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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Yuzhong Zhang, Hao Zhang, Ziqiao Lin, Yanmei Zhou, Shuigeng Qiao, Shaojie Nanning Normal University Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China Nanning Normal University Undergraduate College Nanning China Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9798350337488

High-performance molecular property prediction is one of the essential problems in many research fields. Although Deep Learning models (DLMs) have the strong potential to predict molecular property, the search space of their neural structure and hyperparameters is too huge to enumerate all possibilities, which implies that one will be difficult to get a optimal configuration solution, or even build a DLM with poor performance. To tackle the aforementioned problem, we first abstractly map various types of supervised DLMs for chemical molecular property prediction under a unified molecular DLM conceptualization. Then, we develop a self-learning molecular DLMs construction and optimization framework, called GEP-DL4Mol, based on the unified supervised molecular DLM conceptualization and gene expression programming. Experiments on eight benchmark datasets including 19 property prediction tasks were conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method outperforms other methods. © 2023 IEEE.

关键词： deep learning deep neural network optimization gene expression programming hyper-parameter optimization molecular modeling Molecular property prediction

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Yarn Strength Modelling Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and gene expression programming (GEP)

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JOURNAL OF ENGINEERED FIBERS AND FABRICS 2013年第4期8卷 6-18页

作者： Fallahpour, A. R. Moghassem, A. R. Univ Malaya Fac Engn Dept Mech Engn Kuala Lumpur 50603 Malaysia Islamic Azad Univ Dept Text Engn Qaemshahr Branch Qaemshahr Iran

This study compares capabilities of two different modelling methodologies for predicting breaking strength of rotor spun yarns. Forty eight yarn samples were produced considering variations in three drawing frame parameters namely break draft, delivery speed, and distance between back and middle rolls. Several topologies with different architectures were trained to get the best adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) models. Prediction performance of the GEP model was compared with that of ANFIS using root mean square error (RMSE) and correlation coefficient (R-2-Value) parameters on the test data. Results show that, the GEP model has a significant priority over the ANFIS model in term of prediction accuracy. The correlation coefficient (R-2-value) and root mean square error for the GEP model were 0.87 and 0.35 respectively, while these parameters were 0.48 and 0.53 for the ANFIS model. Also, a mathematical formula was developed with high degree of accuracy using GEP algorithm to predict the breaking strength of the yarns. This advantage is not accessible in the ANFIS model.

关键词： Breaking strength of rotor spun yarns Drawing frame Adaptive neuro-fuzzy inference system gene expression programming Mathematical formula

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Application of artificial intelligence methods to model the effect of grass curing level on spread rate of fires

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ENVIRONMENTAL MODELLING & SOFTWARE 2024年 173卷

作者： Khanmohammadi, Sadegh Cruz, Miguel G. Golafshani, Emadaldin Mohammadi Bai, Yu Arashpour, Mehrdad Monash Univ Dept Civil Engn Melbourne Australia CSIRO GPO Box 1700 Canberra ACT 2601 Australia

Artificial intelligence (AI) enables new approaches to fire behaviour models of operational relevance, including prescribed burns. This is particularly important in modelling of processes that are poorly understood, such as live fuel's effect on fire propagation. The objective of this study was to apply AI algorithms to quantify the effect of the proportion of dead fuels in a senescing grassland, the curing level, on reducing the rate of fire spread relative to the fully cured condition. We applied three different machine learning (ML) models, regression trees, support vector regression (SVR) and gene expression programming (GEP), two ensemble ML methods, Random Forest and GEP Forest, and non-linear regression analysis to an experimental fire dataset. Results show SVR and GEP as the best ML methods to model the curing level impact on fire spread. No differences in model fit were observed between the best ML methods and non-linear regression analysis.

关键词： Bushfire Feature importance gene expression programming Grasslands Machine learning Remote region

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RETRACTED: Predicting the effects of nanoparticles on compressive strength of ash-based geopolymers by gene expression programming (Retracted Article)

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NEURAL COMPUTING & APPLICATIONS 2013年第6期23卷 1677-1685页

作者： Nazari, Ali Riahi, Shadi Islamic Azad Univ Dept Mat Sci & Engn Saveh Branch Saveh Iran

In the present work, the effect of SiO2 and Al2O3 nanoparticles on compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nanoalumina and nanosilica has been predicted by gene expression programming. The models were constructed by 12 input parameters, namely the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO2 nanoparticle content, Al2O3 nanoparticle content, oven curing temperature, oven curing time and test trial number. The value for the output layer was the compressive strength. According to the input parameters in gene expression programming models, the data were trained and tested, and the effects of SiO2 and Al2O3 nanoparticles on compressive strength of the specimens were predicted with a tiny error. The results indicate that gene expression programming model is a powerful tool for predicting the effect of nanoparticles on compressive strength of the geopolymers in the considered range.

关键词： Geopolymer Compressive strength Ash mixture Nanoparticles gene expression programming

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Grey-box solution for predicting thermo-mechanical response of rocks

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GEOTHERMICS 2024年 124卷

作者： Nawaz, Muhammad Naqeeb Natl Univ Sci & Technol NUST NUST Inst Civil Engn Islamabad 44000 Pakistan

Evaluating the thermo-mechanical response of rocks under high temperature treatments is crucial for various engineering geology projects. Current predictions of rock thermo-mechanical response rely on simplistic mathematical fittings treating temperature as a reduction factor, while existing machine learning algorithms often present practical challenges due to their black-box solutions. In this study, highly practical grey-box solutions, utilizing gene expression programming (GEP) are proposed for forecasting rock strength following hightemperature treatments. The dataset, comprising temperature, rock type, rock density, sample size, crack damage stress, confining pressure, and elastic modulus, serves as input parameters, with rock strength from triaxial compression tests as the output. Three grey-box solutions (mathematical formulations) based on distinct input parameter sets are proposed, all demonstrating excellent accuracy with high R-2-values (R-2 > 0.95) and low error values across both the training and testing phases. Feature importance analysis highlights crack damage stress, confining pressure, and elastic modulus as statistically significant parameters influencing the strength of rocks subjected to high temperatures. External validation of the proposed models indicates strong generalization capabilities, underscoring their ability to perform well beyond the training data. Furthermore, a monotonicity study demonstrates that the proposed models align with the expected physical processes. The proposed formulations offer valuable field implications, effectively addressing the limitations of labor-intensive and costly laboratory processes for evaluating rock thermo-mechanical responses.

