Genetic Algorithm Support Vector Regression Research Articles

A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

This study aims to identify the suitability of hybridizing the firefly algorithm (FA), genetic algorithm (GA), and particle swarm optimization (PSO) with two well-known data-driven models of support vector regression (SVR) and artificial neural network (ANN) to predict blast-induced ground vibration. Here, these combinations are abbreviated using FA–SVR, PSO–SVR, GA–SVR, FA–ANN, PSO–ANN, and GA–ANN models. In addition, a modified FA (MFA) combined with SVR model is also proposed in this study, namely, MFA–SVR. The feasibility of the proposed models is examined using a case study, located in Johor, Malaysia. Then, to provide an objective assessment of performances of the predictive models, their results were compared based on several well known and popular statistical criteria. According to the results, the MFA–SVR with the coefficient of determination (R2) of 0.984 and root mean square error (RMSE) of 0.614 was more accurate model to predict PPV than the PSO–SVR with R2 = 0.977 and RMSE = 0.725, the FA–SVR with R2 = 0.964 and RMSE = 0.923, the GA–SVR with R2 = 0.957 and RMSE = 1.016, the GA–ANN with R2 = 0.936 and RMSE = 1.252, the FA–ANN with R2 = 0.925 and RMSE = 1.368, and the PSO–ANN with R2 = 0.924 and RMSE = 1.366. Consequently, the MFA–SVR model can be sufficiently employed in estimating the ground vibration, and has the capacity to generalize.

Monthly prediction of streamflow using data-driven models

The estimation of river run-off is a complex process, but it is of vital importance to the proper operation of reservoirs, the design of hydraulic structures, flood control, drought management and the supply of water and electricity. The high uncertainty in rainfall–run-off modelling and lack of data has made the development of rainfall–run-off models with acceptable levels of accuracy and precision challenging. Furthermore, the rainfall–run-off models commonly do not provide an explicit relationship between run-off and other variables to be used for run-off-related investigations. To overcome the knowledge and information shortage in rainfall–run-off modelling, data-driven models have been used instead of conceptual models for the development of rainfall–run-off models. In this paper, three data-driven models, the genetic algorithm-support vector regression (GA-SVR), genetic algorithm-artificial neural network (GA-ANN) and the group method of data handling (GMDH) have been used to predict the monthly run-off of the Gavehroud basin. Their performances are compared with a conceptual hydrological model (HYMOD) whose parameters are calibrated using the GA. To this end, the monthly data on precipitation, temperature and run-off at the Gavehroud basin over 49 yr (1960–2009) were analysed. Evaluation of the results using performance evaluation indicators showed that the hybrid model of GA-SVR provided better accuracy in predicting the nonlinear behaviour of flow data than the GA-ANN, GMDH and HYMOD.

Closure to “Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm–Support Vector Regression” by Lei Jiang, Mingjun Diao, Hongcheng Xue, and Haomiao Sun

Closure to “Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm–Support Vector Regression” by Lei Jiang, Mingjun Diao, Hongcheng Xue, and Haomiao Sun

Discussion of “Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm–Support Vector Regression” by Lei Jiang, Mingjun Diao, Hongcheng Xue, and Haomiao Sun

Discussion of “Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm–Support Vector Regression” by Lei Jiang, Mingjun Diao, Hongcheng Xue, and Haomiao Sun

Discussion of “Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm–Support Vector Regression” by Lei Jiang, Mingjun Diao, Hongcheng Xue, and Haomiao Sun

Discussion of “Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm–Support Vector Regression” by Lei Jiang, Mingjun Diao, Hongcheng Xue, and Haomiao Sun

Rapid prediction of yellow tea free amino acids with hyperspectral images.

Free amino acids are an important indicator of the freshness of yellow tea. This study investigated a novel procedure for predicting the free amino acid (FAA) concentration of yellow tea. It was developed based on the combined spectral and textural features from hyperspectral images. For the purposes of exploration and comparison, hyperspectral images of yellow tea (150 samples) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing. To reduce the dimension of spectral data, five feature wavelengths were extracted using the successive projections algorithm (SPA). Five textural features (angular second moment, entropy, contrast, correlation, and homogeneity) were extracted as textural variables from the characteristic grayscale images of the five characteristic wavelengths using the gray-level co-occurrence matrix (GLCM). The FAA content prediction model with different variables was established by a genetic algorithm-support vector regression (GA-SVR) algorithm. The results showed that better prediction results were obtained by combining the feature wavelengths and textural variables. Compared with other data, this prediction result was still very satisfactory in the GA-SVR model, indicating that data fusion was an effective way to enhance hyperspectral imaging ability for the determination of free amino acid values in yellow tea.

