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Inferring empirical wall pressure spectral models with gene expression programming

JOURNAL OF SOUND AND VIBRATION 2021年 506卷 116162-116162页

作者： Dominique, Joachim Christophe, Julien Schram, Christophe Sandberg, Richard D. von Karman Inst Chaussee Waterloo 72 B-1640 Rhode St Genese Belgium Univ Melbourne Parkville Vic 3010 Australia

This paper presents a new data-driven approach for the establishment of empirical models describing turbulent boundary layer wall-pressure spectra. Unlike other models presented in literature, the new models are not derived by extending previously existing ones, but are directly built from a given dataset through symbolic regression using a machine learning algorithm known as gene expression programming. Two modifications of the GEP algorithm presented in literature are proposed in this work to cope with some issues that are specific to the modelling of wall pressure spectra: a new power terminal and a local optimization loop. The validity of the new approach is first demonstrated using as input a dataset synthesized following the Chase-Howe and Goody models. The method is then applied to experimental data for a flat plate boundary layer. The results indicate that the wall pressure model obtained with the proposed approach remains consistent with previous formulations for zero pressure gradient, while showing a better match with the data and suggesting new ways to predict the influence of moderate pressure gradient & nbsp;(c) 2021 Elsevier Ltd. All rights reserved.

关键词： gene expression programming Machine learning Turbulent boundary layer Wall pressure spectral models

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Application of ordinal logistic and gene expression programming methods to predict the collapse sensitivity classes of loess soils, a case study: Golestan Province, northeastern Iran

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2023年第2期195卷 305-305页

作者： Zalaghaie, Seddighe Heidari, Mojtaba Nikudel, Mohammad Reza Jalali, Seyed Hossein Seif, Asghar Bu Ali Sina Univ Dept Geol Mahdieh St Hamadan Iran Tarbiat Modares Univ Dept Engn Geol Al Ahmad St Tehran Iran Bu Ali Sina Univ Dept Stat Mahdieh St Hamadan Iran

The current study assesses the collapse sensitivity classes of loess soils using gene expression programming (GEP) and ordinal logistic regression (OLR). The crucial variable to forecast the possible development of loess caves in the Golestan Province (northeast of Iran) is the collapse sensitivity factor (Is). A database of 62 records, including the mechanical and physical characteristics of soils, was used. Oedometer tests were used to estimate the parameters of the collapse coefficient, the time needed for 90% settlement (T90%), and collapse sensitivity. The database includes 10 inputs (grain size, porosity, initial water content, precipitation, climatic data, liquid limit, calcium carbonate content, vegetation, and degree of soil saturation) and one output (collapse sensitivity classes). This is a complicated approach due to the complexity of setting up and performing such kinds of tests in the laboratory. The likelihood of soil classification ranks as severe, moderately severe, moderate, and small sensitivity was inspected using OLR and GEP. This study demonstrated that the OLR approach could effectively differentiate among more than 70% of distinct groups. Furthermore, experimental data reported from Semnan, Sarakhs, and Mashhad also attests to the accuracy of the OLR model. The sensitivity analysis indicated that silt fraction imparts the maximum effect on the collapse sensitivity classes. The trial-and-error method was used to determine the configurations of the GEP model prior to developing an ideal model. The performance of the GEP model to estimate the collapse sensitivity categories in a trustworthy, strong, and useful way is well documented by comparison between the results of the GEP and the experimental findings, which are affordable.

关键词： Collapse sensitivity Ordinal logistic regression model gene expression programming Predictive model

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Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: gene expression programming

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MATERIALS 2022年第7期15卷 2673页

作者： Asghar, Raheel Javed, Muhammad Faisal Alrowais, Raid Khalil, Alamgir Mohamed, Abdeliazim Mustafa Mohamed, Abdullah Vatin, Nikolai Ivanovich COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Jouf Univ Dept Civil Engn Al Jouf 72388 Saudi Arabia Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Dept Civil Engn Alkharj 16273 Saudi Arabia Bayan Coll Sci & Technol Bldg & Construct Technol Dept Khartoum 210 Sudan Future Univ Egypt Res Ctr New Cairo 11835 Egypt Peter Great St Petersburg Polytech Univ St Petersburg 195291 Russia

