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QSAR models for the dermal penetration of polycyclic aromatic hydrocarbons based on gene expression programming

QSAR & COMBINATORIAL SCIENCE 2008年第7期27卷 913-921页

作者： Wang, Tao Si, Hongzong Chen, Pingping Zhang, Kejun Yao, Xiaojun Hosp Qingdao Univ Qingdao 266071 Shandong Peoples R China Qingdao Univ Inst Computat Sci & Engn Lab New Fibrous Mat & Modern Text Growing BaseState Key Lab Qingdao 266071 Shandong Peoples R China Southeast Univ Sch Engn & Comp Sci Nanjing 211189 Jiangsu Peoples R China Zhejiang Univ Dept Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Lanzhou Univ Dept Chem Lanzhou 730000 Gansu Peoples R China

gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure activity relationship model for the prediction of the Percent of Applied Dose Dermally Absorbed (PADA) over 24 h for polycyclic aromatic hydrocarbons. This model is based on descriptors which are calculated from the molecular structure. Three descriptors are selected from the descriptors pool by Heuristic Method (HM) to build a multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.92 and 4.70 for the training set, 0.91 and 7.65 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

关键词： derma Heuristic method gene expression programming polycyclic aromatic hydrocarbons quantitative structure-activity relationship

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Semi-autogenous mill power model development using gene expression programming

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POWDER TECHNOLOGY 2017年 308卷 61-69页

作者： Hoseinian, Fatemeh Sadat Faradonbeh, Roohollah Shirani Abdollahzadeh, Aliakbar Rezai, Bahram Soltani-Mohammadi, Saeed Amirkabir Univ Technol Dept Min & Met Engn Tehran Iran Islamic Azad Univ South Tehran Branch Young Researchers & Elite Club Tehran Iran Univ Kashan Dept Min Engn Kashan Iran

Predicting the performance of the semi-autogenous (SAG) mill is necessary for the best circuit design which is possible by suitable modeling and simulation. Numerous models of the SAG mill are studied in the literature, but the majority of them do not evaluate the predicted model for full-scale mill performance. Mill powers of the semi-autogenous mill have an effective influence on the mill performance. In this regard, a new predictive model based on gene expression programming (GEP) was developed to predict the mill power of the SAG mill. To achieve this purpose, a total number of 186 full-scale SAG mill works were investigated and the most effective parameters on SAG mill power, i.e., feed moisture, mass flowrate, mill load cell weight, SAG mill solid percentage, inlet and outlet water to the SAG mill and work index Were measured and utilized to develop the GEP model. In order to determine the relationship between the input and output parameters, the GEP model was developed and the results were compared with non-linear multiple regression (NLMR) method. The results show the capability of the GEP model in predicting the mill power. It shows that the mill power is more sensitive to mass flowrate and work index than other input parameters. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Semi-autogenous mill Mill power Prediction gene expression programming Non-linear multiple regression

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Distributed correlation model mining from remote sensing big data based on gene expression programming

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2018年第5期11卷 1000-1011页

作者： Yang, Lechan Qin, Zhihao Nanjing Univ Int Inst Earth Syst Sci Nanjing 210093 Jiangsu Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China

With the continuous development of remote sensing techniques, an enormous amount of remote sensing data is collected by observing the earth. The method by which researchers mine the relevant knowledge accurately and efficiently from remote sensing big data remains a topic of interest. The existing model mining algorithms usually rely on a priori knowledge, and cannot meet the actual needs of remote sensing data mining. This paper proposes distributed correlation model mining from remote sensing big data based on gene expression programming (DCMM-GEP) combined with cloud computing to find a better model for remote sensing big data using an abnormal value recognition algorithm based on residual sum of residual (AVR-RSR) and a global model generation algorithm for remote sensing big data based on linear least squares (GMGRS-LLS). Comparative experiments show that DCMM-GEP outperforms both distributed correlation model mining based on genetic programming and genetic algorithms, showing better average time-consumption, R-square values and prediction accuracy. The comparative results also show that with an increasing number of computing nodes, DCMM-GEP has a good speed-up ratio and scale-up ratio.

