Multi-gene Genetic Programming Research Articles

Estimation of Arrivals on Green at Signalized Intersections Using Stop-Bar Video Detection

Across the world, traffic congestion is increasing with alarming rapidity. Traffic signal control effectiveness, in coordinated networks, is often investigated in relation to the type of vehicle arrivals at the signalized intersections. Recently, several transportation agencies have switched from traditional loop detectors to video detection. When video cameras are accompanied by computer vision, one can extract more information about traffic “dynamics” than by using traditional inductive loop detectors. Collecting arrival times of multiple vehicles after the first arrival at the stop-bar detector might be challenging when using inductive loop detectors (since after the first arrival, detector status is always occupied). However, emerging video detection systems allow tracking of each vehicle’s entrance time in the detection zone, departure time from the detection zone, and the type of vehicle. This information can be used to estimate vehicular arrival and departure times, which then can be fed into machine learning algorithms to estimate arrivals on green (AOG). However, such research ideas have not been documented so far. Thus, this paper presents an estimation model for AOG, which was developed using multigene genetic programming. A robust experimental dataset was collected from a highly calibrated and validated microsimulation model of an 11-intersection corridor in Chattanooga, TN. The results of the model’s performance analysis showed the high accuracy of the training-, testing-, and validation datasets. The practical benefit of this model is that it can be applied to estimate arrival types at intersections where only stop-bar video detection exists.

Investigation of the fatigue life relationship among different geometry combinations of the 3-point bending cylinder (3PBC) fatigue test for asphalt concrete

ABSTRACT Laboratory testing is widely used to characterise fatigue properties of asphalt mixtures for the design of asphalt pavements. The recently introduced Three-Point Bending Cylinder (3PBC) test has proven to be an advantageous laboratory testing alternative. However, the standard sample size for the 3PBC test may hamper its use for testing as-built thin lifts of asphalt pavement. In this study, the potential to alter the sample size for 3PBC test was investigated. Various numerical techniques were explored to relate the fatigue performance in terms of the number of cycles to failure (Nf) among varying specimen diameters. In addition to Nf of the tested 3PBC sample diameter Nf (Di), strain level; 3PBC test sample diameter Di; and dynamic modulus |E*|; were selected as input variables to correlate with the Nf for the reference geometry Nf(D.R.). Initially, multi-linear regression (MLR) analysis was applied to inspect if any cogent relation could be achieved among the Nf values for different 3PBC sample diameters. In addition, multigene genetic programming (MGGP) and artificial neural network (ANN) analyses were performed to check if a better fit could be achieved. Both these techniques, however, revealed similar statistical results to the MLR model, with the ANN model achieving higher statistics.

Multigene Genetic Programming Based Prediction of Concrete Fracture Parameters of Unnotched Specimens

This study explores the fracture energy of notched and unnotched concrete specimens subjected to the classical three-point bend test, instantiating a gradational step in the continued development of concrete fracture mechanics. An experimental campaign involving 18 notched test specimens and nine unnotched specimens of three different grades of concrete, an examination of the existing literature models for unnotched specimens, and a novel Multigene Genetic programming (MGGP) based concrete fracture energy model for unnotched specimens are integral to this study. As a salient result, the multiple approaches to quasi-brittle materials adopted in the study, highlighted the criticality of the determination of fracture energy, tensile strength and characteristic length for the crack width study. The failure modes of notched and unnotched specimens were found to be similar. The reported literature has mainly focused on a limited number of fracture energy influencing parameters. Therefore, six impact parameters have been chosen and incorporated into the present study to provide a more acceptable explanation of concrete fracture behaviour. A sensitivity analysis of the parameters and an error analysis of the model undertaken have established the accuracy and robustness of the developed MGGP model. Doi: 10.28991/CEJ-2023-09-02-011 Full Text: PDF

Convective drying of unripe plantain: a comparative response surface methodology and genetic algorithm optimization study, certainty and sensitivity analysis

The requirement for reduction of post-harvest losses increased production, and cost-effectiveness of foods are driving continuous food process investigations. In this study, Response Surface Methodology (RSM) was utilized to understand, model, and optimize the effect of selected process factors on the moisture content (MC) of convectively dried unripe plantain fruit. For technical accuracies, Multi Gene Genetic Programming (MGGP) was also used to model the process and both MGGP and RSM models were statistically compared. Furthermore, Monte Carlo Simulation (MCS) was used to conduct sensitivity analysis of unripe plantain’s MC to each selected process factor. Results showed that increased sample thickness increased the MC while increased drying temperature and drying time decreased the MC of unripe plantain. RSM model had Chi-square, MBE, t-value, RMSE and R2 values of 15.2131, 0.7531, 7.6170, 0.9193 and 0.9674, respectively; while MGGP model had 3.0415, 0.2563, 2.6871, 0.4111 and 0.9956, respectively. Sensitivity analysis showed that sample thickness, drying temperature and drying time had +89.5 %, -10.2 % and -0.3 % contribution to the variances of MC, respectively. These results are useful in unripe plantain drying process prediction, optimization, and product standardization.