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R-2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R-2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R-2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

关键词： reinforced concrete columns lateral load carrying capacity bearing capacity flexural capacity shear capacity gene expression programming

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Application of gene expression programming to a-posteriori LES modeling of a Taylor Green Vortex

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JOURNAL OF COMPUTATIONAL PHYSICS 2021年 424卷 109859-109859页

作者： Reissmann, Maximilian Hasslberger, Josef Sandberg, Richard D. Klein, Markus Univ Melbourne Dept Mech Engn Parkville Vic 3010 Australia Univ Bundeswehr Muenchen Dept Aerosp Engn D-85577 Neubiberg Germany

gene expression programming (GEP), a branch of machine learning, is based on the idea to iteratively improve a population of candidate solutions using an evolutionary process built on the survival-of-the-fittest concept. The GEP approach was initially applied with encouraging results to the modeling of the unclosed tensors in the context of RANS (Reynolds Averaged Navier-Stokes) turbulence modeling. In a subsequent study it was demonstrated that the GEP concept can also be successfully used for modeling the unknown Sub-Grid Stress (SGS) tensor in the context of Large Eddy Simulations (LES). This was done in an a-priori analysis, where an existing Direct Numerical Simulation (DNS) database was explicitly filtered to evaluate the unknown stresses and to assess the performance of model candidates suggested by GEP. This paper presents the next logical step, i.e. the application of GEP to a-posteriori LES model development. Because a-posteriori analysis, using in-the-loop optimization, is considered the ultimate way to test SGS models, this can be considered an important milestone for the application of machine learning to LES based turbulence modeling. GEP is here used to train LES models for simulating a Taylor Green Vortex (TGV) and results are compared with existing standard models. It is shown that GEP finds a model that outperforms known models from literature as well as the no-model LES. Although the performance of this best model is maintained for resolutions and Reynolds numbers different from the training data, this is not automatically guaranteed for all other models suggested by the algorithm. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithm gene expression programming In-the-loop optimization A-posteriori LES Taylor Green Vortex

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Numerical sensitive data recognition based on hybrid gene expression programming for active distribution networks

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APPLIED SOFT COMPUTING 2020年 91卷 106213-106213页

作者： Deng, Song Xie, Xiangpeng Yuan, Changan Yang, Lechan Wu, Xindong Nanjing Univ Post & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Guangxi Acad Sci Nanning 530003 Guangxi Peoples R China Jinling Inst Technol Dept Soft Engn Nanjing 211169 Peoples R China Mininglamp Acad Sci Mininglamp Technol Beijing 102218 Peoples R China

Complex and flexible access mode, and frequent data interaction bring about large security risks to data transmission for active distribution networks. How to ensure data security is critical to the safe and stable operation of active distribution networks. Traditional methods, like access control, data encryption, and text filtering based on intelligent algorithms, are difficult to ensure the security of dynamically increased and high-dimensional numerical data transmission in active distribution networks. In this paper, we first propose a rough feature selection algorithm based on the average importance measurement (RFS-AIM) to simplify the complexity of data recognition. Then, we propose a sensitive data recognition function mining algorithm based on RFS-AIM and improved gene expression programming (SDR-IGEP) where population update operation is constructed by chromosome similarity based on the Jaccard coefficient. The operation avoids local convergence of the gene express programming by increasing individual diversity in the new population. Finally, we present a new incremental mining algorithm for a sensitive data recognition function based on global function fitting (ISDR-GFF) by using a grain granulation model for incremental datasets. The experimental results on IEEE benchmark datasets and real datasets show that the algorithms proposed in this paper outperform the state-of-the-art algorithms in terms of the average running time, precision, recall, F-1 index, accuracy, specificity and speedup on all experimental datasets. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Function mining Active distribution network Sensitive data recognition gene expression programming Rough set

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New spectral model for estimating leaf area index based on gene expression programming

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COMPUTERS & ELECTRICAL ENGINEERING 2020年 83卷 106604-106604页

