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Prediction of the depth of local scouring at a bridge pier using a gene expression programming method

SN APPLIED SCIENCES 2021年第2期3卷 159页

作者： Hassan, Waqed H. Jalal, Halah K. Univ Kerbala Coll Engn Civil Engn Dept Kerbala Iraq

Local scouring around the piers of a bridge is the one of the major reasons for bridge failure, potentially resulting in heavy losses in terms of both the economy and human life. Prediction of accurate depth of local scouring is a difficult task due to the many factors that contribute to this process, however. The main aim of this study is thus to offer a new formula for the prediction the local depth of scouring around the pier of a bridge using a modern fine computing modelling technique known as gene expression programming (GEP), with data obtained from numerical simulations used to compare GEP performance with that of a standard non-linear regression (NLR) model. The best technique for prediction of the local scouring depth is then determined based on three statistical parameters: the determination coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE). A total data set of 243 measurements, obtained by numerical simulation in Flow-3D, for intensity of flow, ratio of pier width, ratio of flow depth, pier Froude number, and pier shape factor is divided into training and validation (testing) datasets to achieve this. The results suggest that the formula from the GEP model provides better performance for predicting the local depth of scouring as compared with conventional regression with the NLR model, with R-2 = 0.901, MAE = 0.111, and RMSE = 0.142. The sensitivity analysis results further suggest that the ratio of the depth of flow has the greatest impact on the prediction of local scour depth as compared to the other input parameters. The formula obtained from the GEP model gives the best predictor of depth of scouring, and, in addition, GEP offers the special feature of providing both explicit and compressed arithmetical terms to allow calculation of such depth of scouring.

关键词： Local scouring Scour depth prediction gene expression programming Non-linear regression

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The trading on the mutual funds by gene expression programming with Sortino ratio

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APPLIED SOFT COMPUTING 2014年第0期15卷 219-230页

作者： Chen, Hung-Hsin Yang, Chang-Biau Peng, Yung-Hsing Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan Inst Informat Ind Innovat DigiTech Enabled Applicat & Serv Inst Kaohsiung Taiwan

The aim of this paper is to combine several techniques together to provide one systematic method for guiding the investment in mutual funds. Many researches focus on the prediction of a single asset time series, or focus on portfolio management to diversify the investment risk, but they do not generate explicit trading rules. Only a few researches combine these two concepts together, but they adjust trading rules manually. Our method combines the techniques for generating observable and profitable trading rules, managing portfolio and allocating capital. First, the buying timing and selling timing are decided by the trading rules generated by gene expression programming. The trading rules are suitable for the constantly changing market. Second, the funds with higher Sortino ratios are selected into the portfolio. Third, there are two models for capital allocation, one allocates the capital equally (EQ) and the other allocates the capital with the mean variance (MV) model. Also, we perform superior predictive ability test to ensure that our method can earn positive returns without data snooping. To evaluate the return performance of our method, we simulate the investment on mutual funds from January 1999 to September 2012. The training duration is from 1999/1/1 to 2003/12/31, while the testing duration is from 2004/1/1 to 2012/9/11. The best annualized return of our method with EQ and MV capital allocation models are 12.08% and 12.85%, respectively. The latter also lowers the investment risk. To compare with the method proposed by Tsai et al., we also perform testing from January 2004 to December 2008. The experimental results show that our method can earn annualized return 9.07% and 11.27%, which are better than the annualized return 6.89% of Tsai et al. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Mutual fund gene expression programming Sortino ratio Mean variance model Superior predictive ability test

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Prediction model for concrete carbonation depth using gene expression programming

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COMPUTERS AND CONCRETE 2020年第6期26卷 497-504页

作者： Murad, Yasmin Z. Tarawneh, Bashar K. Ashteyat, Ahmed M. Univ Jordan Dept Civil Engn Amman 11942 Jordan

Concrete can lose its alkalinity by concrete carbonation causing steel corrosion. Thus, the determination of the carbonation depth is necessary. An empirical model is proposed in this research to predict the carbonation depth of concrete using gene expression programming (GEP). The GEP model was trained and validated using a large and reliable database collected from the literature. The model was developed using the six parameters that predominantly control the carbonation depth of concrete including carbon dioxide CO2 concentration, relative humidity, water-to-cement ratio, maximum aggregate size, aggregate to binder ratio and carbonation period. The model was statistically evaluated and then compared to the Jiang et al. model. A parametric study was finally performed to check the proposed GEP model's sensitivity to the selected input parameters.