关键词： Correlationmodel mining Remote sensing Big data gene expression programming

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Prediction of the hydrodynamic performance and cavitation volume of the marine propeller using gene expression programming

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SHIPS AND OFFSHORE STRUCTURES 2019年第7期14卷 723-736页

作者： Mahmoodi, Kumars Ghassemi, Hassan Nowruzi, Hashem Shora, Mohammad Mahdi Amirkabir Univ Technol Dept Maritime Engn Tehran Iran

In the present study, hydrodynamic performance and cavitation volume of the marine propeller under different geometrical and physical conditions are evaluated by using three models which are proposed through gene expression programming (GEP). To this accomplishment, computational fluid dynamics (CFD) data of propeller thrust, torque and cavitation volume under different pitch ratio (P-r), rake angle (RA) and skew angle (SA), advance velocity ratio (J) and cavitation number (s) are considered as inputs-outputs of GEP models. Then, mathematical equations are obtained for the thrust, torque and cavitation volume in terms of the geometrical and physical prescribed parameters. In order to evaluate the performance and accuracy of the GEP models, four statistical measures including root mean square error (RMSE), mean absolute error (MAE), scatter index (SI) and coefficient of determination (R-2) are used. The final models are found to be of reasonable accuracy between observed and GEP-predicted values.

关键词： B-Series propeller hydrodynamic performance cavitation volume gene expression programming

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Pulsed electrodeposition of Ni-W coatings predicts microhardness via gene expression programming

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JOURNAL OF APPLIED ELECTROCHEMISTRY 2023年第7期53卷 1433-1447页

作者： Shojaei, Z. Khayati, G. R. Shahid Bahonar Univ Kerman Dept Mat Sci & Engn Kerman Iran

In the present work, a robust prediction model is presented for predicting Ni-W coating microhardness using powerful soft computing techniques, i.e., gene expression programming (GEP). We considered the W content in coating (wt.%), pH, time (min) of coating bath, frequency (kHz) and current density (mA cm(-2)) of pulse current electrodeposition as input variables and the microhardness of Ni-W coatings as output variables. To achieve this, we had three main steps: (i) 63 experiments were collected to construct models;(ii) Creating training and testing phases based on 50 and 13 data and (iii) A new model is built and compared using correlation coefficients (R-2), root relative square errors (RRSE), and relative standard errors (RSE). Using the results, GEP-6 was found to be the appropriate model for predicting the microhardness of Ni-W coatings with R-2 = 0.9926, RSE = 0.0077, and RRSE = 0.0880. Additionally, sensitivity analysis results indicated that pulse electrodeposition's frequency, current density, and time of coating bath were the most effective parameters in determining microhardness.{GRAPHIS}

关键词： Ni-W coatings Pulse electrodeposition Microhardness gene expression programming

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Performing multi-target regression via gene expression programming- based ensemble models

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NEUROCOMPUTING 2021年 432卷 275-287页

作者： Moyano, Jose M. Reyes, Oscar Fardoun, Habib M. Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal Cordoba Spain King Abdulaziz Univ Fac Comp & Informat Technol North Jeddah Saudi Arabia Maimonides Biomed Res Inst Cordoba Knowledge Discovery & Intelligent Syst Biomed Lab Cordoba Spain

Multi-Target Regression problem comprises the prediction of multiple continuous variables given a common set of input features, unlike traditional regression tasks, where just one output target is available. There are two major challenges when addressing this problem, namely the exploration of the intertarget dependencies and the modeling of complex input-output relationships. This work proposes a Symbolic Regression method following the basis of gene expression programming paradigm to solve the multi-target regression problem, and called GEPMTR. It evolves a population of individuals, where each one represents a complete solution to the problem by using a multi-genic chromosome, and encodes a mathematical function for each target variable involving the input attributes. The proposed model can estimate the inter-target dependencies by applying some genetic operators. Furthermore, three ensemble-based methods are developed to better exploit the inter-target and input-output relationships. The effectiveness of the proposals is analyzed through an extensive experimental study on 18 datasets. The codification schema and the process followed to ensure a diverse population in GEPMTR lead to obtain an effective proposal to solve the MTR problem. Furthermore, the EGEPMTR-B ensemble method obtained the best performance across all proposed models, being the best in 8 out of 11 cases, demonstrating that more sophisticated mechanisms were not needed for ensuring that GEPMTR method would properly model the existing inter-target dependencies. Finally, the experimental results also showed that the proposed approach attains competitive results compared to state-of-the-art, showing the possibilities that can bring this research line for effectively solving the MTR problem. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-target regression gene expression programming Symbolic regression Ensemble-based model