关键词： Thermo-mechanical response High temperature treatment machine learning Grey-box solutions gene expression programming

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Collective of Base Classifiers for Mining Imbalanced Data 22nd

Collective of Base Classifiers for Mining Imbalanced Data

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22nd Annual International Conference on Computational Science (ICCS)

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat Fac Math Phys & Informat PL-80308 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)9783031087547;9783031087530

Mining imbalanced datasets is a challenging and difficult problem. In this paper we adress it by proposing GEP-NB classifier based on the oversampling technique. It combines two learning methods - gene expression programming and Naive Bayes, which cooperate to produce a final prediction. At the pre-processing stage a simple mechanism for generating synthetic minority class examples and balancing the training set is used. Next, two genes g1 and g2 are evolved using gene expression programming. They differ by applying in each case a different procedure for selecting synthetic minority class examples. If the class prediction by g1 agrees with the class prediction made by g2, their decision is final. Otherwise the final predictive decision is taken by the Naive Bayes classifier. The approach is validated in an extensive computational experiment. Results produced by GEP-NB are compared with performance of several state-of-the-art classifiers. Comparisons show that GEP-NB offers a competitive performance.

关键词： Imbalanced data Oversampling gene 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 *** 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 *** first variable is changed among seven levels,whereas the other variables are changed among three *** 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 *** related variables for each output are imported for the training and testing of different surrogate *** 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 *** show that GEP outperforms MLR,MLnER,and *** results indicate that the rotation and deflection of the CTMC depend on the *** addition,the fire resistance increases with a decrease in the beam length;thus,a shorter beam can increase the fire resistance of 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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Designing spiking neural models of neurophysiological recordings using gene expression programming

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BMC Neuroscience 2013年第1期14卷 1-2页

作者： Josafath I Espinosa-Ramos Nareli Cruz-Cortes Roberto A Vazquez Computation Research Center National Polytechnic Institute Mexico City 0738 Mexico Intelligent Systems Group Faculty of Engineering La Salle University Mexico City 06140 Mexico

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Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2021年第4期21卷 1–28页

作者： Deng, Song Chen, Fulin Dong, Xia Gao, Guangwei Wu, Xindong Nanjing Univ Posts & Telecommun Nanjing 210023 Jiangsu Peoples R China Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei 230009 Peoples R China Mininglamp Acad Sci Mininglamp Technol Shanghai 200232 Peoples R China

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved gene expression programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved gene expression programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE. MAPE, RSME, and R-2.

关键词： gene expression programming abnormal load recognition power load forecasting probability distribution adaptive evolution

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A comparative study of LightGBM, XGBoost, and GEP models in shear strength management of SFRC-SBWS

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STRUCTURES 2024年 61卷

作者： Alshboul, Odey Almasabha, Ghassan Shehadeh, Ali Al-Shboul, Khaled Hashemite Univ Fac Engn Dept Civil Engn POB 330127 Zarqa 13133 Jordan Yarmouk Univ Hijjawi Fac Engn Technol Dept Civil Engn Irbid 21163 Jordan Jordan Univ Sci & Technol Dept Nucl Engn POB 3030 Irbid 22110 Jordan

Steel fiber reinforced concrete (SFRC) was revealed to have superior mechanical properties compared to plain concrete, especially under shear loads. However, Due to a lack of prediction models for SFRC-Slender Beams without Stirrups (SFRC-SBWS) in the available literature, the application of SFRC-SBWS in reinforced concrete (RC) constructions is limited. This research aims to fill a literature gap by analyzing different Machine Learning (ML) algorithms, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and gene expression programming (GEP), to forecast the shear strength of SFRC-SBWS specimens. A database of 333 tested SFRC-SBWS specimens was collected and analyzed. The results revealed the LightGBM and XGBoost algorithms surpassed other studied algorithms with Coefficient of determination (R2) = 95.74% and 93.27%, Root Mean Square Error (RMSE) = 20.04 kN and 25.19 kN, respectively. A GEP-based closed-form model was suggested to forecast the shear strength, considering the most important factors, including beam depth effect, beam width, and longitudinal steel reinforcement ratio. The proposed GEP model has an R2 = 91.12% and RMSE = 101.44 kN, showing a slightly lower prediction accuracy. The effectiveness of the studied models was evaluated from various angles, and the results demonstrate the importance of managing new technological advancements to create reliable support methods. Engineers that use these approaches get a dependable tool for estimating shear resistance, which is crucial in guaranteeing the safety and lifespan of RC structures. The successful combination of classic engineering approaches and revolutionary ML strategies indicates the potential for improving accuracy and reducing bias in prediction-oriented investigations. As the engineering field supports technological improvements, combining robust and efficient prediction models, as described in this paper, can contribute considerably to resilient structure d

关键词： SFRC Machine learning Shear strength Beams without stirrups gene expression programming

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