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A framework for designing of genetic operators automatically based on gene expression programming and differential evolution

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NATURAL COMPUTING 2021年第3期20卷 395-411页

作者： Jiang, Dazhi Tian, Zhihang He, Zhihui Tu, Geng Huang, Ruixiang Shantou Univ Dept Comp Sci Shantou 515063 Peoples R China

The design of genetic operators is absolutely one of the core work of evolutionary algorithms research. However, the essence of the evolutionary algorithms is that a lot of algorithm design is based on the manual result analysis, summarize, refine, feedback, and then, the algorithms are designed adaptively and correspondingly. This kind of design scheme needs artificial statistics and analysis of large amounts of data, which greatly increases the burden of the designers. To solve this problem, an evolutionary algorithm framework based on genetic operator automatic design is proposed in this paper. In the first step, gene expression programming and Differential Evolution methods are combined together and used to design the genetic operators automatically and adaptively, this hybrid method can not only explore solutions in problem space for the problem solving as most classical evolutionary algorithms do, but also generate genetic operators automatically in operator space for the proper operators extraction and selection related to the evolutionary algorithms . In the second step, the designed operators are adopted into the typical evolutionary algorithms to verify the performance and the result shows that the new designed genetic operator is superior to or at least equivalent to some existing DE variants in a set of classical benchmark functions. More importantly, this paper is not aimed at designing high performance algorithms, but to provide a new perspective for algorithms designing, and to provide a reference scheme for the machine algorithms designing.

关键词： Evolutionary algorithm Automatically designing Self adaptive differential evolution algorithm gene expression programming

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Modelling of expansion ratio and half-life of foamed bitumen using gene expression programming

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INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2021年第3期22卷 369-381页

作者： Eleyedath, Abhary Kar, Siksha Swaroopa Swamy, Aravind Krishna Indian Inst Technol Delhi Dept Civil Engn New Delhi India CSIR Cent Rd Res Inst Pavement Engn Area New Delhi India

A typical foamed bituminous mixture design process involves production and evaluation of foamed bitumen over a range of foaming temperatures and water contents. Such a process of elaborate testing (over a range of temperatures and water contents) is a time- and resource-consuming exercise. This work presents a novel gene expression programming-based approach to predict the expansion ratio and half-life of foamed bitumen. To develop these predictive models, a database that consists of foamed bitumen characteristics (i.e. expansion ratio and half-life) and physico-chemical properties of bitumen was developed using six distinct binders (of different viscosity grades). Initially, all binders were tested for their physical properties and chemical composition. Furthermore, foamed bitumen characteristics of these binders were measured over the range of test temperatures (110 degrees C to 200 degrees C) and water contents (2% to 11%). The final database consisted of 166 observations (arising out of different combinations of 6 binder types, 6 water contents, and 8 test temperatures and an average of 3 replicates for each condition). expressions to predict foamed bitumen characteristics have been developed based on temperature, water content, viscosity, and chemical composition using this database through the gene expression programming technique. The numerical values of goodness of fit parameters indicated that models based on physical properties are able to predict expansion ratio and half-life with reasonable accuracy. Furthermore, the use of information regarding chemical composition along with physical properties improved the accuracy of predictive models significantly. These expressions can be used to identify the feasible range of test conditions for the production of foamed bitumen before actual testing is commenced.

关键词： Cold mix technology foamed bitumen expansion ratio half-life gene expression programming

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Permeability Prediction in Tight Carbonate Rocks Using gene expression programming (GEP)

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ROCK MECHANICS AND ROCK ENGINEERING 2021年第5期54卷 2581-2593页

作者： Wei, Yufeng Xue, Xinhua Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Pro Chengdu 610059 Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu 610065 Peoples R China

Figures 12, 13 and 14 plot the variations of three input variables (e.g., HT ht , HT F ht and HT dPT ht ) against the permeability HT k ht predicted by the GEP model. The constants of the first gene HT c2 ht , HT c7... 详细信息

