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

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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Numerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP)

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 4期38卷 3581-3597页

作者： Zhang, Jun Shi, Ruoli Shi, Shaohua Alzo'ubi, A. K. Roco-Videla, Angel Hussein, Mohamed M. A. Khan, Afrasyab Yunnan Univ Sch Architecture & Planning Kunming 650091 Yunnan Peoples R China Sichuan Univ Arts & Sci Sch Architectural Engn Dazhou 635000 Sichuan Peoples R China Abu Dhabi Univ Civil Engn Dept Abu Dhabi U Arab Emirates Univ Bernardo OHiggins Fac Ciencias Salud Programa Magister Ciencias Quim Biol Santiago Chile Univ Catolica Santisima Concepcion Fac Ingn Dept Ingn Civil Concepcion Chile Sohag Univ Fac Engn Civil Engn Dept Sohag Egypt South Ural State Univ Inst Engn & Technol Dept Hydraul & Hydraul & Pneumat Syst Lenin Prospect 76 Chelyabinsk 454080 Russia

Rectangular tunnel boring machine (TBM) is applied for the tunnels' excavation including a cross section of circular and rectangular shape within the various rocks and soil strata. Excessive structural forces, which is produced within tunnel linings, might affect the serviceability and safety of tunnels whose forces acting on tunnels linings during the initial design period have to be accurately calculated. Few numerical studies have been conducted on different soil-rectangular tunnel systems to clarify the critical response characteristics of cut-and-cover (rectangular) tunnels adjusted to transversal ground shaking. In this case, predicting the soil dynamic shear stresses developed around the tunnel is an elaborate task due to the interaction of TBM in the rectangular form and the rock. Despite doing the empirical studies in analyzing the rectangular tunnels systems, using artificial intelligence (AI) methods could significantly develop the optimization of TBM tunneling and decreasing the cost, error percentages, disturbance, and time-consuming complications related to tunneling. In this study, two algorithms, namely, support vector machine (SVM) and gene expression programming (GEP), were used to accurately predict the soil dynamic shear stresses developed around the tunnel. The models were developed and measured resulting that SVM can indicate a high-performance capacity in predicting the soil dynamic shear stresses developed around the tunnel through the rectangular TBM excavation machine.

关键词： Rectangular TBM Artificial intelligence Excavation Support vector machine gene expression programming

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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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An algorithm evaluation for discovering classification rules with gene expression programming

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第2期9卷 263-280页

作者： Guerrero-Enamorado, Alain Morell, Carlos Noaman, Amin Y. Ventura, Sebastian Univ Ciencias Informat Havana Cuba Univ Cent Las Villas Villa Clara Cuba King Abdulaziz Univ Jeddah 21413 Saudi Arabia Univ Cordoba UCO Cordoba Spain

In recent years, evolutionary algorithms have been used for classification tasks. However, only a limited number of comparisons exist between classification genetic rule-based systems and gene expression programming rule-based systems. In this paper, a new algorithm for classification using gene expression programming is proposed to accomplish this task, which was compared with several classical state-of-the-art rule-based classifiers. The proposed classifier uses a Michigan approach;the evolutionary process with elitism is guided by a token competition that improves the exploration of fitness surface. Individuals that cover instances, covered previously by others individuals, are penalized. The fitness function is constructed by the multiplying three factors: sensibility, specificity and simplicity. The classifier was constructed as a decision list, sorted by the positive predictive value. The most numerous class was used as the default class. Until now, only numerical attributes are allowed and a mono objective algorithm that combines the three fitness factors is implemented. Experiments with twenty benchmark data sets have shown that our approach is significantly better in validation accuracy than some genetic rule-based state-of-the-art algorithms (i.e., SLAVE, HIDER, Tan, Falco, Bojarczuk and CORE) and not significantly worse than other better algorithms (i.e., GASSIST, LOGIT-BOOST and UCS).