作者： Yang, Lechan Deng, Song Zhang, Zi Jinling Inst Technol Dept Soft Engn Nanjing 211169 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China

The accurate evaluation of leaf area index (LAI) in paddy rice is of great importance in predicting the production and assessing food security. Traditional LAI estimation based on the PROSAIL model requires a large number of parameters. To solve this problem, this paper proposes a new estimation model between LAI and spectral reflectance for paddy rice canopies based on gene expression programming. And, we suggest a new spectral index for maximizing the response to LAI and weakening the sensitivity to chlorophyll concentration in the proposed LAI estimation model. Experimental results show that compared with existing indices, the proposed index (VI-R2) maximized the response to green LAI and was less sensitive to chlorophyll content variations, and in the validation with measured LAI values, VI-R2 shows the excellent fitting performance with coefficient of determination (r(2)) of 0.954. Meanwhile, based on a comparison of the estimated LAI values among existing indices using hyperspectral image, the results indirectly demonstrate that VI-R2 is usable for estimating LAI at the remote sensing pixel scale. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Spectral indices gene expression programming Hyperspectral data Leaf area index

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A gene expression programming Algorithm for Multi-Label Classification

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2011年第2-3期17卷 183-206页

作者： Avila, J. L. Gibaja, E. L. Zafra, A. Ventura, S. Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

This paper presents a gene expression programming algorithm for multi-label classification which encodes a discriminant function into each individual to show whether a pattern belongs to a given class or not. The algorithm also applies a niching technique to guarantee that the population includes functions for each existing class. The algorithm has been compared to other recently published algorithms. The results found on several datasets demonstrate the feasibility of this approach in the tackling of multi-label problems.

关键词： Multi-label classification discriminant functions gene expression programming machine learning

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Geopolymer Concrete Compressive Strength via Artificial Neural Network, Adaptive Neuro Fuzzy Interface System, and gene expression programming With K-Fold Cross Validation

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FRONTIERS IN MATERIALS 2021年 8卷

作者： Khan, Mohsin Ali Zafar, Adeel Farooq, Furqan Javed, Muhammad Faisal Alyousef, Rayed Alabduljabbar, Hisham Khan, M. Ijaz Natl Univ Sci & Technol NUST Mil Coll Engn MCE Islamabad Pakistan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Al Kharj Saudi Arabia Riphah Int Univ Dept Math & Stat Islamabad Pakistan

The ultrafine fly ash (FA) is a hazardous material collected from coal productions, which has been proficiently employed for the manufacturing of geopolymer concrete (GPC). In this study, the three artificial intelligence (AI) techniques, namely, artificial neural network (ANN), adaptive neuro-fuzzy interface (ANFIS), and gene expression programming (GEP) are used to establish a reliable and accurate model to estimate the compressive strength (f'(c)) of fly ash-based geopolymer concrete (FGPC). A database of 298 instances is developed from the peer-reviewed published work. The database consists of the ten most prominent explanatory variables and f'(c) of FGPC as a response parameter. The statistical error checks and criteria suggested in the literature are considered for the verification of the predictive strength of the models. The statistical measures considered in this study are MAE, RSE, RMSE, RRMSE, R, and performance index (rho). These checks verify that the ANFIS predictive model gives an outstanding performance followed by GEP and ANN predictive models. In the validation stage, the coefficient of correlation (R) for ANFIS, GEP, and ANN model is 0.9783, 0.9643, and 0.9314, respectively. All three models also fulfill the external verification criterion suggested in the literature. generally, the GEP predictive model is ideal as it delivers a simplistic and easy mathematical equation for future use. The k-fold cross-validation (CV) of the GEP model is also conducted, which verifies the robustness of the GEP predictive model. Furthermore, the parametric study is carried via proposed GEP expression. This confirms that the GEP model accurately covers the influence of all the explanatory variables used for the prediction of f'(c) of FGPC. Thus, the proposed GEP equation can be used in the preliminary design of FGPC.