Figures 12, 13 and 14 plot the variations of three input variables (e.g., HT

ht , HT

F

ht and HT

d_{PT}

ht ) against the permeability HT

k

ht predicted by the GEP model. The constants of the first gene HT

c_{2}

ht , HT

c_{7}

ht and HT

c_{8}

ht are -8.833, -8.661 and -6.561, respectively. 3 can be written mathematically as HT

(x \times 5) - (cos)

ht . The constants of the second gene HT

c_{2}

ht and HT

c_{5}

ht are -9.067 and 3.056, respectively. [Extracted from the article]

关键词： gene expression programming Permeability Tight carbonate rocks Artificial neural network Conventional models genetic algorithms

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Dynamic flexible job shop scheduling method based on improved gene expression programming

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MEASUREMENT & CONTROL 2021年第7-8期54卷 1136-1146页

作者： Zhang, Chunjiang Zhou, Yin Peng, Kunkun Li, Xinyu Lian, Kunlei Zhang, Suyan Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Luoyu Rd 1037 Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Sch Management Wuhan Peoples R China Walmart Technol Bentonville AR USA Capital Aerosp Machinery Co Ltd Beijing Peoples R China

Dynamic scheduling is one of the most important key technologies in production and flexible job shop is widespread. Therefore, this paper considers a dynamic flexible job shop scheduling problem considering setup time and random job arrival. To solve this problem, a dynamic scheduling framework based on the improved gene expression programming algorithm is proposed to construct scheduling rules. In this framework, the variable neighborhood search using four efficient neighborhood structures is combined with gene expression programming algorithm. And, an adaptive method adjusting recombination rate and transposition rate in the evolutionary progress is proposed. The test results on 24 groups of instances with different scales show that the improved gene expression programming performs better than the standard gene expression programming, genetic programming, and scheduling rules.

关键词： Dynamic scheduling flexible job shop scheduling gene expression programming variable neighborhood search

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geneOS: An accurate equation of state for the fast calculation of two-phase geofluids properties based on gene expression programming

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COMPUTER PHYSICS COMMUNICATIONS 2024年 297卷

作者： Esmaeilpour, Morteza Nitschke, Fabian Kohl, Thomas Karlsruhe Inst Technol Inst Appl Geosci Geothermal Energy & Reservoir Technol Karlsruhe Germany

Numerical simulation of two-phase multicomponent flows requires solving continuity, momentum, energy, and transport equations. Typically, these conservation equations are solved for computing the main variables of pressure, enthalpy, velocity, and composition. Variation of thermophysical properties (e.g., density, viscosity, etc.) as functions of the main variables necessitates introducing equations of state (EOS) to the modeling scheme, equating the number of unknowns and equations. The problem arises here as almost all the available EOSs in the literature receive temperature as an input, which is not a main variable. Guessing temperature, as an unknown input, imposes more iterations on the already iterative algorithm of the EOS and increases the computational cost. The primary focus of this study is to provide highly-precise, but fast EOS scheme for calculating two-phase fluid properties using artificial intelligence algorithms. In the first step, a Fugacity-Activity model is implemented to supply a supervised learning algorithm with a large dataset. The provided data are fed into a machine-learning (ML) model called gene expression programming (GEP). The outputs of this GEP model are high-preciseness explicit formulas for non-iterative computing of temperature and equilibrium constants. Testing the proposed GEP equations for 1,000,000 arbitrary sets of inputs revealed high accuracy in predicting desired outputs (e.g., < 0.6% error in calculating temperature). Implementing GEP equations in modeling platforms can result in similar to 90% reduction in EOS-related computational cost. This ML-based EOS is a transparent box for computing thermophysical properties of two-phase mixtures containing H2O, CO2, CH4, N-2, H2S, NaCl, KCl, CaCl2, and MgCl2.

