Gene-expression programming for flip-bucket spillway scour

2012 ◽  
Vol 65 (11) ◽  
pp. 1982-1987 ◽  
Author(s):  
Aytac Guven ◽  
H. Md. Azamathulla
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Statistical Error ◽  
Field Measurements ◽  
Error Measures ◽  
Soft Computing Techniques ◽  
Scatter Plots ◽  
Actual Field ◽  
Better Than ◽  
Good Agreement

During the last two decades, researchers have noticed that the use of soft computing techniques as an alternative to conventional statistical methods based on controlled laboratory or field data, gave significantly better results. Gene-expression programming (GEP), which is an extension to genetic programming (GP), has nowadays attracted the attention of researchers in prediction of hydraulic data. This study presents GEP as an alternative tool in the prediction of scour downstream of a flip-bucket spillway. Actual field measurements were used to develop GEP models. The proposed GEP models are compared with the earlier conventional GP results of others (Azamathulla et al. 2008b; RMSE = 2.347, δ = 0.377, R = 0.842) and those of commonly used regression-based formulae. The predictions of GEP models were observed to be in strictly good agreement with measured ones, and quite a bit better than conventional GP and the regression-based formulae. The results are tabulated in terms of statistical error measures (GEP1; RMSE = 1.596, δ = 0.109, R = 0.917) and illustrated via scatter plots.

scholarly journals Application of Gene Expression Programming, Artificial Neural Network and Multilinear Regression in Predicting Hydrochar Physicochemical Properties

2020 ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

Abstract Globally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass by using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE), and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10-10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

scholarly journals Acoustics of a Music Venue/Bar—A Case Study

2021 ◽  
Author(s):  
Ramani Ramakrishnan ◽  
Romain Dumoulin
Keyword(s):  
Field Measurements ◽  
Performance Space ◽  
Rock Band ◽  
Northern Portion ◽  
Actual Field ◽  
Performance Results ◽  
South Axis ◽  
Western Portion ◽  
Better Than

A vacant unit, once used by a Portuguese Deli, was converted to a bar/music room in Toronto. The unit was divided into two spaces along its north-south axis. The western portion was designed as a music room that would provide a performance space from a solo artist to a Jazz combo to a small rock band. The eastern part was designed as a regular bar/dining area. The plan also called for a microbrewery unit at the back of the unit. The bar music can be loud, while the music room can be pianissimo to forte depending on the type of performance. The acoustical design aspects are critical for the music room. In addition, the acoustical separation between the two spaces is equally important. The music room/bar is currently in use. The design results are compared to actual field measurements. The results showed that the music venue performed satisfactorily. The acoustical separation between the music venue and the bar/restaurant was better than expected other than an installation deficiency of the south side sound lock doors. The background sound along the northern portion was NC-35 or less. However, the southern portion’s background sound exceeded NC-35 due to the hissing of the return air grille. The acoustical design and the performance results of the music venue-bar/restaurant are presented in this paper.

scholarly journals Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1106 ◽  
Author(s):  
Mohsin Ali Ali Khan ◽  
Adeel Zafar ◽  
Arslan Akbar ◽  
Muhammad Faisal Javed ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Empirical Equation ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Regression Equations ◽  
Soft Computing Techniques ◽  
Water Ratio

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength fc′ of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 fc′ experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F/AG), the percentage of total aggregate by volume ( %AG), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (AL/FA), the sodium oxide (Na2O) to water ratio (N/W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (Ns/No). A GEP empirical equation is proposed to estimate the fc′ of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

scholarly journals Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-17 ◽  
Author(s):  
Mohsin Ali Khan ◽  
Shazim Ali Memon ◽  
Furqan Farooq ◽  
Muhammad Faisal Javed ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Fly Ash ◽  
Random Forest ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Soft Computing Techniques ◽  
Water Ratio

Fly ash (FA) is a residual from thermal industries that has been effectively utilized in the production of FA-based geopolymer concrete (FGPC). To avoid time-consuming and costly experimental procedures, soft computing techniques, namely, random forest regression (RFR) and gene expression programming (GEP), are used in this study to develop an empirical model for the prediction of compressive strength of FGPC. A widespread, reliable, and consistent database of compressive strength of FGPC is set up via a comprehensive literature review. The database consists of 298 compressive strength data points. The influential parameters that are considered as input variables for modelling are curing temperature T , curing time t , age of the specimen A , the molarity of NaOH solution M , percent SiO2 solids to water ratio %   S / W in sodium silicate (Na2SiO3) solution, percent volume of total aggregate (   %   A G ), fine aggregate to the total aggregate ratio F / A G , sodium oxide (Na2O) to water ratio N / W in Na2SiO3 solution, alkali or activator to the FA ratio A L / F A , Na2SiO3 to NaOH ratio N s / N o , percent plasticizer ( %   P ), and extra water added as percent FA E W % . RFR is an ensemble algorithm and gives outburst performance as compared to GEP. However, GEP proposed an empirical expression that can be used to estimate the compressive strength of FGPC. The accuracy and performance of both models are evaluated via statistical error checks, and external validation is considered. The proposed GEP equation is used for sensitivity analysis and parametric study and then compared with nonlinear and linear regression expressions.

scholarly journals Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Jibril Abdulsalam ◽  
Abiodun Ismail Lawal ◽  
Ramadimetja Lizah Setsepu ◽  
Moshood Onifade ◽  
Samson Bada
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Gene Expression Programming ◽  
Mean Bias Error ◽  
Energy Yield ◽  
Multilinear Regression ◽  
Soft Computing Techniques ◽  
Artificial Neural

