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gene expression programming-based model prediction of performance and emission characteristics of a diesel engine fueled with linseed oil biodiesel/diesel blends: An artificial intelligence approach

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 1385-1399页

作者： Sharma, Prabhakar Guru Nanak Dev Inst Technol Mech Engn Dept Thermal Engn Lab Delhi 110089 India

In the present study, Artificial Intelligence (AI) based gene expression programming (GEP) is used to develop a model to predict the performance and emission characteristics of a single-cylinder diesel engine fueled with linseed oil methyl ester (LOME) blended with mineral diesel. The data to be used for GEP were obtained experimentally by varying the biodiesel/mineral diesel blending ratio, engine load, fuel injection pressure, and fuel injection timing. The GEP-based model was developed to predict the brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), NOx, and unburned hydrocarbon (UHC) emission. A major part (70%) of the collected data was used for training and remaining (30%) was used for model validation. The developed GEP model was robust enough to provide a high degree of accuracy in the prediction of engine performance and emission parameters. The statistical measure of model robustness such as coefficient of correlation (R) was in the range of 0.9926-0.9999 and the coefficient of determination (R2) was 0.9854-0.9998 for the output prediction. The root mean square error (RMSE) in the GEP model predicted results were in the range of 0.0048-2.597 and mean absolute error (MSE) was ***: AI: Artificial Intelligence;BSFC: Brake specific fuel consumption;bTDC: Before top dead center;BTE: Brake thermal efficiency;B0: (MOME 0% + Mineral diesel 100%);B10: (MOME 10% + Mineral diesel 90%);B20: (MOME 20% + Mineral diesel 80%);CI: Compression ignition;CO: Carbon monoxide;ET: expression tree;FIP: Fuel injection pressure;FIT: Fuel injection timing;GEP: gene expression programming;ICE: Internal combustion engine;ID: Ignition delay;MAE: Mean absolute error;MIT: Machine identical tool;NOx: Nitrogen oxides;ppm: Parts per million;R: Regression coefficient;R2: Coefficient of determination;RMSE: Root mean square error;UHC: Unburned hydrocarbon

关键词： Artificial intelligence gene expression programming alternative fuel biodiesel linseed oil methyl ester

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gene expression programming for parametric optimization of an electrochemical machining process

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年第2期17卷 649-666页

作者： Mandal, Kishal Kalita, Kanak Chakraborty, Shankar Jadavpur Univ Dept Prod Engn Kolkata India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Mech Engn Avadi India

Due to its capability to generate intricate shape profiles on various electrically conductive difficult-to-machine work materials, electrochemical machining (ECM) process has found immense applications in aerospace, automotive, die making, artillery and surgical instruments manufacturing industries. Like other machining processes, the performance of an ECM process with respect to various product quality characteristics is also influenced by its different input parameters. This paper proposes the novel application of an almost unexplored evolutionary algorithm in the form of gene expression programming for parametric optimization of an ECM process while treating electrolyte concentration, electrolyte flow rate, applied voltage and tool feed rate as the input parameters, and material removal rate (MRR) and average surface roughness (Ra) as the responses. It is noticed that an optimal parametric intermix as electrolyte concentration = 15.1417 g/l, electrolyte flow rate = 6.1846 l/min, applied voltage = 15.94709 V and tool feed rate = 0.99995 mm/min would lead to simultaneous optimization of both the responses. A comparative analysis of its optimization performance in relation to accuracy and variability of the derived solutions, and computational speed against genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) optimization techniques validates its superiority over the others. At the derived optimal combination, MRR is increased by 26.14, 3.64, 12.50 and 12.55%, and Ra is reduced by 1.28, 21.97, 21.03 and 7.86% against GA, PSO, ACO and ABC techniques. An optimal Pareto front is also developed to determine the optimal parametric intermixes for having maximum MRR and minimum Ra values. The scatter plots would assist the process engineers in investigating the influences of the input parameters of the considered ECM process on its responses.

关键词： ECM gene expression programming Optimization Process parameter Response

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gene expression programming for Quantum Computing

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ACM TRANSACTIONS ON QUANTUM COMPUTING 2023年第4期4卷 1-14页

作者： Alvarez, Gonzalo Bennink, Ryan Irle, Stephan Jakowski, Jacek Oak Ridge Natl Lab POB 2008MS6102 Oak Ridge MS 37831 USA Oak Ridge Natl Lab POB 2008MS6211 Oak Ridge MS 37831 USA Oak Ridge Natl Lab POB 2008MS6493 Oak Ridge MS 37831 USA

We introduce QUANTUMGEP, a scientific computer program that uses gene expression programming (GEP) to find a quantum circuit that either (1) maps a given set of input states to a given set of output states or (2) transforms a fixed initial state to minimize a given physical quantity of the output state. QUANTUMGEP is a driver program that uses evendim, a generic computational engine for GEP, both of which are free and open source. We apply QUANTUMGEP as a powerful solver for MaxCut in graphs and for condensed matter quantum many-body Hamiltonians.