关键词： Equation of state gene expression programming Machine learning Two-phase flow Multicomponent geofluids

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Defined an Optimized Molding for Physical and Mechanical Properties of W–Cu Nanocomposite Through Spark Plasma Sintering Using gene expression programming: The Combination of Artificial Intelligence and Material Science

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SN Computer Science 2022年第1期3卷 37页

作者： Shojaei, Mohammdreza Khayati, Gholam Reza Department of Materials Science and Engineering Sharif University of Technology Tehran Iran Department of Materials Science and Engineering Shahid Bahonar University of Kerman P.O. Box No Kerman 76135-133 Iran

Cu–W nanocomposites had many engineering applications due to the unique characteristics including hardness, transverse rupture strength, electrical conductivity, thermal conductivity, and relative density. This study was an attempt to used gene expression programming as a powerful soft computing technique to model the parameters for the synthesis of Cu–W nanocomposite prepared by spark plasma sintering. First, 97 different reliable experiments were carried out considering the type of Cu and W concentration, temperature, die pressure, and heat rate as input variables. The hardness, transverse rupture modules, electrical conductivity, thermal conductivity, and relative density of nanocomposite defined as output variable separately. An absolute fraction of variance (R²), mean absolute percentage error (MAPE), root relative squared error (RRSE), and mean squared error (MSE) were considered to validate the most appropriate GEP models. Sixfold cross validation was used through testing and training steps of GEP modeling. The results were divided randomly into 68 training sets and 29 testing sets. Finally, the best GEP models were selected for each output parameter. Sensitivity analyses are done to determine the rank of the practical parameters on each investigated properties and revealed that on hardness, transverse rupture modules, electrical conductivity, thermal conductivity, and relative density of nanocomposite, respectively. The results confirmed the ability of GEP for all parameters of Cu–W nanocomposites prepared by spark plasma sintering. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.

关键词： Cu–W nanocomposite gene expression programming Modeling Powder metallurgy Spark plasma sintering

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Predicting mechanical properties of sustainable green concrete using novel machine learning: Stacking and gene expression programming

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REVIEWS ON ADVANCED MATERIALS SCIENCE 2024年第1期63卷

作者： Ashraf, Muhammad Waqas Khan, Adnan Tu, Yongming Wang, Chao Ben Kahla, Nabil Javed, Muhammad Faisal Ullah, Safi Tariq, Jawad Southeast Univ Sch Civil Engn Key Lab Concrete & Prestressed Concrete Struct Minist EducNatl Engn Res Ctr Prestressing Technol Nanjing 211189 Peoples R China Southeast Univ Sch Transportat Jiulonghu Campus Nanjing 211189 Jiangsu Peoples R China Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Div Struct & Fire Engn SE-97187 Lulea Sweden Bath Spa Univ Global Banking Sch Dept Construct Management Devonshire St North Manchester M12 6JH England King Khalid Univ Dept Civil Engn Coll Engn POB 394 Abha 61411 Saudi Arabia COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad Pakistan Western Caspian Univ Dept Tech Sci Baku Azerbaijan Henan Univ Technol Sch Civil Engn Zhengzhou 450001 Henan Peoples R China

Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with R values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.

关键词： rice husk ash stacking gene expression programming tensile and flexural strength parametric analysis SHAP analysis

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A decision model based on gene expression programming for discretionary lane-changing move

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TRANSPORTATION PLANNING AND TECHNOLOGY 2024年第7期47卷 1133-1155页

作者： Bagdatli, Muhammed Emin Cihangir Choghtay, Raz Mohammad Nigde Omer Halisdemir Univ Engn Fac Dept Civil Engn Nigde Turkiye

This study focuses on modeling Discretionary Lane-Changing (DLC), which accounts for the majority of lane-change moves in traffic flows. A binary decision model for lane-changing moves was improved with the method of gene expression programming (GEP). The decision to prefer GEP is due to its high performance in a variety of engineering solutions in the literature. The GEP model was trained with Next generation SIMulation (NGSIM) trajectory data gathered at the I-80 Freeway in Emeryville, California, and then tested with data gathered at the U.S. Highway 101 in LA, California. The test results indicate that the model made decisions of "change lane" with 92.98% accuracy, and "do not change lane" with 99.65% accuracy. A sensitivity analysis was also conducted to discover potential limits of the performance of the GEP model. The performance of this model was compared with other high-performance decision models developed with the NGSIM's DLC data in the literature and with TransModeler's gap acceptance model. This comparison indicates that the GEP model is the most successful decision model for discretionary lane-changing moves. The GEP model has a high potential to be applied in DLC decision support systems in (semi-) automated vehicles, as well as traffic simulation software.