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Multiple linear regression and gene expression programming to predict fracture density from conventional well logs of basement metamorphic rocks

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2024年第7期14卷 1899-1921页

作者： Hasan, Muhammad Luqman Toth, Tivadar M. Univ Szeged Dept Mineral Geochem & Petrol Egyet Utca 2 H-6722 Szeged Hungary Univ Teknol Petronas Dept Petr Engn Bandar Seri Iskandar 32610 Perak Darul Rid Malaysia

Fracture identification and evaluation requires data from various resources, such as image logs, core samples, seismic data, and conventional well logs for a meaningful interpretation. However, several wells have some missing data;for instance, expensive cost run for image logs, cost concern for core samples, and occasionally unsuccessful core retrieving process. Thus, a majority of the current research is focused on predicting fracture based on conventional well log data. Interpreting fractures information is very important especially to develop reservoir model and to plan for drilling and field development. This study employed statistical methods such as multiple linear regression (MLR), principal component analysis (PCA), and gene expression programming (GEP) to predict fracture density from conventional well log data. This study explored three wells from a basement metamorphic rock with ten conventional logs of gamma rays, thorium, potassium, uranium, deep resistivity, flushed zone resistivity, bulk density, neutron porosity, sonic porosity, and photoelectric effect. Four different methods were used to predict the fracture density, and the results show that predicting fracture density is possible using MLR, PCA, and GEP. However, GEP predicted the best fracture density with R-2 > 0.86 for all investigated wells, although it had limited use in predicting fracture density. All methods used highlighted that flushed zone resistivity and uranium content are the two most significant well log parameters to predict fracture density. GEP was efficient for use in metamorphic rocks as it works well for conventional well log data as the data is nonlinear, and GEP uses nonlinear algorithms.

关键词： Fractured reservoir Fracture density Multiple linear regression Well-logging gene expression programming Metamorphic basement

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Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming

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JOURNAL OF HAZARDOUS MATERIALS 2020年 384卷 121322-121322页

作者： Iqbal, Muhammad Farjad Liu, Qing-feng Azim, Iftikhar Zhu, Xingyi Yang, Jian Javed, Muhammad Faisal Rauf, Momina Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai Peoples R China Collaborat Innovat Ctr Adv Ship & Deep Sea Explor Shanghai Peoples R China Tongli Univ Key Lab Rd & Traff Engn Minist Educ Suzhou Peoples R China Univ Birmingham Sch Civil Engn Birmingham W Midlands England COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad Khyber Pakhtunk Pakistan Natl Univ Sci & Technol NUST Sch Civil & Environm Engn Islamabad Pakistan

Waste foundry sand (WFS) is a major pollutant generated from metal casting foundries and is classified as a hazardous material due to the presence of organic and inorganic pollutants which can cause adverse environmental impact. In order to promote the re-utilization of WFS, gene expression programming (GEP) has been employed in this study to develop empirical models for prediction of mechanical properties of concrete made with WFS (CMWFS). An extensive and reliable database of mechanical properties of CMWFS is established through a comprehensive literature review. The database comprises of 234 compressive strength, 163 split tensile strength and 85 elastic modulus results. The four most influential parameters i.e. water-to-cement ratio, WFS percentage, WFS-to-cement content ratio and fineness modulus of WFS are considered as the input parameters for modelling. The mechanical properties can be estimated by the application of proposed simplified mathematical expressions. The performance of the models is assessed by conducting parametric analysis, applying statistical checks and comparing with regression models. The results reflected that the proposed models are accurate and possess a high generalization and prediction capability. The findings of this study can enhance the re-usage of WFS for development of green concrete leading to environmental protection and monetary benefits.