MeterGPX: A Smart Multimeter Embedded with Multigene Genetic Programming Model for Multiarray Antenna Transmitter

Genetic programming (GP) is an evolutionary algorithm used to produce high-quality solutions to various problems. It has seen few claims in circuitry and the development of antenna designs. The application of GP in the model of embedded digital systems on multi-channel antenna arrays of subsurface imaging equipment has not yet been investigated. This study focuses on designing and developing a digital multimeter embedded with a multigene genetic programming (MGGP) model for multi-array transmitter antenna used for subsurface imaging operating at a low frequency of 3.5 kHz to 18.5 kHz using Arduino microcontroller for prototyping. The electrical outputs of a transmitter antenna system employed in a subsurface imaging device require live measurement and monitoring during operation for data logging purposes. The amount of transmitted voltage, produced current, and operating frequency are significant parameters for mapping the underground resistivity, thus the produced GP models are functions of the three parameters. GP fitness function was determined through MATLAB software. The output current signal from the transmitter were imitated in Proteus simulation software using a current source in the designed current measuring circuit. This produced linear and nonlinear relationships of the electrical outputs where GP modeling was beneficially applied. The application of GP in with the microcontroller provided an accurate reading of frequency, current and voltage produced by the multi-array transmitter antenna. These measurements were displayed using LM016L LCD display. Moreover, this embedded digital multimeter on transmitter antenna avoids utilizing costly high voltage measuring devices.

Grape Pseudocercospora Leaf Specked Area Estimation Using Hybrid Genetic Algorithm and Recurrent Neural Network

Grapes are prone to Pseudocercospora vitis fungus which causes Isariopsis leaf speck disease to the crop’s leaves, flower, and most importantly the fruit. Typical manual inspection of vineyard farmers is normally ineffective, destructive, and laborious. To address this, the use of integrated computer vision, machine learning, and computational intelligence techniques were realized to sort out healthy grape leaf image from a fungus-specked leaf image and to estimate the specked area percentage (SAP). A dataset made up of 343 normally healthy and 200 fungus-specked grape leaf images were initially pre-processed and segmented via graph cut prior to feature extraction and selection. Significant features were identified using classification tree (CTree). A multigene genetic programming tool called GPTIPSv2 was utilized to generate the fitness function needed for the optimization process done via genetic algorithm (GA). An optimal hidden neuron counts of 110, 50, and 10 were selected for a three-layered GA-optimized recurrent neural network (GA-RNN). Linear discriminant analysis (LDA) topped other disease recognition models with an accuracy of 99.99%. The developed GA-RNN model outperformed Gaussian process regression (GPR), regression tree (RTree), regression support vector machine (RSVM), and linear regression (RLinear) in performing leaf specked area estimation with an R2 value of 0.822. The developed CTree-LDA2-GA-RNN2 hybrid model has been proven to be the most viable model for this task.

Multi-gene GP and GA-FIS Models to Deal with Scaling Problem in the ANFIS model for Estimating Roughness Coefficient in Erodible Channels

Estimation of the roughness coefficient is important for reliable hydraulic design in erodible channels. In this paper, the capability of multi-gene Genetic Programming (GP), a combined Genetic Algorithm and Fuzzy Inference System (GA-FIS) model, and Multi Regression (MR) methods are employed to estimate the roughness coefficient. These methods try to extract either an explicit or an implicit relationship between the roughness coefficient and input variables. In addition, traditional GP, widely used by researchers, and conventional empirical formulas are implemented to evaluate the models. Results show that the employed methods are more accurate than empirical methods. In addition, the effects of some other parameters, such as non-dimensional water depth and shear Reynolds number, are highlighted over the roughness coefficient while previously ignored in the empirical methods. Also, findings prove that the GA is a helpful tool to optimize a FIS compared with gradient-based models like ANFIS, while the scale of input variables is not in the same order. The R2 for multi-gene GP and GA-FIS are 0.8504 and 0.8842, respectively, while this value for the most accurate empirical method (Yalin, 1992) is 0.6286.