关键词： carbonation depth of concrete gene expression programming Jiang model

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Information Fusion in Offspring generation: A Case Study in gene expression programming

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IEEE ACCESS 2020年 8卷 74782-74792页

作者： Liu, Tonglin Zhang, Hengzhe Zhang, Hu Zhou, Aimin Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent Beijing 100074 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai 200062 Peoples R China

gene expression programming (GEP), which is a variant of genetic programming (GP) with a fixed-length linear model, has been applied in many domains. Typically, GEP uses genetic operators to generate offspring. In recent years, the estimation of distribution algorithm (EDA) has also been proven to be efficient for offspring generation. genetic operators such as crossover and mutation generate offspring from an implicit model by using the individual information. By contrast, EDA operators generate offspring from an explicit model by using the population distribution information. Since both the individual and population distribution information are useful in offspring generation, it is natural to hybrid EDA and genetic operators to improve the search efficiency. To this end, we propose a hybrid offspring generation strategy for GEP by using a univariate categorical distribution based EDA operator and its original genetic operators. To evaluate the performance of the new hybrid algorithm, we apply the algorithm to ten regression tasks using various parameters and strategies. The experimental results demonstrate that the new algorithm is a promising approach for solving regression problems efficiently. The GEP with hybrid operators outperforms the original GEP that uses genetic operators on eight out of ten benchmark datasets.

关键词： gene expression Sociology Statistics Probabilistic logic genetic algorithms programming genetic programming gene expression programming estimation of distribution algorithm offspring generation

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Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming

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ROAD MATERIALS AND PAVEMENT DESIGN 2023年第9期24卷 2192-2208页

作者： Leon, Lee P. Azamathulla, Hazi Felix, Portia Prasad, C. Venkata Siva Rama Univ West Indies Dept Civil & Environm Engn St Augustine Trinidad Tobago Vignana Bharathi Inst Technol Dept Civil Engn Hyderabad India

An essential factor in the design and in situ performance of asphalt concrete pavements is its elastic-stiffness properties. The objective of the present study, is to develop empirical equations to estimate indirect tensile stiffness modulus (ITSM) of bituminous mixture using the soft computing techniques of multi expression programming (MEP) and gene expression programming (GEP). The soft computing models were developed using experimental data points containing laboratory and field blended dense-graded hot mix asphalt (HMA) and gap-graded stone matrix asphalt (SMA) mixtures including three asphalt binders. The dominant parameters covered mix physical and volumetric properties, test temperatures, and the mix variables needed during quality control and assurance assessments. The soft computing models could successfully estimate the stiffness modulus with notable values of R values between 0.835 and 0.944. The GEP equations estimated the elastic stiffness modulus with a higher rate of accuracy when compared to the MEP, non-linear and linear regression estimates.

关键词： Asphalt concrete gene expression programming HMA indirect tensile stiffness modulus multi-expression programming SMA

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Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming

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ENERGY 2017年 126卷 144-164页

作者： Kaboli, S. Hr Aghay Fallahpour, A. Selvaraj, J. Rahim, N. A. UMPEDAC Wisma R&D Univ Malaya UM Level 4JalanPantai Baharu Kuala Lumpur 59990 Malaysia King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia

This study formulates the effects of two different historical data types on electrical energy consumption of ASEAN-5 counties. On this basis, optimized GEP (gene expression programming) is applied to precisely formulate the relationships between historical data and electricity consumption. The optimized GEP is a more recent extension of GEP with high probability of finding closed-form solution in mathematical modeling without prior knowledge about the nature of the relationships between variables. This merit is provided by balancing the exploration of solution structure and exploitation of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP. To assess the applicability and accuracy of the proposed method, its estimates are compared with those obtained from ANN (artificial neural network), SVR (support vector regression), ANFIS (adaptive neuro-fuzzy inference system), rule-based data mining algorithm, GEP, linear and quadratic models optimized by PSO (particle swarm optimization), CSA (cuckoo search algorithm) and BSA (backtracking search algorithm). The simulation results are validated by actual data sets observed from 1971 until 2011. The results confirm the higher accuracy of the proposed method as compared with other artificial intelligence based models. Future estimations of electrical energy consumption in ASEAN-5 countries are projected up to 2030 according to rolling-based forecasting procedure. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Electrical energy consumption Forecasting gene expression programming Optimization

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Parallel hybrid evolutionary computation: Automatic tuning of parameters for parallel gene expression programming

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APPLIED MATHEMATICS AND COMPUTATION 2008年第1-2期201卷 108-120页

作者： Park, Ho-Hyun Grings, Alexandre dos Santos, Marcus Vinicius Soares, Alexsandro Santos Ryerson Univ Dept Comp Sci Toronto ON M5B 2K3 Canada Univ Fed Goias BR-75705220 Gatalao Go Brazil Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea Univ Fed Uberlandia Fac Elect Engn BR-38400902 Uberlandia MG Brazil

A parallel hybrid framework that combines gene expression programming (GEP) as the evolutionary problem-solving methodology and alternative meta-heuristics for tuning parameter values of the parallel GEP runs is presented. The implementation of this framework is based on a client-server architecture which includes clients that use GEP to evolve candidate solutions for the problem in question, and clients that use (possibly) other meta-heuristics to tune GEP input parameters. In the implementation of this framework, a genetic algorithms methodology is used for parameter tuning. For testing the framework and its implementation, a suite of symbolic regression problems of different complexities is used. Our experimental results show that our approach provides a solution for the problem of automatically tuning two GEP input parameters, viz., the number of genes and the length of each gene. (C) 2007 Elsevier Inc. All rights reserved.