关键词： geopolymer concrete artificial neural network adaptive neuro-fuzzy interface system gene expression programming k-fold cross-validation

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Indirect estimation of resilient modulus (Mr) of subgrade soil: gene expression programming vs multi expression programming

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STRUCTURES 2024年 66卷

作者： Khawaja, Laiba Javed, Muhammad Faisal Asif, Usama Alkhattabi, Loai Ahmed, Bilal Alabduljabbar, Hisham COMSATS Univ Islamabad Abbottabad Campus Abbottabad Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Western Caspian Univ Baku Azerbaijan Nazarbayev Univ Sch Engn & Digital Sci Dept Civil & Environm Engn Nur Sultan 010000 Kazakhstan Univ Jeddah Coll Engn Dept Civil & Environm Engn Jeddah 23890 Saudi Arabia Silesian Tech Univ PL-44100 Gliwice Poland Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia

Accurate prediction of resilient modulus (MR) in compacted subgrade soil is crucial for planning secure and environmentally friendly flexible pavement systems. This research assembled a dataset of 2813 data points from twelve compacted soils. The dry density, confining stress, deviator stress, number of freeze-thaw cycles, and moisture content were among the important variables considered for determining the MR. Subsequently, this study employs ensemble machine learning methodologies, specifically gene expression programming (GEP) and multi-expression programming (MEP), to investigate the subject further. The precision and anticipatory proficiency of both the GEP and MEP models are assessed through statistical evaluations, encompassing crucial metrics (R, RMSE, MAE, RSE, RRMSE, and rho). The GEP and MEP models align well with validation criteria, underscoring their robustness in predicting novel data and showcasing their broad applicability. The GEP model consistently outperformed the MEP model, with higher coefficient of regression (R2) values in both training (0.992 vs. 0.983) and testing (0.981 vs. 0.972) phases, demonstrating its superior predictive accuracy and robustness. In summary, the GEP model consistently outperforms the MEP model in accuracy and prediction, making it the preferred choice for subgrade soil MR prediction. Sensitivity analysis was done, which ranked the parameters by their influence: dry density (26.6 %), confining stress (22.7 %), weighted plasticity index (15.3 %), moisture content (13.5 %), deviator stress (12.5 %), and freeze and thaw cycles (9.4 %). This research aims to enhance the utilization of GEP and MEP in civil engineering for more accurate and efficient MR prediction, ultimately reducing time and costs.

关键词： Resilient modulus Subgrade soil Machine learning gene expression programming Multi -expression programming

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Intrusion detection model using gene expression programming to optimize parameters of convolutional neural network for energy internet

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APPLIED SOFT COMPUTING 2023年 134卷

作者： Deng, Song Yuan, Xinya Li, Qianliang Zhang, Jie Sun, Mengfei Fu, Xiong Yang, Lechan Nanjing Univ Post & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Nanjing Univ Post & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Jinling Inst Technol Dept Soft Engn Nanjing Peoples R China

The open, interconnected, and shared operational characteristics of the energy Internet introduce more sophisticated cybersecurity attacks. How to accurately detect these cyber attacks is crucial for energy Internet security protection. Existing machine learning-based intrusion detection algorithms cannot cope with the continuous increase of network traffic and features in the energy Internet. And convolutional neural networks (CNN) can be a good solution for the descending and optimal selection of high-dimensional intrusion features. Unfortunately, traditional convolutional neural networks have complex structures with many parameters and are prone to fall into local optimality. To fill the gap of CNN, in this paper, we use a gene expression programming (GEP) to optimize the parameters of CNN and propose an intrusion detection algorithm based on GEP-CNN (GCNN-IDS). Our key idea is to avoid the convolutional neural network from falling into local optimum by designing a new code on GEP and fitness function to optimize the parameters of the CNN using the global search capability of GEP. The experimental results on two benchmark datasets and a real dataset substantiate that the detection accuracy of the optimized CNN-based intrusion detection algorithm (ICNN-IDS) reaches up to 0.9143 under different parameter combinations;meanwhile, compared with other algorithms, the detection accuracy, precision, recall, F1 and false detection rate of the intrusion detection model proposed in this paper reach 0.9897, 0.99, 0.98, 0.97 and 0.0126, respectively.(c) 2022 Elsevier B.V. All rights reserved.

关键词： gene expression programming Convolutional neural network Parameter optimization Intrusion detection

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