Gene Expression Programming Technique Research Articles

Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques.

Deposition of sediment is a vital economical and technical problem for design of sewers, urban drainage, irrigation channels and, in general, rigid boundary channels. In order to confine continuous sediment deposition, rigid boundary channels are designed based on self-cleansing criteria. Recently, instead of using a single velocity value for design of the self-cleansing channels, more hydraulic parameters such as sediment, fluid, flow and channel characteristics are being utilized. In this study, two techniques of neuro-fuzzy (NF) and gene expression programming (GEP) are implemented for particle Froude number (Frp) estimation of the non-deposition condition of sediment transport in rigid boundary channels. The models are established based on laboratory experimental data with wide ranges of sediment and pipe sizes. The developed models' performances have been compared with empirical equations based on two statistical factors comprising the root mean square error (RMSE) and the concordance coefficient (CC). Besides, Taylor diagrams are used to test the resemblance between measured and calculated values. The outcomes disclose that NF4, as the precise NF model, performs better than the best GEP model (GEP1) and regression equations. As a conclusion, the obtained results proved the suitable accuracy and applicability of the NF method in Frp estimation.

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

ABSTRACT 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°C to 200°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.

Gene expression programming for evaluation of aggregate angularity effects on permanent deformation of asphalt mixtures

Abstract The shape of the aggregate influences the particle interlocking capabilities, thus influencing the deformation behaviour of mixes. The research paper explores the use of gene-expression programming (GEP) as an unconventional approach to predict permanent deformation of asphalt concrete with the inclusion of aggregate angularity. Data for prediction of permanent deformation of dense graded asphalt concrete was obtained through laboratory experiments. The development and analysis of the proposed GEP technique was completed by using laboratory data. The new technique for the assessment of permanent deformation strain with additional angularity factor was developed. The GEP approach concluded satisfactory outcomes of the estimation of permanent deformation when compared to regression analysis.

Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations

PurposeThis study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (qult) of rock foundations, which is a considerable civil and geotechnical engineering problem.Design/methodology/approachThe input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of qult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.FindingsA new and explicit formula generated by GEP is proposed for the estimation of the qult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.Originality/valueThe superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

Determining the Scour Dimensions Around Submerged Vanes in a 180° Bend with the Gene Expression Programming Technique

Submerged vanes are installed on rivers and channel beds to protect the outer bank bends from scouring. Also, local scouring occurs around the submerged vanes over time, and identifying the effective factors on the scouring phenomena around these submerged vanes is one of the important issues in river engineering. The most important aim of this study is investigation of scour pattern around submerged vanes located in 180° bend experimentally and numerically. Firstly, the effects of various parameters such as the Froude number (Fr), angle of submerged vanes to the flow (α), angle of submerged vane location in the bend (θ), distance between submerged vanes (d), height (H), and length (L) of the vanes on the dimensionless volume of the scour hole were experimentally studied. The submerged vanes were installed on a 180° bend whose central radius and channel width were 2.8 and 0.6 m, respectively. By reducing the Froude number, the scour hole volume decreased. For all Froude numbers, the biggest scour hole formed at θ = 15°. In all models, by increasing the Froude number, the scour hole volume significantly increases. In addition, by increasing the submerged vanes’ length and height, the scour hole dimensions also grow. Secondly, using gene expression programming (GEP), a relationship for determining the scour hole volume around the submerged vanes was provided. For this model, the determination coefficients (R2) for the training and test modes were computed as 0.91 and 0.9, respectively. In addition, this study performed partial derivative sensitivity analysis (PDSA). According to the results, the PDSA was calculated as positive for all input variables.

Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques

The geometric dimensions and bank profile shape of channels with boundaries containing particles on the verge of motion (threshold channels) are significant factors in channel design. In this study, extensive experimental work was done at different flow velocities to propose a reliable method capable of estimating stable channel bank profile. The proposed method is based on gene expression programming (GEP). Laboratorial datasets obtained from Mikhailova et al. (Hydro Tech Constr 14:714–722, 1980), Ikeda (J Hydraul Div ASCE 107:389–406 1981), Diplas (J Hydraul Eng ASCE 116:707–728, 1990) and Hassanzadeh et al. (J Civil Environ Eng 43(4):59–68, 2014) were used to train, test, validate and examine the GEP model in various geometric and hydraulic conditions. The obtained results demonstrate that the proposed model can estimate bank profile characteristics with great accuracy (determination coefficient of 0.973 and mean absolute relative error of 0.147). Moreover, for practical calculations of channel dimensions, the model provides a specific mathematical relationship to solve problems with different discharge rates (Q) and particles with various median grain sizes (D50). The proposed model’s performance is compared with 8 relationships suggested previously by researchers (based on empirical and theoretical analyses) and a relationship obtained using a nonlinear regression model with different experimental data. The polynomial VDM and two exponential functions, i.e. IKM and DIM, are introduced as the superior existing models. According to the present study results, the proposed GEP model can predict the bank profile shape trend well and similar to the experimental datasets. Sensitivity analysis was also applied to assess the impact of each input variable (x, Q and D50) on the presented relationship. According to the current study, the GEP model provides a suitable equation for predicting the bank profile shape of stable channels.

