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An improved gene expression programming approach for symbolic regression problems

NEUROCOMPUTING 2014年 137卷 293-301页

作者： Peng, YuZhong Yuan, ChangAn Qin, Xiao Huang, JiangTao Shi, YaBing Guangxi Teachers Educ Univ Key Lab Sci Comp & Intelligent Informat Proc Univ Nanning 530001 Peoples R China

gene expression programming (GEP) is a powerful evolutionary method for knowledge discovery and model learning. Based on the basic GEP algorithm, this paper proposes an improved algorithm named S_GEP, which is especially suitable for dealing with symbolic regression problems. The major advantages for this S_GEP method include: (I) A new method for evaluating individual without expression tree;(2) a corresponding expression tree construction schema for the new evaluating individual method if required by some special complex problems;and (3) a new approach for manipulating numeric constants so as to improve the convergence. A thorough comparative study between our proposed S_GEP method with the primitive GEP, as well as other methods are included in this paper. The comparative results show that the proposed S_GEP method can significantly improve the GEP performance. Several well-studied benchmark test cases and real-world test cases demonstrate the efficiency and capability of our proposed S_GEP for symbolic regression problems. (C) 2014 Elsevier B.V. All rights reserved.

关键词： genetic computing gene expression programming Evolutionary algorithm Symbolic regression Data modeling

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Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

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BIORESOURCES AND BIOPROCESSING 2020年第1期7卷 1-22页

作者： Abdulsalam, Jibril Lawal, Abiodun Ismail Setsepu, Ramadimetja Lizah Onifade, Moshood Bada, Samson Univ Witwatersrand Fac Engn & Built Environm Sch Chem & Met Engn DSI NRF Clean Coal Technol Res Grp ZA-2050 Johannesburg South Africa Inha Univ Dept Energy Resources Engn Incheon South Korea Fed Univ Technol Akure Dept Min Engn Akure Nigeria Univ Namibia Dept Min & Met Engn Windhoek Namibia

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 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 (R-2), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10 to 10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R-2 = 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.

关键词： Artificial neural network Biomass gene expression programming Higher heating value Hydrochars Hydrothermal carbonization

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Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations

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ENGINEERING COMPUTATIONS 2018年第5期35卷 2078-2106页

作者： Sadrossadat, Ehsan Ghorbani, Behnam Oskooei, Rahimzadeh Kaboutari, Mahdi Islamic Azad Univ Mashhad Branch Young Researchers & Elite Club Mashhad Iran Ferdowsi Univ Mashhad Fac Engn Civil Engn Dept Mashhad Iran Inst Higher Educ Eqbal Lahoori Dept Civil Engn Mashhad Iran

Purpose This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (q(ult)) of rock foundations, which is a considerable civil and geotechnical engineering problem. Design/methodology/approach The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of q(ult) is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study. Findings A new and explicit formula generated by GEP is proposed for the estimation of the q(ult) of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems. Originality/value The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

关键词： Prediction Adaptive neuro-fuzzy inference system gene expression programming Rock mass properties Shallow foundation Ultimate bearing capacity

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Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 3341-3350页

作者： Bayat, Parichehr Monjezi, Masoud Mehrdanesh, Amirhossein Khandelwal, Manoj Tarbiat Modares Univ Dept Min Fac Engn Tehran 14115143 Iran Federat Univ Australia Sch Engn Informat Technol & Phys Sci Ballarat Vic 3350 Australia

Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations.

关键词： Ground vibration Meta-heuristic optimization gene expression programming Grasshopper optimization algorithm

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Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2016年第6期13卷 1453-1464页

作者： Faradonbeh, R. Shirani Armaghani, D. Jahed Majid, M. Z. Abd Tahir, M. Md Murlidhar, B. Ramesh Monjezi, M. Wong, H. M. Tarbiat Modares Univ Fac Engn Dept Min Tehran 14115143 Iran Univ Teknol Malaysia Fac Civil Engn Dept Geotech & Transportat Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Civil Engn ISIIC Construct Res Ctr Skudai 81310 Johor Malaysia Islamic Azad Univ South Tehran Branch Fac Engn Tehran Iran Univ Malaya Dept Mech Engn Kuala Lumpur Malaysia

Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

关键词： Blasting Ground vibration gene expression programming Nonlinear multiple regression

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Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques

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NEURAL COMPUTING & APPLICATIONS 2019年第10期31卷 5799-5817页

作者： Gholami, Azadeh Bonakdari, Hossein Zeynoddin, Mohammad Ebtehaj, Isa Gharabaghi, Bahram Khodashenas, Saeed Reza Razi Univ Dept Civil Engn Kermanshah Iran Univ Guelph Sch Engn Guelph ON NIG 2W1 Canada Ferdowsi Univ Mashhad Water Engn Dept Mashhad Razavi Khorasan Iran