关键词： genetic programming gene expression programming Classification rules Discriminant functions

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Accurate prediction of water dewpoint temperature in natural gas dehydrators using gene expression programming approach

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JOURNAL OF MOLECULAR LIQUIDS 2017年 243卷 196-204页

作者： Rostami, Alireza Shokrollahi, Amin PUT Dept Petr Engn Ahvaz Iran Islamic Azad Univ North Tehran Branch Young Researchers & Elites Club Tehran Iran

Triethylene Glycol (TEG) is one of the most frequently applied liquid desiccants in natural gas dehydration. For this purpose, minimum concentration of TEG is necessary to achieve the outlet gas water dewpoint specification. Several empirical models have been developed to predict the equilibrium established between water vapor and TEG;however, the existing models requires numerous input parameters needing to longer and more complicated calculations. Thus, in present study, the most recent and powerful mathematical strategy known as, gene expression programming (GEP), was applied to develop an easy-to-use and a simple mathematical expression for prognostication equilibrium water dewpoint temperature as a function of contactor temperature and TEG purity in water. For absolute pressures up to 10,300 kpa (103 MPa), equilibrium water dewpoint temperature is nearly independent of operating pressure. For this, 167 datapoints were collected from the open literature, then the database was divided into the two sets of training (about 80% of the databank) and test (about 20% of the databank). Comprehensive error analysis and diverse graphical illustration were employed to show the performance of the developed empirically derived correlation in this study. The results of the GEP model was also compared with the previously published correlation available in literature, which leads to this consequence that the proposed model in this study is a more efficient approach with higher simplicity than the prementioned literature correlation. GEP-based model gives an acceptable agreement with experimental databank reported in the open literature in terms of determination coefficient (R-2) of 0.9820 and Root Mean Square Error (RMSE) of 3.5018. With respect to cumulative frequency analysis, it is understood that about 80% of the whole databank used in this study have estimation error equal or <0.2. The results of sensitivity analysis approved that the TEG purity is the most affecting varia

关键词： Natural gas dehydration Triethylene Glycol Equilibrium water dewpoint temperature gene expression programming Correlation Error analysis

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Prediction of hydroxyapatite crystallite size prepared by sol-gel route: gene expression programming approach

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JOURNAL OF SOL-GEL SCIENCE AND TECHNOLOGY 2018年第1期86卷 112-125页

作者： Jafari, Mehrdad Mahdavi Khayati, Gholam Reza Shahid Bahonar Univ Kerman Dept Mat Sci & Engn Kerman Iran

Excellent biocompatibility and its similar biological characteristics to bone apatite, extensively expand the hydroxyapatite (HA) usage in bioimplant applications. The crystallite size of HA is one of the most administrated parameter for determination of reaction rate at the interface of artificial/natural bones. This study tried to propose a new predictive model by employment of the gene expression programming (GEP), i.e., a powerful soft computing technique, to estimate the crystallite size of HA that were prepared by sol-gel route. Firstly, 37 different reliable experiments were carried out considering the type of phosphor precursor, pH, drying temperature, aging time, temperature and time of calcination as practical parameters as input variables, and HA crystallite size as output variable. Absolute fraction of variance (R (2)), mean absolute percentage error (MAPE), root relative squared error (RRSE), and mean squared error (MSE) were considered to validate the most appropriate GEP model/s. The experiment results were divided randomly into 29 training sets and 8 testing sets. Finally, the best model was selected in R (2) = 0.9929, MAPE = 2.8, RRSE = 0.0956, and MSE = 1.7. The results of simulation confirmed the unique features of GEP for the determination of HA crystallite size prepared by sol-gel route. [GRAPHICS] .

关键词： Hydroxyapatite Crystallite size Sol-gel Process parameters Modeling gene expression programming

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QSAR model for prediction capacity factor of molecular imprinting polymer based on gene expression programming

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QSAR & COMBINATORIAL SCIENCE 2007年第1期26卷 41-50页

作者： Si, H. Z. Zhang, K. J. Hu, Z. D. Fan, B. T. Lanzhou Univ Dept Chem Lanzhou 730000 Peoples R China Ctr Dis Control Gansu Province Lanzhou 730020 Peoples R China JUST Sch Mech & Elect Engn Ganzhou 341000 Peoples R China Univ Paris 07 ITODYS F-75005 Paris France

The gene expression programming (GEP), as a novel type of learning machine, for the first time, has been in this study used to develop a quantitative structure-activity relationship model of 39 compounds of molecular imprinting polymer based on calculated chemical parameters. The comparison with heuristic method and support vector machines approaches reveals a good prediction of GEP.