Using gene expression programming in monthly reference evapotranspiration modeling: A case study in Egypt

The Penman-Monteith FAO-56 equation requires the complete climatic records for estimating reference evapotranspiration (ETo). The present study is aimed at developing and evaluating a gene expression programming (GEP) model for estimating mean monthly ETo by using minimal amount of climatic data. The data used in the analysis are collected from 32 weather stations in Egypt through the CLIMWAT database. The results showed that the accuracy of the GEP model significantly improved when either mean relative humidity (RH) or wind speed at 2-m height (u2) was used as additional input variables. The GEP model with the inputs as maximum and minimum air temperature, RH, and u2 showed the lowest root mean square error (0.426 mm d−1 and 0.430 mm d−1) and, the highest coefficient of determination, (0.963 and 0.962) overall index of model performance (0.960 and 0.960), and index of agreement (0.991 and 0.990) for training and testing sets, respectively. Comparing the results of GEP models with other empirical models showed that the GEP technique are more accurate and can be employed successfully in modelling ETo.

Multi-objective optimization of the performance-emission trade-off characteristics of a CRDI coupled CNG diesel dual-fuel operation: A GEP meta-model assisted MOGA endeavour

A meta-model based multi-objective optimization endeavor was undertaken in the present work to investigate the potential of the off-line model based calibration technique to extend the actual CRDI-CNG dual-fuel experimental investigations in determining the possibility of unearthing viable potential trade-off domains hitherto unexplored by the constraints of resources, cost and time warranted by an experimental investigation. For the ensuing optimization study, CNG energy share, fuel injection pressure and load have been used as the decision variables while PM, NHC and BSFC were chosen as the output variables to be optimized. In absence of a closed form correlation between the participating variables under study, the explicit characterization capability of the Gene Expression Programming technique was harnessed. The appropriate GEP based meta-models were adopted from a previous study correlating the identical system output responses for the same set of decision variables of interest in the present study. Genetic algorithm was chosen as the optimization routine in the present study in view of its promising potential of extremely fast convergent speed, diversity of optimal solutions and simplicity of operation. Experimental validation of the obtained solutions pertaining to the desired objectives were carried out by actual experimentation. The present optimization endeavor was able to better the best vantage in category of the desired objective of minimum fuel consumption and exhaust emissions, obtained not only as compared to baseline diesel operation comprehensively but also was superior than the actual CRDI-CNG strategy during actual dual-fuel operation corresponding to actual experimentation.

Numerical study on lateral buckling of pipelines with imperfection and sleeper

Lateral buckling is an important issue in unburied high-temperature and high-pressure (HT/HP) subsea pipelines systems. The imperfection–sleeper method is one of the most well-known methods used to control lateral buckling of HT/HP pipelines. Pipelines–sleeper–seabed numerical models are established and verified to analyze the buckling behavior of pipelines using the imperfection–sleeper method. The influence of six main factors on lateral buckling behavior is investigated in details based on the numerical results. Equations of buckling displacement (buckling displacement is defined by the final displacement of the middle point of the pipelines), critical buckling force, and buckling stress (Mises stress) are proposed using the gene expression programming technique. These equations show good accuracy and can be used to assist in the design of sleepers and assess the compressive and stress levels of pipelines.

Prediction of local scour around complex piers using GEP and M5-Tree

Local scour around piers is one of the main causes of bridge failures. In this study, three robust techniques, artificial neural networks (ANNs), M5-Tree, and Gene Expression Programming (GEP), were employed for prediction of scour depth around complex piers. The clear water condition was chosen for all experimental tests. The results indicated that pier diameter (b c) and foundation level (Y) are the main parameters for local scour. Furthermore, the minimum scour depth occurs in range of Y/b c = 1.1~1.3. In next step, to evaluate the mentioned techniques, a wide range of dataset was collected from the present study and literature. The radial base function (RBF) with R 2 = 0.945 and RMSE = 0.031 provides better prediction in comparison with conventional equations, M5-Tree (R 2 = 0.883, RMSE = 0.292) and the GEP techniques (R 2 = 0.811 and RMSE = 0.263). The equations developed by M5-Tree and GEP are more useful for practical purposes and can be easily employed to predict the depth of scour at complex piers.

