on descriptors which were calculated from the molecular structures. Four descriptors were selected from the pool of descriptors by best multiple linear regression（BMLR） method. After that, three regression methods, multiple linear regression（MLR）, support vector machine（SVM） and GEP, were used to build QSAR models. Compared to MLR and SVM, GEP produced a better model with the square of correlation coefficient（R2）, 0.9203 and 0.9014, and the root mean square error（RMSE）, 0.6187 and 0.6875, for training set and test set, respectively. The results show that the GEP model has better predictive ability and more reliable than the MLR and SVM models. This indicates that GEP is a promising method on relevant researches in radiation area.

关键词： Nitroimidazole Irradiation enhancement effect gene expression programming

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Security Risk Assessment of Cyber Physical Power System Based on Rough Set and gene expression programming

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IEEE/CAA Journal of Automatica Sinica 2015年第4期2卷 431-439页

作者： Song Deng Dong Yue Xiong Fu Aihua Zhou Research Institute of Advanced Technology Nanjing University of Post and Telecommunications School of Computer Science and Technology Nanjing University of Post and Telecommunications State Grid Smart Grid Research Institute

Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality.

关键词： gene expression programming function mining security risk assessment cyber physical power system

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Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

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Theoretical & Applied Mechanics Letters 2024年第2期14卷 90-97页

作者： Hanyu Zhou Haochen Li Yaomin Zhao HEDPS Center for Applied Physics and Technologyand College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

关键词： PDE discovery gene expression programming Deep Learning Knowledge embedding

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Rapid Prototype Development Approach for genetic programming

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Journal of Computer and Communications 2024年第2期12卷 67-79页

作者： Pei He Lei Zhang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Cyberspace Security Guangzhou University Guangzhou China

genetic programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

关键词： genetic programming Grammatical Evolution gene expression programming Regression Analysis Mathematical Modeling Rapid Prototype Development

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Multi-expression Based gene expression programming

Multi-Expression Based Gene Expression Programming

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2013年中国智能自动化学术会议

作者： Wei Deng Pei He Zhi Huang School of Computer and Communication Engineering Changsha University of Science and Technology Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology

Among the variants of GP,GEP stands out for its simplicity of encoding method and MEP catches our attention for its multi-expression capability.In this paper,a novel GP variant-MGEP(Multi-expression based gene expression programming) is proposed to combine these two approaches.The new method preserves the GEP structure,however unlike the traditional GEP,its genes,like those of MEP,can be disassembled into many expressions.Therefore in MGEP,the traditional GEP gene can contain multiple solutions for a problem.The experimental result shows the MGEP is more effective than the traditional GEP and MEP in solving problems.

关键词： genetic programming gene expression programming Multi expression programming

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An innovative model for predicting the displacement and rotation of column-tree moment connection under fire

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Frontiers of Structural and Civil Engineering 2021年第1期15卷 194-212页

作者： Mohammad Ali NAGHSH Aydin SHISHEGARAN Behnam KARAMI Timon RABCZUK Arshia SHISHEGARAN Hamed TAGHAVIZADEH Mehdi MORADI Department of Civil Engineering Isfahan University of TechnologyIsfahan 84156-83111Iran School of Civil Engineering School of Civil EngineeringIran University of Science and TechnologyTehran 13114-16846Iran Department of Civil Engineering International Institute of Earthquake Engineering and SeismologyTehran 19539-14453Iran Division of Computational Mechanics Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityVietnam Department of Civil Engineering Islamic Azad UniversityTehran 16511-53311Iran Department of Civil Engineering Isfahan UniversityIsfahan 81746-73441Iran

In this study,we carried out nonlinear finite element simulations to predict the performance of a columntree moment connection(CTMC)under fire and static loads.We also conducted a detailed parameter study based on five input variables,including the applied temperature,number of flange bolts,number of web bolts,length of the beam,and applied static loads.The first variable is changed among seven levels,whereas the other variables are changed among three levels.Employing the Taguchi method for variables 2-5 and their levels,9 samples were designed for the parameter study,where each sample was exposed to 7 different temperatures yielding 63 outputs.The related variables for each output are imported for the training and testing of different surrogate models.These surrogate models include a multiple linear regression(MLR),multiple Ln equation regression(MLnER),an adaptive network-based fuzzy inference system(ANFIS),and gene expression programming(GEP).44 samples were used for training randomly while the remaining samples were employed for testing.We show that GEP outperforms MLR,MLnER,and ANFIS.The results indicate that the rotation and deflection of the CTMC depend on the temperature.In addition,the fire resistance increases with a decrease in the beam length;thus,a shorter beam can increase the fire resistance of the building.The numbers of flanges and web bolts slightly affect the rotation and displacement of the CTMCs at temperatures of above 400℃.

关键词： column-tree moment connection Finite element model parametric study fire regression models gene expression programming

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