The geometric dimensions and bank profile shape of channels with boundaries containing particles on the verge of motion (threshold channels) are significant factors in channel design. In this study, extensive experimental work was done at different flow velocities to propose a reliable method capable of estimating stable channel bank profile. The proposed method is based on gene expression programming (GEP). Laboratorial datasets obtained from Mikhailova et al. (Hydro Tech Constr 14:714-722, 1980), Ikeda (J Hydraul Div ASCE 107:389-406 1981), Diplas (J Hydraul Eng ASCE 116:707-728, 1990) and Hassanzadeh et al. (J Civil Environ Eng 43(4):59-68, 2014) were used to train, test, validate and examine the GEP model in various geometric and hydraulic conditions. The obtained results demonstrate that the proposed model can estimate bank profile characteristics with great accuracy (determination coefficient of 0.973 and mean absolute relative error of 0.147). Moreover, for practical calculations of channel dimensions, the model provides a specific mathematical relationship to solve problems with different discharge rates (Q) and particles with various median grain sizes (D-50). The proposed model's performance is compared with 8 relationships suggested previously by researchers (based on empirical and theoretical analyses) and a relationship obtained using a nonlinear regression model with different experimental data. The polynomial VDM and two exponential functions, i.e. IKM and DIM, are introduced as the superior existing models. According to the present study results, the proposed GEP model can predict the bank profile shape trend well and similar to the experimental datasets. Sensitivity analysis was also applied to assess the impact of each input variable (x, Q and D-50) on the presented relationship. According to the current study, the GEP model provides a suitable equation for predicting the bank profile shape of stable channels.

关键词： gene expression programming Bank profile shape Threshold channel Sensitivity analysis

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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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Predicting buckling loads of perforated rectangular isotropic panels using gene expression programming and Artificial Neural Network

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2023年第8期52卷 5174-5194页

作者： Al Qablan, Husam Al-Qablan, Tamara Hashemite Univ Fac Engn Civil Engn Dept Zarqa Jordan Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Amman Jordan POB 150459 Zarqa 13135 Jordan

This study aims to develop semi-empirical formulas for foretelling the buckling loads of clamped and simply supported rectangular isotropic panels with central circular perforation exposed to different ratios of biaxial load conditions. The empirical formulas were developed and evaluated using gene expression programming (GEP), Artificial Neural Network (ANN), and Finite Element Method (FEM). A total of 714 data set, generated using the FEM, is used to establish and validate the empirical formulas. This study investigates the effect of perforation sizes, plate aspect ratios, and biaxial load ratios on the buckling strength of perforated panels. The proposed formulas will allow a quick and easy estimation of buckling loads for perforated rectangular panels with acceptable accuracy without the need for sophisticated calculations. The results of the empirical formulas were comparable reasonably well with the results of the finite element analysis (FE) and available literature findings.

关键词： Buckling cutout FE analysis semi-empirical formula gene expression programming Artificial Neural Network

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A novel method for real parameter optimization based on gene expression programming

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APPLIED SOFT COMPUTING 2009年第2期9卷 725-737页

作者： Xu, Kaikuo Liu, Yintian Tang, Rong Zuo, Jie Zhu, Jun Tang, Changjie Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China Chengdu Univ Informat Technol Chengdu 610225 Peoples R China Natl Ctr Birth Defects Monitoring Chengdu 610041 Peoples R China

gene expression programming (GEP) is a new technique of evolutionary algorithm that implements genome/phoneme representation in computing programs. Due to its power in global search, it is widely applied in symbolic regression. However, little work has been done to apply it to real parameter optimization yet. This paper proposes a real parameter optimization method named Uniform-Constants based GEP (UC-GEP). In UC-GEP, the constant domain directly participates in the evolution. Our research conducted extensive experiments over nine benchmark functions from the IEEE Congress on Evolutionary Computation 2005 and compared the results to three other algorithms namely Meta-Constants based GEP (MC-GEP), Meta-Uniform-Constants based GEP (MUC-GEP), and the Floating Point genetic Algorithm (FP-GA). For simplicity, all GEP methods adopt a one-tier index gene structure. The results demonstrate the optimal performance of our UC-GEP in solving multimodal problems and show that at least one GEP method outperforms FP-GA on all test functions with higher computational complexity. (C) 2008 Elsevier B. V. All rights reserved.

关键词： gene expression programming Real parameter optimization expression tree

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generalizability of gene expression programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran

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JOURNAL OF HYDROLOGY 2014年 508卷 1-11页

作者： Shiri, Jalal Sadraddini, Ali Ashraf Nazemi, Amir Hossein Kisi, Ozgur Landeras, Gorka Fard, Ahmad Fakheri Marti, Pau Univ Tabriz Fac Agr Water Engn Dept Tabriz Iran Canik Basari Univ Fac Engn & Architecture Dept Civil Engn Samsun Turkey Basque Country Res Inst Agr Dev NEIKER Alava Basque Country Spain Univ Politecn Valencia Dept Ingn Rural & Agroalimentaria Valencia 46022 Spain

When dealing with climatic variables, the performance assessment of many Artificial Intelligence (AI) and/or data mining applications is based on a single data set assignment of the training and test sets. Further, it is very usual that this assignment is defined according to a local and temporary criterion, i.e. the models are trained and tested using data of the same station. Based on this procedure, the performance of the models outside the training location cannot be inferred. The present work evaluates the performance of gene expression programming (GEP) based models for estimating reference evapotranspiration (ET0) according to temporal and spatial criteria and data set scanning procedures in coastal environments of Iran. The accuracy differences between the local and the external performance depend on the specific climatic trends of the test stations, as well as on the input combination used to feed the models. When relying on a suitable input selection, externally trained models might be a valid alternative to locally trained ones, which would be a crucial advantage in places where only limited climatic variables are available. K-fold testing is a good choice to prevent partially valid conclusions derived from model assessments based on a simple data set assignment. Further, calibration of the GEP model may not be needed, if enough climatic data are available at other stations for external model application. The performance of the GEP model fluctuates chronologically and spatially. A suitable assessment of the model should consider a complete local and/or external scan of the data set used. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Evapotranspiration Exogenous data gene expression programming Local training

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