Impurity effect on clear water evaporation: toward modelling wastewater evaporation using ANN, ANFIS-SC and GEP techniques

ABSTRACTThe growing shortage of freshwater resources and increasing environmental awareness give rise to the use of treated wastewater as an alternative resource for water supply. Accurate estimation of wastewater evaporation (WWE), as the main cause of water losses, is necessary for proper water resources management. Unfortunately, few studies have focused on modelling WWE despite its vital importance. This study investigates the ability of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN) techniques to estimate WWE as a function of variables including wastewater properties, clear water evaporation and climatic factors. The study uses measured data from an experiment conducted in Neishaboor municipal wastewater treatment plant, Iran. Results indicate that the ANN model is superior among the three methods, and also demonstrates higher accuracy when compared with those of a dimensional analysis model using the F-test statistic.

ESTIMATION OF MONTHLY MEAN REFERENCE EVAPOTRANSPIRATION USING GENE EXPRESSION PROGRAMMING

Evapotranspiration is a main component of the water cycle and is important in crop growth. Monthly mean reference evapotranspiration (ETo) is estimated using gene expression programming (GEP) in Basrah City, south of Iraq. Various climatic data, such as air temperature, relative humidity, and wind speed are used as inputs of GEP model to estimate the values of reference evapotranspiration (ETo) given by the FAO-56 (Penman-Monteith equation). Nine input combinations tested with GEP are coded as model No. (1-9). Root relative squared error (RRSE) is taken as fitness function in each of GEP models. GEP models with three climatic input variables (temperature, relative humidity, and wind speed) take the highest level in the performance. The GEP technique was successfully employed to estimate ETo in the study area. The explicit formulas obtained can be used as powerful models for estimating the mean monthly ETo in the irrigation practices with limited climatic data.

Modelling daily reference evapotranspiration in humid locations of South Korea using local and cross‐station data management scenarios

ABSTRACTModelling reference evapotranspiration (ET0) is a key issue in hydrology, water resources management and irrigation scheduling. This article aimed at evaluating gene expression programming (GEP) and support vector machine (SVM) techniques for modelling ET0 in humid regions of South Korea. The GEP and SVM results were also compared with the empirical ET0 models with the same input parameters. Daily meteorological data from eight weather stations comprising a period of 10 years were utilized to establish the models. This study evaluated two different data management scenarios of simulations. At the first scenario, the heuristic and empirical models were developed and tested at each location, separately, which confirmed the superiority of heuristic models to empirical models. At the second scenario, two cross‐station approaches were developed: modelling ET0 using data from ancillary stations and developing generalized heuristic models for the studied stations. The obtained results revealed that both the GEP and SVM techniques could simulate ET0 in both the scenarios, successfully. Meanwhile, GEP outperformed SVM in cross‐station scenario.

Local vs. cross station simulation of suspended sediment load in successive hydrometric stations: heuristic modeling approach

The present paper aims at modeling suspended sediment load (SSL) using heuristic data driven methodologies, e.g. Gene Expression Programming (GEP) and Support Vector Machine (SVM) in three successive hydrometric stations of Housatonic River in U.S. The simulations were carried out through local and cross-station data management scenarios to investigate the interrelations between the SSL values of upstream/downstream stations. The available scenarios were applied to predict SSL values using GEP to obtain the best models. Then, the best models were predicted by SVM approach and the obtained results were compared with those of GEP. The comparison of the results revealed that the SVM technique is more capable than the GEP for modeling the SSL through the both local and cross-station data management strategies. Besides, local application seems to be better than cross-station application for modeling SSL. Nevertheless, the cross-station application demonstrated to be a valid methodology for simulating SSL, which would be of interest for the stations with lack of observational data. Also, the prediction capability of conventional Sediment Rating Curve (SRC) method was compared with those of GEP and SVM techniques. The obtained results revealed the superiority of GEP and SVM-based models over the traditional SRC technique in the studied stations.

Modeling scour depth downstream of grade-control structures using data driven and empirical approaches

Local scour occurs in the immediate vicinity of structures as a result of impinging on a bed with a high velocity flow. Prediction of scour depth has an important role in control structure management and water resource engineering issues, so a study of new heuristic expressions governing it is necessary. The present study aims to investigate different methods' capabilities to estimate scour depth downstream of grade-control structures using field measurements from the literature. Accordingly, data driven feed forward neural network and gene expression programming techniques were selected for the investigation. Additionally, the optimum data driven based scour depth models were compared with the corresponding physical–empirical based formulas. Three data categories corresponding to (a) scouring downstream of a ski-jump bucket, (b) a sharp-crested weir, and (c) an inclined slope controlled structure (as grade-control structures) were applied as reference patterns for developing and validating the applied models. A sensitivity analysis was also performed to identify the most influential parameters on scouring. The obtained results indicated that the applied methods have promising performance in estimating the scour depth downstream of spillways and control structures. Nevertheless, the applied data driven approaches show higher accuracy than the corresponding traditional formulas.