关键词： evolutionary computation gene expression programming parallel architectures optimization techniques mathematical modeling

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Selection of Most Relevant Input Parameters Using Waikato Environment for Knowledge Analysis for gene expression programming Based Power Transformer Fault Diagnosis

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2014年第16期42卷 1849-1861页

作者： Malik, Hasmat Mishra, Sukumar Mittal, Alok Prakash Univ Delhi Netaji Subhas Inst Technol Instrumentat & Control Engn Dept New Delhi India Indian Inst Technol Dept Elect Engn New Delhi 110016 India

The diagnosis of incipient fault is important for power transformer condition monitoring. Incipient faults are monitored by conventional and artificial intelligence based models. Key gases, percentage value of gases, and ratio of the Doernenburg, Roger, IEC methods are input variables to artificial intelligence models, which affects the accuracy of incipient fault diagnosis, so selection of the most influencing relevant input variable is an important research area. With this main objective, Waikato Environment for Knowledge Analysis software is applied to 360 simulated samples having different operating lives to find the most influencing input parameters for incipient fault diagnosis in the gene expression programming model. The Waikato Environment for Knowledge Analysis identifies%C2H2,%C2H4,%CH4, C2H6/C2H2, C2H2/C2H4, CH4/H-2, C2H4/C2H6, and C2H2/CH4 as the most relevant input variables in incipient fault diagnosis, and it is used for fault diagnosis using different artificial intelligence methods, i.e., artificial neural networks, fuzzy logic, support vector machines, and gene expression programming. The compared results shows that gene expression programming gives better results than the artificial neural network, fuzzy logic, and support vector machine with accuracy variation from 98.15 to 100%, proving the gene expression programming method can be used in transformer fault diagnosis research.

关键词： gene expression programming Waikato environment power transformer knowledge analysis fault diagnosis dissolved gas analysis

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Improved gene expression programming to solve the inverse problem for ordinary differential equations

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 38卷 231-239页

作者： Li, Kangshun Chen, Yan Li, Wei He, Jun Xue, Yu South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Aberystwyth Univ Dept Comp Sci Aberystwyth Ceredigion England Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China

Many complex systems in the real world evolve with time. These dynamic systems are often modeled by ordinary differential equations in mathematics. The inverse problem of ordinary differential equations is to convert the observed data of a physical system into a mathematical model in terms of ordinary differential equations. Then the model may be used to predict the future behavior of the physical system being modeled. genetic programming has been taken as a solver of this inverse problem. Similar to genetic programming, gene expression programming could do the same job since it has a similar ability of establishing the model of ordinary differential systems. Nevertheless, such research is seldom studied before. This paper is one of the first attempts to apply gene expression programming for solving the inverse problem of ordinary differential equations. Based on a statistic observation of traditional gene expression programming, an improvement is made in our algorithm, that is, genetic operators should act more often on the dominant part of genes than on the recessive part. This may help maintain population diversity and also speed up the convergence of the algorithm. Experiments show that this improved algorithm performs much better than genetic programming and traditional gene expression programming in terms of running time and prediction precision.

关键词： gene expression programming System of ordinary differential equations Inverse problem Runge-Kutta algorithm

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Development of predictive models of asphalt pavement distresses in Idaho through gene expression programming

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NEURAL COMPUTING & APPLICATIONS 2022年第17期34卷 14913-14927页

作者： Deng, Yong Shi, Xianming Washington State Univ Dept Civil & Environm Engn Natl Ctr Transportat Infrastruct Durabil & Life E Pullman WA 99164 USA

Good predictive models should feature the capability of obtaining accurate results through easy and accessible methods in a timely manner. Following a "golden triangle" connecting the accuracy, complexity and applicability of each model, this work employed gene expression programming (GEP) algorithm to develop models for four typical distresses of asphalt pavements in the state of Idaho, respectively. The models developed in this work retain and combine advantages of empirical models and machine learning models and thus serve as promising alternatives to mechanistic-empirical (ME) models. Models with explicit forms were selected and calibrated by GEP, in an intelligent and automatic way. This work also proposes a variable selection method featuring principal component analysis coupled with correlation analysis, and this method was utilized in data pre-processing for construction of simple models with precise model form. Furthermore, this work introduces a systematic and comprehensive approach to evaluation of model accuracy, robustness and sensitivity, based on various statistical methods. Compared with regression models and ME models, the models generated by GEP achieved higher and more stable accuracy. Finally, we present the trade-off between model accuracy and rationality and point out the importance and necessity of reaching a balance in the golden triangle.

关键词： Asphalt pavement gene expression programming Golden triangle Accuracy Sensitivity Statistical method

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