关键词： genetic algorithms gene expression programming quantum computing condensed matter quantum chemistry

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gene expression programming-based multivariate model for earth infrastructure: predicting ultimate bearing capacity of rock socketed shafts in layered soil-rock strata

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2024年第4期10卷 5241-5256页

作者： Nawaz, Muhammad Naqeeb Haseeb, Muhammad Qamar, Sana Ullah Hassan, Waqas Shahzad, Arfan Natl Univ Sci & Technol NUST NUST Inst Civil Engn Islamabad 44000 Pakistan Natl Univ Comp & Emerging Sci FAST NUCES Dept Sci & Humanities Chiniot Faisalabad Campus Faisalabad Pakistan

The evaluation of ultimate bearing capacity (Qu) of rock socketed shafts (RSSs) is crucial for the design of foundation systems. This study proposes a new data-driven multivariate formulation using gene expression programming (GEP) to assess Qu of RSSs in layered soil-rock strata. A dataset of 151 points from the literature was used to develop the prediction model. Six influencing features were considered: the material constant for rock (mi), unconfined compression strength (sigma c), geological strength index (GSI), length of socket in soil (Lss), length of socket in rock (Lsr), and diameter of the socket base (d). The model predicts the ultimate bearing capacity factor (Nu). The proposed GEP-based formulation showed high accuracy. For the training data, the correlation coefficient (R) was 0.90, with root mean square error (RMSE), mean absolute error (MAE), relative root mean square error (RRMSE), relative standard error (RSE), and performance index (rho) values of 1.40, 0.98, 0.37, 0.18, and 0.2, respectively. For the testing data, R was 0.86, with RMSE, MAE, RRMSE, RSE, and rho values of 1.37, 0.93, 0.49, 0.26, and 0.26, respectively. The model's efficiency was validated through comparison with existing correlations, sensitivity analysis, and rock socketed shaft test results. The proposed GEP-based model showed significantly improved performance over the ensemble learning (EL) model from the previous study, with a 44.23% higher R value, a 22% lower RMSE, and a 26% lower MAE. Additionally, a computer application was developed to facilitate quick estimation of Nu, enhancing the model's practical applicability in earth systems and infrastructure.

关键词： Ultimate bearing capacity factor Rock socketed shafts Layered soil-rock strata gene expression programming Sensitivity analysis

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gene expression programming and data mining methods for bushfire susceptibility mapping in New South Wales, Australia

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NATURAL HAZARDS 2022年第2期113卷 1349-1365页

作者： Hosseini, Maryamsadat Lim, Samsung Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia

Australia is one of the most bushfire-prone countries. Prediction and management of bushfires in bushfire-susceptible areas can reduce the negative impacts of bushfires. The generation of bushfire susceptibility maps can help improve the prediction of bushfires. The main aim of this study was to use single gene expression programming (GEP) and ensemble of GEP with well-known data mining to generate bushfire susceptibility maps for New South Wales, Australia, as a case study. We used eight methods for bushfire susceptibility mapping: GEP, random forest (RF), support vector machine (SVM), frequency ratio (FR), ensemble techniques of GEP and FR (GEPFR), RF and FR (RFFR), SVM and FR (SVMFR), and logistic regression (LR) and FR (LRFR). Areas under the curve (AUCs) of the receiver operating characteristic were used to evaluate the proposed methods. GEPFR exhibited the best performance for bushfire susceptibility mapping based on the AUC (0.892 for training, 0.890 for testing), while RFFR had the highest accuracy (95.29% for training, 94.70% for testing) among the proposed methods. GEPFR is an ensemble method that uses features from the evolutionary algorithm and the statistical FR method, which results in a better AUC for the bushfire susceptibility maps. Single GEP showed AUC of 0.884 for training and 0.882 for testing. RF also showed AUC of 0.902 and 0.876 for training and testing, respectively. SVM had 0.868 for training and 0.781 for testing for bushfire susceptibility mapping. The ensemble methods had better performances than those of the single methods.