关键词： Discretionary lane-changing decision support gene expression programming lane change NGSIM

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On the evaluation of crude oil oxidation during thermogravimetry by generalised regression neural network and gene expression programming: application to thermal enhanced oil recovery

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COMBUSTION THEORY AND MODELLING 2021年第7期25卷 1268-1295页

作者： Mohammadi, Mohammad-Reza Hemmati-Sarapardeh, Abdolhossein Schaffie, Mahin Husein, Maen M. Karimian, Milad Ranjbar, Mohammad Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran Univ Calgary Dept Chem & Petr Engn Calgary AB Canada Razi Univ Dept Chem Engn Kermanshah Iran

Enhancing oil recovery using in-situ combustion (ISC) is an attractive alternative, especially for heavy crudes. During ISC, part of the hydrocarbon is pyrolysed/oxidised, which generates heat and deposits fuel in the combustion front. In this study, crude reactions during ISC are modelled after their thermogravimetry thermo-oxidative profiles using advanced machine learning systems. The model inputs include the weight per cent of asphaltenes, resins, and degrees API gravity of the oil as well as the heating rate and the temperature. Four types of artificial neural networks (ANNs);namely multilayer perceptron (MLP), generalised regression neural network (GRNN), cascade-forward neural network (CFNN), and radial basis function (RBF) neural network, were employed to develop models for accurate prediction of the weight per cent of residual crude oil based on 2289 experimental data points. Moreover, three optimisation algorithms;including Bayesian Regularisation (BR), Levenberg-Marquardt (LM), and Scaled Conjugate Gradient (SCG) were applied in the training step of MLP and CFNN to improve the prediction ability. GRNN provided the most accurate prediction with similar to 2.3% overall average absolute per cent relative error and coefficient of determination of 0.9983. GRNN model is reliable for crude oils with degrees API gravity of 5-35 and up to 820 degrees C. Lastly, a mathematical correlation was developed to estimate the residual crude oil from thermogravimetry analysis using gene expression programming (GEP). GEP also predicted the thermo-oxidative profile with high accuracy. On the basis of sensitivity analysis, residue formation during crude oil oxidation was impacted the most by the temperature, oil degrees API gravity, and asphaltenes content, respectively. The Leverage approach identified 2.9% of the data points as doubtful.

关键词： in-situ combustion EOR TGA intelligent model gene expression programming generalised regression neural network

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Fatigue Life Prediction Model of FRP-Concrete Interface Based on gene expression programming

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MATERIALS 2024年第3期17卷 690页

作者： Zhang, Zhimei Huo, Yinglong Shanghai Univ Sch Mech & Engn Sci Dept Civil Engn Shanghai 200444 Peoples R China

Under fatigue loading, the interfacial fatigue life of fiber-reinforced polymer(FRP)-concrete is an important index for the analysis of the fatigue performance of reinforced concrete beams strengthened with FRP materials and the evaluation of the reinforcement effect. To solve the problems of the inconsistent and limited accuracy of existing fatigue life prediction models, gene expression programming (GEP) was used to study the interfacial fatigue life of FRP-concrete. Firstly, 219 sets of interfacial fatigue test data were collected, which included two kinds of reinforcement methods, namely, externally bonded (EB) reinforcement and near-surface-mounted (NSM) reinforcement;secondly, Pearson correlation analysis was used to determine the key factors affecting the fatigue life, and then GEP was used to explore the influence of different input forms on the prediction accuracy of the model. Fatigue life calculation formulas applicable to the two kinds of reinforcement methods, i.e., EB and NSM, were established, and a specific calculation formula was established. The model was subjected to parameter sensitivity analysis and variable importance analysis and was found to reflect the intrinsic relationship between the fatigue life and various factors. Finally, the GEP model was compared with the models proposed by other researchers. Five statistical indices, such as the coefficient of determination and the average absolute error, were selected to assess the model, and the results show that the GEP model has higher prediction accuracy than other models, with a coefficient of determination of 0.819, and indicators such as the average absolute error are also lower than those of the rest of the models.

关键词： gene expression programming FRP-concrete interface fatigue life prediction model

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