A New Approach for Analyzing the Performance of the WiMAX Networks based on QoS Traffic Prediction Routing Protocol using Gene Expression Programming

WiMAX is one of the broadband wireless access technologies, which provides the efficient QoS to the large number of users. The multimedia applications such as real time and non-real time services are gaining importance in the WiMAX network. To support such applications, there is a need to propose an efficient QoS traffic prediction routing protocol for the WiMAX networks. To address this, the authors are using Gene Expression Programming technique. They have generated datasets for CBR based traffic and file transfer applications. Here, they focus to develop the mathematical expressions for throughput of the network in terms of bandwidth, average end-to-end delay and average jitter for CBR based traffic and file transfer applications, so that they can analyze and predict the QoS traffic of the network. The simulation results show that the model values and the target values match with better approximation. Further, sensitivity analysis has been carried out for both CBR based traffic and file transfer applications.

Optimizing hadoop parameter settings with gene expression programming guided PSO

SummaryHadoop MapReduce has become a major computing technology in support of big data analytics. The Hadoop framework has over 190 configuration parameters, and some of them can have a significant effect on the performance of a Hadoop job. Manually tuning the optimum or near optimum values of these parameters is a challenging task and also a time consuming process. This paper optimizes the performance of Hadoop by automatically tuning its configuration parameter settings. The proposed work first employs gene expression programming technique to build an objective function based on historical job running records, which represents a correlation among the Hadoop configuration parameters. It then employs particle swarm optimization technique, which makes use of the objective function to search for optimal or near optimal parameter settings. Experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings. Moreover, it outperforms both rule‐of‐thumb settings and the Starfish model in Hadoop performance optimization. © 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd.

Development of predictive model for flood routing using genetic expression programming

AbstractFlood disasters continue to occur in many countries around the world and cause tremendous casualties and property damage. Flood peak values are required in the design of bridges, culvert waterways, spillways for dams, and estimation of scour at a hydraulic structure. When a flood wave passes through a reservoir its peak is attenuated, and the time base is enlarged due to effect of storage. Modification in the hydrograph is studied through flood routing. Flood routing is the technique of determining the flood hydrograph at a section of a river by utilising the data of flood flow at one or more upstream sections. Advances in the field of artificial intelligence (AI) offer opportunities for utilising new algorithms and models. Experiments have shown that the gene‐expression programming (GEP) model and its algorithms are 100–60 000 times faster than older genetic algorithms. This study presents the GEP method, which is an extension to genetic programming, as an alternative approach to modelling of flood routing in natural channels. Thus, novel models for prediction of flood routing using the GEP technique are developed. The proposed GEP‐based approach is compared with three hydrograph examples and the results of other solution techniques. The GEP method makes use of few hydrologic parameters (inflow, outflow, and time). The performance of the models is evaluated by two goodness‐of‐fit measures, namely, the root‐mean‐square error (RMSE) and the coefficient of determination (R2). In the results, the GEP models show superior performance to the other solution techniques based on the Muskingum model.

Oil price forecasting using gene expression programming and artificial neural networks

This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models. The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices. Further, the GEP model outperforms the NN and the ARIMA models in terms of the mean squared error, the root mean squared error and the mean absolute error. Finally, the GEP model also has the highest explanatory power as measured by the R-squared statistic. The results of this study have important implications for both theory and practice.

Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate

Artificial neural networks (ANNs) and gene expression programming (GEP) were compared to estimate daily reference evapotranspiration (ETref) under arid conditions. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The ANN and GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman–Monteith (PMG) model was used as a reference target for evapotranspiration values, with hc varies from 5 to 105cm with increment of a centimetre. The developed models were spatially validated using climatic data from 1980 to 2010 taken from another six meteorological stations. The results showed that the eight ETref models developed using the ANN technique were slightly more accurate than those developed using the GEP technique. The ANN models’ determination coefficients (R2) ranged from 67.6% to 99.8% and root mean square error (RMSE) values ranged from 0.20 to 2.95mmd-1. The GEP models’ R2 values ranged from 64.4% to 95.5% and RMSE values ranged from 1.13 to 3.1mmd-1. Although the GEP models performed slightly worse than the ANN models, the GEP models used explicit equations.