关键词： gene expression programming Bushfire Susceptibility map Machine learning

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Application of group method of data handling and gene expression programming to modeling molecular diffusivity of CO2 in heavy crudes

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 237卷

作者： Lv, Qichao Zhou, Tongke Zheng, Haimin Rezaei, Farzaneh Chen, Weiyu Mohammadi, Mohammad-Reza Liu, Yang Hemmati-Sarapardeh, Abdolhossein China Univ Petr Coll Carbon Neutral Energy Beijing Peoples R China Univ Manchester Dept Chem Engn Manchester England CNOOC Res Inst Cooperat Engn & Design Dept Beijing Peoples R China Amirkabir Univ Technol Dept Petr Engn Tehran Iran CNOOC Energy Technol & Serv Ltd Engn & Technol Branch Tianjin Peoples R China Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran PetroChina Changqing Oilfield Co Xian Shaanxi Peoples R China

Successful enhancement of heavy oil and bitumen production using CO2-based enhanced oil recovery (EOR) techniques requires extensive knowledge about the diffusivity of CO2 in heavy crudes. In this work, two advanced correlative approaches, namely group method of data handling (GMDH) and gene expression programming (GEP), were utilized to establish simple-to-use mathematical correlations by applying a comprehensive dataset including 260 laboratory data of CO2 diffusion coefficient (DC) in heavy crudes and taking into account all the parameters affecting the accuracy of the models. Moreover, the response surface and contour plots were analyzed to obtain the desired response values and operating conditions. The GMDH correlation represented more reliable results with better statistical criteria including average absolute percentage error (AAPRE) values of 7.47%, 7.83%, and 7.54% for training, testing, and the total sets, respectively. Also, the results showed that the increase in pressure and/or temperature caused an increment in the amount of diffusivity of CO2 in heavy crudes;and the maximum amount of CO2 DC was obtained at the upper limit of these operational parameters. At a given temperature and pressure, it appears that the highest CO2 DC in heavy crudes was obtained when the CO2 mass fraction was less than 0.02. Furthermore, the Pearson and Spearman correlation coefficients were used to investigate linear and non-linear relationships between input variables and the responses of GMDH and GEP correlations. The mass fraction of CO2, temperature, pressure, and temperature_PVT had a direct relationship, and on the other hand, the viscosity and density of crudes had a reverse relationship with CO2 DC. Finally, the reliability of the data bank of CO2 DC in heavy crudes along with the high validity of GMDH and GEP correlations was verified by the leverage technique. Notably, no out-of-leverage data were identified, with only seven and five data points considered as suspe

关键词： CO2-Based enhanced oil recovery Heavy crude oil Bitumen CO2 diffusion coefficient Group method of data handling gene expression programming

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Wind turbine energy forecasting using real wind farm's measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2025年第2期22卷 414-431页

作者： Mehmood, Zahid Wang, Zhenyu Zhejiang Univ Coll Civil Engn & Architecture Yuhangtang Rd 866 Hangzhou 310058 Peoples R China

Forecasting wind power is vital to ensure steady, sustainable, and renewable energy. The complex nonlinear nature of wind flow and its interrelated factors make power prediction challenging. This study predicted wind power curves inspired by the Biologically Inspired Evolutionary Computation (BIEC) paradigm, incorporating gene expression programming (GEP), Artificial Neural Networks (ANN), Least Square Support Vector Machines (LSSVM), and Random Forest (RF) models. Key input parameters include wind speed, yaw azimuth, turbulent intensity, veer, horizontal and vertical shear, ambient temperature, blade pitch, and rotor speed. The study evaluates these models' effectiveness using root mean square error (RMSE) and correlation coefficient R. Results indicate that the GEP model offers transparent modeling, emphasizing critical inputs like wind speed, rotor speed, blade pitch angle, and temperature for wind power prediction. The GEP model identified wind speed, rotor speed, blade pitch angle, and temperature as the most influential parameters, with variable importance index (Ii) values of 69.39%, 24.39%, 5.46%, and 0.64% for training, and 69.37%, 25.03%, 4.98%, and 0.54% for validation. The study demonstrates the GEP model's efficacy in accurately forecasting wind power curve, achieving a high correlation with training and validation coefficients of 0.9899 and 0.994, respectively, outperforming traditional models with minor errors.

关键词： data fusion data monitoring gene expression programming wind energy forecasting Wind power generation

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Consideration of Non-Locality for gene expression programming: Modeling the Transition to Turbulence in the Boundary Layer

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FLOW TURBULENCE AND COMBUSTION 2025年 1-23页

作者： Bleh, Alexander Morsbach, Christian German Aerosp Ctr Inst Prop Technol D-51147 Cologne Germany

The consideration of the inherently non-local characteristics of turbulence is an open challenge and subject to many investigations. Recent approaches rely on the utilization of spatially configured Neural Networks such as e.g. Convolutional Neural Networks to account for non-local effects (Comput. Methods Appl. Mech. Eng. 384:113927, 2021). Nevertheless, approaches featuring Neural Networks are not easily available for gene expression programming. An alternative option, to consider non-local effects, is the use of partial differential equations (PDE) like an additional convection-diffusion equation as is done for example in several transition models such as the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}- model by Menter et al. (Flow Turbul. Combust. 583-619, 2015). Consequently, instead of only modeling a local correction factor directly using GEP, we equip the input quantities with an additional optional convection-diffusion equation of which we model the production term, diffusion constants and boundary type. The methodology is applied on a set of low pressure turbine testcases in order to find transition models. Resulting expressions are further analysed in terms of underlying mechnims and logical foundations.