AbstractGlobally, the provision of energy is becoming an absolute necessity. Biomass resources are abundant and have been described as a potential alternative source of energy. However, it is important to assess the fuel characteristics of the various available biomass sources. Soft computing techniques are presented in this study to predict the mass yield (MY), energy yield (EY), and higher heating value (HHV) of hydrothermally carbonized biomass using Gene Expression Programming (GEP), multiple-input single output-artificial neural network (MISO-ANN), and Multilinear regression (MLR). The three techniques were compared using statistical performance metrics. The coefficient of determination (R2), mean absolute error (MAE) and mean bias error (MBE) were used to evaluate the performance of the models. The MISO-ANN with 5-10 to 10-1 and 5-15-15-1 network architectures provided the most satisfactory performance of the three proposed models (R2 = 0.976, 0.955, 0.996; MAE = 2.24, 2.11, 0.93; MBE = 0.16, 0.37, 0.12) for MY, EY and HHV, respectively. The GEP technique’s ability to predict hydrochar properties based on the input parameters was found to be satisfactory, while MLR provided an unsatisfactory predictive model. Sensitivity analysis was conducted, and the analysis revealed that volatile matter (VM) and temperature (Temp) have more influence on the MY, EY, and HHV.

scholarly journals Acoustics of a Music Venue/Bar—A Case Study

2021 ◽  
Author(s):  
Ramani Ramakrishnan ◽  
Romain Dumoulin
Keyword(s):  
Field Measurements ◽  
Performance Space ◽  
Rock Band ◽  
Northern Portion ◽  
Actual Field ◽  
Performance Results ◽  
South Axis ◽  
Western Portion ◽  
Better Than

A vacant unit, once used by a Portuguese Deli, was converted to a bar/music room in Toronto. The unit was divided into two spaces along its north-south axis. The western portion was designed as a music room that would provide a performance space from a solo artist to a Jazz combo to a small rock band. The eastern part was designed as a regular bar/dining area. The plan also called for a microbrewery unit at the back of the unit. The bar music can be loud, while the music room can be pianissimo to forte depending on the type of performance. The acoustical design aspects are critical for the music room. In addition, the acoustical separation between the two spaces is equally important. The music room/bar is currently in use. The design results are compared to actual field measurements. The results showed that the music venue performed satisfactorily. The acoustical separation between the music venue and the bar/restaurant was better than expected other than an installation deficiency of the south side sound lock doors. The background sound along the northern portion was NC-35 or less. However, the southern portion’s background sound exceeded NC-35 due to the hissing of the return air grille. The acoustical design and the performance results of the music venue-bar/restaurant are presented in this paper.

scholarly journals Component Thermodynamical Selection Based Gene Expression Programming for Function Finding

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhaolu Guo ◽  
Zhijian Wu ◽  
Xiaojian Dong ◽  
Kejun Zhang ◽  
Shenwen Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Selective Pressure ◽  
Gene Expression Programming ◽  
Population Diversity ◽  
Minimal Free Energy ◽  
Slow Convergence ◽  
Benchmark Datasets ◽  
Function Finding ◽  
Better Than

Gene expression programming (GEP), improved genetic programming (GP), has become a popular tool for data mining. However, like other evolutionary algorithms, it tends to suffer from premature convergence and slow convergence rate when solving complex problems. In this paper, we propose an enhanced GEP algorithm, called CTSGEP, which is inspired by the principle of minimal free energy in thermodynamics. In CTSGEP, it employs a component thermodynamical selection (CTS) operator to quantitatively keep a balance between the selective pressure and the population diversity during the evolution process. Experiments are conducted on several benchmark datasets from the UCI machine learning repository. The results show that the performance of CTSGEP is better than the conventional GEP and some GEP variations.

scholarly journals Multi Expression Programming

2021 ◽  
Author(s):  
Mihai Oltean ◽  
D. Dumitrescu
Keyword(s):  
Gene Expression ◽  
Genetic Programming ◽  
Gene Expression Programming ◽  
Test Problems ◽  
Machine Code ◽  
Cartesian Genetic Programming ◽  
Mathematical Expressions ◽  
Multi Expression Programming ◽  
Complex Computer ◽  
Better Than

Abstract Multi Expression Programming (MEP) is a new evolutionary paradigm intended for solving computationally difficult problems. MEP individuals are linear entities that encode complex computer programs. MEP chromosomes are represented in the same way as C or Pascal compilers translate mathematical expressions into machine code. MEP is used for solving some difficult problems like symbolic regression and game strategy discovering. MEP is compared with Gene Expression Programming (GEP) and Cartesian Genetic Programming (CGP) by using several well-known test problems. For the considered problems MEP outperforms GEP and CGP. For these examples MEP is two magnitude orders better than CGP.

scholarly journals Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

2021 ◽  
Author(s):  
Mohsin Ali Khan ◽  
Adeel Zafar ◽  
Arslan Akbar ◽  
Muhammad Faisal Javed ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Empirical Equation ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Regression Equations ◽  
Soft Computing Techniques ◽  
Water Ratio

Abstract: For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength f_c^' of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 f_c^' experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%E_W), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F⁄A_G ), the percentage of total aggregate by volume ( 〖%A〗_G), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (A_L⁄F_A ), the sodium oxide (Na2O) to water ratio (N⁄W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (N_s⁄N_o ). A GEP empirical equation is proposed to estimate the f_c^' of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

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