Exploring the use of multi-gene genetic programming in regional models for the simulation of monthly river runoff series

Exploring the use of multi-gene genetic programming in regional models for the simulation of monthly river runoff series

Estimation of Delay and Surrogate Conflicts in Automated Network Management Using Linear Regression and Multigene Genetic Programming

Estimation of Delay and Surrogate Conflicts in Automated Network Management Using Linear Regression and Multigene Genetic Programming

Correction to: Buckling Load Estimation Using Multiple Linear Regression Analysis and Multigene Genetic Programming Method in Cantilever Beams with Transverse Stiffeners

Correction to: Buckling Load Estimation Using Multiple Linear Regression Analysis and Multigene Genetic Programming Method in Cantilever Beams with Transverse Stiffeners

Two-dimensional convolutional neural network outperforms other machine learning architectures for water depth surrogate modeling

Rapid prediction of spatially distributed hydrological variables, such as water depths in rivers, is an important but challenging task. This study proposes a novel matrix-based deep learning approach for predicting spatial distribution of water depths in rivers. The proposed approach was constructed based on a two-dimensional (2D) convolutional neural network (CNN) with a new architecture that was specifically designed for providing spatial distribution maps. A numerical dataset was established based on a field cruise and two-dimensional hydraulic modeling for different scenarios, and numerical experiments were designed to predict spatial distribution of water depths for different scenarios using the adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), genetic programming (GP), multi-gene genetic programming (MGGP), one-dimensional CNN (1D-CNN), and the proposed CNN algorithms. The results showed that the proposed CNN approach captured both the large-scale and small-scale spatial patterns remarkably well, and it outperformed the other approaches. This study shows that the 2D CNN algorithm is better than the classical machine learning (ML) algorithms for inundation modeling. The proposed approach is thus a promising tool for providing rapid predictions of spatial distribution of water depths in river systems and can potentially be leveraged to predict other spatially distributed hydrological variables.

Machine learning-based downscaling: application of multi-gene genetic programming for downscaling daily temperature at Dogonbadan, Iran, under CMIP6 scenarios

Machine learning-based downscaling: application of multi-gene genetic programming for downscaling daily temperature at Dogonbadan, Iran, under CMIP6 scenarios

Development of correlations for steam condensation over a vertical tube in the presence of noncondensable gas using machine learning approach

Steam condensation is an important phenomenon encountered in nuclear reactor under severe accidents. Even though many correlations for predicting steam condensation heat transfer coefficient (HTC) in the presence of noncondensable gas (NCG) have been proposed over the past decade, a more reliable and accurate model is still required. Thus, in this study, multigene genetic programming (MGGP), a biologically inspired machine learning method, is applied to develop new correlations for condensation HTC of steam-NCG mixture over a vertical tube in turbulent free convection regime. To this end, a consolidated database with 1440 data points from 18 sources is compiled. Then, using the database, both a new empirical correlation and a MGGP model are developed for better comparison. The performance of the MGGP-based correlation selected using Pareto tournaments strategy is compared with the new developed empirical correlation and another 20 relevant correlations. The results reveal the superiority of the MGGP-based correlation. In addition, it is found that the tube length is excluded in the best-trained correlation, even though it is used as the input of MGGP, which agrees well with the results of previous theoretical and experimental studies. The present study demonstrates that MGGP is promising in developing explicit, accurate, and compact models for the complex heat transfer and multiphase flow phenomena such as steam condensation in the presence of NCG.

Backbone model for reinforced concrete block shear wall components and systems using controlled multigene genetic programming

Reinforced concrete block shear walls (RCBSWs)have been used as an effective seismic force resisting system in low- and medium-rise buildings for many decades. However, attributed to their complex nonlinear behavior and the composite nature of their constituent materials, accurate prediction of their seismic performance, relying solely on mechanics, has been challenging. This study adopts multi-gene genetic programming (MGGP)— a class of bio-inspired artificial intelligence, to uncover the complexity of RCBSW behaviors and develop simplified procedures for predicting the full backbone curve of flexure-dominated RCBSWs under cyclic loading. A piecewise linear backbone curve was developed using five secant stiffness expressions associated with: cracking; yielding; 80% ultimate; ultimate; and 20% strength degradation (i.e., post-peak stage) derived through mechanics-controlled MGGP. Based on the experimental results of large-scale cyclically loaded fully-grouted RCBSWs, compiled from previously reported studies, a variable selection procedure was performed to identify the most influential variable subset governing wall behaviors. Subsequently, the MGGP stiffness expressions were trained and tested, and their accuracy was compared to that of existing models employing various statistical measures. In addition, the predictability of the developed backbone model was assessed at the system-level against experimental results of two two-story buildings available in the literature. This study demonstrates the power of the MGGP approach in addressing the complexity of the cyclic behavior of RCBSWs at both component- and system-level—offering an efficient prediction tool that can be adopted by relevant seismic design standards pertaining to RCBSW buildings.

Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods.