关键词： Waste foundry sand gene expression programming Compressive strength Split tensile strength Green concrete

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Experimental, Modeling, and Optimization Investigation on Mechanical Properties and the Crashworthiness of Thin-Walled Frusta of Silica/Epoxy Nano-composites: Fuzzy Neural Network, Particle Swarm Optimization/Multivariate Nonlinear Regression, and gene expression programming

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JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE 2022年第4期31卷 3030-3040页

作者： Dadrasi, A. Shariati, M. Farzi, Gh A. Fooladpanjeh, S. Islamic Azad Univ Dept Mech Engn Shahrood Branch Shahrood Iran Ferdowsi Univ Mashhad Fac Engn Dept Mech Engn Mashhad Razavi Khorasan Iran Hakim Sabzevari Univ Dept Mat & Polymer Engn Sabzevar *** Iran

In this work, an experimental study on the quasi-static collapse of thin-walled frusta of silica/epoxy nano-composites was conducted. The effect of nano-silica content and the particle size hybrid on the energy absorption capability of thin-walled frusta, the impact strength, Young's modulus, and the yield strength was investigated. For this purpose, three various sizes of the silica particle with the mean diameter of 17, 25, and 65 nm were used. The results showed that by adding the silica nano-particles up to 6 wt.%, the impact strength and Young's modulus increased, the yield strength remained constant, and the crashworthy capability of structures decreased. Also, two approaches including Fuzzy Neural Network, the hybrid of Particle Swarm Optimization (PSO), and Multivariate Nonlinear Regression (MNLR) were employed to determine the effect of the mentioned parameters. In comparison with the mentioned models and the experimental results, PSO/MNLR approach showed a better prediction for the parameters. Different parameters were optimized by gene expression programming. Some fracture surfaces were studied by scanning the electron microscopy.

关键词： crashworthy capability frusta gene expression programming particle swarm optimization silica nano-particles size hybrid

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Uniaxial compressive strength prediction through a new technique based on gene expression programming

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NEURAL COMPUTING & APPLICATIONS 2018年第11期30卷 3523-3532页

作者： Armaghani, Danial Jahed Safari, Vali Fahimifar, Ahmad Amin, Mohd For Mohd Monjezi, Masoud Mohammadi, Mir Ahmad Amirkabir Univ Technol Dept Civil & Environm Engn Tehran 15914 Iran Tarbiat Modares Univ Fac Engn Tehran 14115143 Iran Univ Teknol Malaysia Dept Geotech & Transportat Fac Civil Engn Skudai 81310 Johor Malaysia Tarbiat Modares Univ Dept Min Tehran 14115143 Iran

Proper estimation of rock strength is a critical task for evaluation and design of some geotechnical applications such as tunneling and excavation. Uniaxial compressive strength (UCS) test can be measured directly in the laboratory;nevertheless, the direct UCS determination is time-consuming and expensive. In this study, feasibility of gene expression programming (GEP) model in indirect determination of UCS values of sandstone rock samples is examined. In this regard, several laboratory tests including Brazilian test, density test, slake durability test and UCS test were conducted on 47 samples of sandstone which were collected from the Dengkil, Malaysia. Considering multiple inputs, several GEP models were constructed to estimate UCS of the rock and finally, the best GEP model was selected. In order to indicate capability of the proposed GEP model, linear multiple regression (LMR) was also performed. It was found that the GEP model is superior to LMR one in terms of applied performance indices. Based on coefficient of determination (R-2) of testing datasets, by proposing GEP model, it can be improved from 0.930 (which was obtained by LMR model) to 0.965. As a result, it is concluded that the proposed models in this study, could be utilized to estimate UCS of similar rock type in practice.

关键词： Uniaxial compressive strength Sandstone gene expression programming Multiple regression

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