关键词： capacity factor gene expression programming heuristic method molecular imprinting QSAR support vector machine

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Estimation of local scour depth around twin piers using gene expression programming (local scour around twin piers)

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WATER SUPPLY 2022年第6期22卷 5915-5932页

作者： Devi, Geeta Kumar, Munendra Delhi Technol Univ New Delhi 110042 India

The scour around the bridge piers has been estimated using conventional empirical formulae;however, these formulas are unable to predict the scour depth precisely. The present study is conducted in two parts (a) experimental investigation for evaluating the behaviour of local scour around twin piers positioned in the transverse direction of flow and (b) an empirical equation to estimating scour depth is proposed utilising new evolutionary Artificial Intelligence technique gene expression programming (GEP). Experimental results of the present study demonstrate the influence of the rate of flow and clear spacing between the piers on the scour depth. Additionally the results of the soft computing technique GEP during testing and training of proposed modelling, fitness function Root Mean Square Error is observed as 0.00133 and 0.00113, with the coefficient of determination as 0.950 and 0.955, respectively. Furthermore, in order to find out the role of each variable for scour depth sensitivity analysis has been conducted. The findings of the sensitivity analysis show that the pier spacing and rate of flow play the most significant role in scour depth estimation. Results of this study demonstrate good agreement with the proposed GEP model and conclude that it is a better approach for forecasting scour depth.

关键词： bridge piers bridge scour gene expression programming maximum scour depth sensitivity analysis

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Modelling reference evapotranspiration using gene expression programming and artificial neural network at Pantnagar,India

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Information Processing in Agriculture 2023年第4期10卷 547-563页

作者： Pangam Heramb Pramod Kumar Singh K.V.Ramana Rao A.Subeesh Division of Irrigation and Drainage Engineering ICAR-Central Institute of Agricultural EngineeringBhopal 462038India Department of Irrigation and Drainage Engineering G.B.Pant University of Agriculture and TechnologyPantnagar 263145India Division of Agricultural Mechanization ICAR-Central Institute of Agricultural EngineeringBhopal 462038India

Evapotranspiration is an essential component of the hydrological cycle that is of particular interest for water resource *** quantification is helpful in irrigation scheduling,water balance studies,water allocation,*** of reference evapotranspiration(ET0)using both gene expression programming(GEP)and artificial neural network(ANN)techniques was done using the daily meteorological data of the Pantnagar region,India,from 2010 to 2019.A total of 15 combinations of inputs were used in developing the ET0 *** model with the least number of inputs consisted of maximum and minimum air temperatures,whereas the model with the highest number of inputs consisted of maximum air temperature,minimum air temperature,mean relative humidity,number of sunshine hours,wind speed at 2mheight and extra-terrestrial radiation as inputs and with ET0 as the output for all the *** the GEP models were developed for a single functional set and pre-defined genetic operator values,while the best structure in each ANN model was found based on the performance during the testing *** was found that ANN models were superior to GEP models for the estimation *** was evident from the reduction in RMSE values ranging from 2%to 56%during training and testing phases in all the ANN models compared with GEP *** ANN models showed an increase of about 0.96%to 9.72%of R2 value compared to the respective GEP *** comparative study of these models with multiple linear regression(MLR)depicted that the ANN and GEP models were superior to MLR models.

关键词： Artificial Neural Networks Evolutionary algorithms gene expression programming Machine Learning Regression Analysis Reference evapotranspiration models

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Restricted gene expression programming: a new approach for parameter identification inverse problems of partial differential equation

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SOFT COMPUTING 2017年第10期21卷 2651-2663页

作者： Chen, Yan Li, Kangshun Chen, Zhangxing Wang, Jinfeng South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Univ Calgary Dept Chem & Petr Engn Calgary AB Canada

Traditionally, solving the parameter identification inverse problems of partial differential equation encountered many difficulties and insufficiency. In this article, we proposed a restricted gene expression programming (GEP) for parameter identification inverse problems of partial differential equation based on the self-adaption, self-organization, and self-learning characters of GEP. The algorithm simulates parametric function itself of partial differential equation directly through the observed values by taking effect to inverse results caused by noise of the measured value into full consideration. Modeling is unnecessary to add regularization in modeling process aiming at special problems again. The experiment results also showed that the algorithm has good noise-immunity. In case there is no noise or noise is very low, the identified parametric function is almost the same as the original accurate value;when noise is very high, accurate result can still be obtained, which successfully realizes automation of parameter modeling process for partial differential equation.

关键词： gene expression programming Partial differential equation Inverse problems Thomas algorithm

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