关键词： gene expression programming Data-driven Turbulence Transition Non-locality

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Gradient Information and Regularization for gene expression programming to Develop Data-Driven Physics Closure Models

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FLOW TURBULENCE AND COMBUSTION 2025年第1期114卷 145-175页

作者： Waschkowski, Fabian Li, Haochen Deshmukh, Abhishek Grenga, Temistocle Zhao, Yaomin Pitsch, Heinz Klewicki, Joseph Sandberg, Richard D. Univ Melbourne Dept Mech Engn Melbourne Vic 3010 Australia Peking Univ Ctr Appl Phys & Technol Beijing 100871 Peoples R China Rhein Westfal TH Aachen Inst Combust Technol D-52062 Aachen Germany

Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as gene expression programming (GEP). This paper introduces the concept of adaptive symbols to the GEP framework by Weatheritt and Sandberg (J Comput Phys 325:22-37, 2016a) to develop advanced physics closure models. Adaptive symbols utilize gradient information to learn locally optimal numerical constants during model training, for which we investigate two types of nonlinear optimization algorithms. The second contribution of this work is implementing two regularization techniques to incentivize the development of implementable and interpretable closure models. We apply L2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_2$$\end{document} regularization to ensure small magnitude numerical constants and devise a novel complexity metric that supports the development of low complexity models via custom symbol complexities and multi-objective optimization. This extended framework is employed to four use cases, namely rediscovering Sutherland's viscosity law, developing laminar flame speed combustion models and training two types of fluid dynamics turbulence models. The model prediction accuracy and the convergence speed of training are improved significantly across all of the more and less complex use cases, respectively. The two regularization methods are essential for developing implementable closure models and we demonstrate that the developed turbulence models substantially improve simulations over state-of-the-art models.

关键词： gene expression programming Nonlinear optimization Regularization Model complexity

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A Predictive Model for the Equivalent Hydraulic Aperture of Single Fracture Using gene expression programming

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ROCK MECHANICS AND ROCK ENGINEERING 2025年第2期58卷 1975-1993页

作者： Zhai, Zixuan Zhang, Shuai Ni, Zhihui Li, Dongxu Qian, Ruipeng Chongqing Jiaotong Univ Southwestern Res Inst Water Transport Engn Chongqing 400016 Peoples R China Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Taiyuan Univ Sci & Technol Coll Safety & Emergency Management Engn Taiyuan 030024 Peoples R China

The accurate prediction of the equivalent hydraulic aperture (EHA) in a single fracture is of paramount importance for the investigation of fracture flow capacity. Previous studies primarily relied on fitting experimental data to obtain the EHA, failing to accurately characterize the impact of the strong nonlinearity among fracture geometric parameters on EHA. This research integrates 170 datasets of measured EHA, incorporating key geometric parameters: average mechanical aperture (bm), peak roughness height (xi), and the joint roughness coefficient (JRC). These three main geometric characteristic parameters, used as input parameters, are the primary factors influencing the EHA. The gene expression programming (GEP) method was utilized to construct a model for predicting EHA (bh). The developed GEP model was then compared with 6 existing empirical models, as well as 2 AI models (Random Forest (RF) and Support Vector Machine (SVM)). The results indicate that the GEP model (R2 = 0.997) and the SVM model (R2 = 0.9944) both demonstrate high accuracy, evidenced by the GEP model's low RMSE (0.014) and MAE (0.01) values. The sensitivity analysis indicates that in the GEP model, the hydraulic aperture increases linearly with the increase of bm, decreases linearly with the increase of xi, and initially increases but then decreases as JRC increases. These findings provide insightful contributions to the assessment of hydraulic conductivity in the rough single fracture. GEP model accurately predicts hydraulic aperture (R-2 = 0.997) with clear *** model outperforms six existing empirical & two AI models in *** m, JRC as key in hydraulic aperture, impact is *** model shows complex impact of geometry on hydraulic aperture

关键词： gene expression programming Peak roughness height Average mechanical aperture Joint roughness coefficient Single fracture

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