In this study, the predictive power of three different machine learning (ML)-based approaches, namely, multi-gene genetic programming (MGGP), M5 model trees (M5Tree), and K-nearest neighbor algorithm (KNN), for long-term monthly reference evapotranspiration (ET0) prediction were investigated. The input data consist of monthly solar radiation (Rs), maximum air temperature (Tmax), and wind speed (Ws) derived from 163 meteorological stations in Turkey. Different input combinations were created and analyzed. The model's performance was evaluated using criteria such as Nash-Sutcliffe efficiency, Kling-Gupta efficiency, relative root mean squared error, mean absolute percentage error, and determination coefficient. Moreover, Taylor, radar, and boxplot diagrams were created. It was determined that the MGGP model outperformed both the M5Tree and the KNN models. The equation obtained from the MGGP model, for the best-performed combination of Rs-Tmax-Ws, was presented. The best weather conditions were obtained as 0.029 to 31.814MJ/m2, - 5.8 to 45.7°C, and 0.140 to 5.086m/s for Rs, Tmax, and Ws, respectively. It was also found that the Rs was the most potent input variable for ET0 estimation while Ws was the weakest.

Soft Computing Approach for Predicting the Effects of Waste Rubber–Bitumen Interaction Phenomena on the Viscosity of Rubberized Bitumen

The ability to anticipate the effects of the interaction between waste rubber particles from end-of-life tires and bitumen can encourage the use of rubberized bitumen, a material with proven environmental benefits, in civil engineering applications. In this study, a predictive model of rubberized bitumen viscosity is presented for this purpose. A machine learning-based approach (Multi-Gene Genetic Programming—MGGP) and a more traditional multi-variable least square regression (MLSR) method are compared. The statistical analysis indicates that the robustness and the capability of the MGGP algorithm led to a better estimation of the rubberized bitumen’s viscosity. Additionally, the MGGP analysis returned an actual equation that could be easily implemented in any spreadsheet for an initial tuning of the production protocol based on the desired level of interaction between the rubber and bitumen.

Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in 3D Ultrasound Images

Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in 3D Ultrasound Images

CFD-CNN Modeling of the Concentration Field of Multiport Buoyant Jets

At present, there are increasing applications for rosette diffusers for buoyant jets with a lower density than the ambient water, mainly in the discharge of wastewater from municipal administrations and sea water desalination. It is important to study the mixing effects of wastewater discharge for the benefit of environmental protection, but because the multiport discharge of the wastewater concentration field is greatly affected by the mixing and interacting functions of wastewater, the traditional research methods on single-port discharge are invalid. This study takes the rosette multiport jet as a research subject to develop a new technology of computational fluid dynamics (CFD) modeling and carry out convolutional neural network (CNN) simulation of the concentration field of a multiport buoyant jet. This study takes advantage of CFD technology to simulate the mixing process of a rosette multiport buoyant jet, uses CNNs to construct the machine learning model, and applies RSME, R2 to conduct evaluations of the models. This work also makes comparisons with the machine learning approach based on multi-gene genetic programming, to assess the performance of the proposed approach. The experimental results show that the models constructed based on the proposed approach meet the accuracy requirement and possess better performance compared with the traditional machine learning method, and they can provide reasonable predictions.

Undersaturated Oil Viscosity Based on Multi-Gene Genetic Programming

Abstract Undersaturated oil viscosity represents an important physical property for reservoir simulation, enhanced oil recovery, and optimal production. It can be determined either by experimental measurements or by modeling through empirical correlations of appropriate accuracy. As a result of the high cost of its determination experimentally as well as its unavailable in most cases, looking for a high-reliability model is vital. Therefore, in this paper, a new undersaturated crude oil viscosity model using multi-gene genetic programming (MGGP) is presented. This model was built using several data points which are distributed as 528 experimental measurements for a broad range of reservoir pressure and oil properties and 276 points were used for validating and testing the new model. Furthermore, the new model was compared to 11 published correlations. The results indicated that the new MGGP-based model is the closest to the experimental measurements and yields a precise prediction of undersaturated oil viscosity.

Multi-Gene Genetic Programming of IoT Water Quality Index Monitoring from Fuzzified Model forOreochromis niloticusRecirculating Aquaculture System

Real-time water quality index (WQI) monitoring – a simplified single variable indication of water quality (WQ) – is vital in attaining a sustainable future in precision aquaculture. Although several monitoring systems for water quality parameters (WQP) use IoT, there is no existing WQI IoT monitoring forOreochromis niloticusbecause the current WQI models are too complex to be deployed for low-level computing platforms such as the IoT modules and dashboards. Thus, the development of the IoT-based WQI fuzzy inference system (FIS) was simplified by the multi-gene genetic programming (MGGP) to search for non-linear equations given the simulated WQP fuzzy sets. Results have shown that the implemented novel system can accurately predict the WQI IoT monitoring with an average of R2and RMSE of 0.9112 and 0.6441, respectively. Implementing WQI in the IoT monitoring dashboard using the MGGP has significantly addressed the present challenges in deploying other complex AI-based models for WQI, such as the FIS and neural networks in low-computing capable platforms.