Modeling and prediction of the specific heat capacity of Al[formula omitted][formula omitted]/water nanofluids using hybrid genetic algorithm/support vector regression model

In this study, the specific heat capacity of Alumina (Al2O3)/water nanofluid has been accurately evaluated using genetic algorithm/support vector regression (GA/SVR) model at volume fractions of 3.7–9.3%. The proposed (genetic algorithm/support vector regression) GA/SVR model was formulated using volume fractions and specific heat capacities of the alumina nanoparticles. The developed GA/SVR model is very accurate as determined from 99.998% correlation coefficient with experimentally obtained data and also has a root mean square error of 0.0014. Furthermore, the obtained results from the GA/SVR were compared with existing analytic models. Remarkably, the proposed model achieved an order of magnitude improvement over the model based on thermal equilibrium (Model II) and a two order of magnitude improvement over the model based on simple mixing rule for ideal gases (model I). Given the improvement in the accuracy, the proposed model would be useful for rapid and highly accurate estimation of the specific heat capacity of alumina/water nanofluids.

Financial Forecasting With Multivariate Adaptive Regression Splines and Queen Genetic Algorithm-Support Vector Regression

In consideration of the financial indicators of financial structure, solvency, operating ability, profitability, and cash flow as well as the non-financial indicators of firm size and corporate governance, the algorithms of multivariate adaptive regression splines (MARS) and queen genetic algorithm-support vector regression (QGA-SVR) are used in this study to create a comprehensive financial forecast of operating revenue, earnings per share, free cash flow, and net working capital to help enterprises forecast their future financial situation and offer investors and creditors a reference for investment decision-making. This study’s objectives are achieved through the following steps: (i) establishment of feature indicators for financial forecasting, (ii) development of a financial forecasting method, and (iii) demonstration of the proposed method and comparison with existing methods.

Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area.

The specific surface area (SSA) of ABO3-type perovskite is one of the important properties associated with photocatalytic ability. In this work, data mining methods were used to explore the relationship between the SSA (in the range of 1-60 m2 g-1) of perovskite and its features, including chemical compositions and technical parameters. The genetic algorithm-support vector regression method was used to screen the main features for modeling. The correlation coefficient ( R) between the predicted and experimental SSAs reached as high as 0.986 for the training data set and 0.935 for leave-one-out cross-validation. ABO3-type perovskites with higher SSA can be screened out using the Online Computation Platform for Materials Data Mining (OCPMDM) developed in our laboratory. Further, an online web server has been developed to share the model for the prediction of SSA of ABO3-type perovskite, which is accessible at http://118.25.4.79/material_api/csk856q0fulhhhwv .

Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm–Support Vector Regression

AbstractAccurately forecasting energy dissipation is critical to the hydraulic design of stepped spillways. In this study, support vector machine regression (SVR) was applied to estimate the energy...

Application of global precipitation dataset for drought monitoring and forecasting over the Lake Urmia basin with the GA-SVR model

In the present study, the accuracy of the climate research unit (CRU) precipitation data was assessed as an alternative source instead of in situ data for monitoring the drought in the Lake Urmia Basin area during the period from 1984 to 2013. Later, a genetic algorithm-support vector regression (GA-SVR) model was utilised in order to forecast drought conditions up to four months ahead. The results demonstrated that the CRU data had acceptable accuracy in drought monitoring so that in at least 75% of the cases, there was no difference between the monitored drought classed through observed data and CRU data. In the forecasting section, the results showed two general patterns. The first pattern indicated a descending trend of forecast accuracy with an increase in the lead-times ahead of forecasts; the second pattern revealed the ascending trend of forecast accuracy, with an increase in the SPI scale.

Short-Term Wind Speed Prediction Using Signal Preprocessing Technique and Evolutionary Support Vector Regression

Short-term wind speed prediction is beneficial to guarantee the safety of wind power utilization and reduce the cost of wind power generation. As a kind of the powerful artificial intelligent algorithms, support vector regression (SVR) has been successfully employed in solving forecasting problems. However, due to the intrinsic complexity and multi-patterns of wind speed fluctuations, it is regarded as one of the most challenging applications for wind speed prediction. To alleviate the influence of complexity and capture these different patterns, this study proposes a novel approach named SIE–WDA–GA–SVR for short-term wind speed prediction, which applies the seasonal information extraction (SIE) and wavelet decomposition algorithm (WDA) into hybrid model that integrates the genetic algorithm (GA) into SVR. First, the proposed approach uses SIE to decompose the original wind speed into seasonal and trend components, and the seasonal indices are calculated by SIE. Second, the proposed approach uses WDA to decompose the trend component into both the approximate and the detailed scales. Third, the proposed approach uses GA–SVR to forecast the approximated and detailed scales, respectively. Then, the prediction values of the trend component can be obtained by integrating the prediction values of the approximated scale into the prediction values of the detailed scale. By integrating the seasonal indices into the prediction values of trend component, we can obtain the final forecasting results of the original wind speed. Moreover, the partial autocorrelation function is used to determine the number of input dimension for the SVR, and the GA is used to select the parameters of the SVR. Four real wind speed datasets are used as test samples to verify the proposed approach. Experimental results indicate that the proposed approach outperforms other benchmark models in four statistical error measures, and can improve the forecasting accuracy of wind speed.

Autonomic point cloud-based surface reconstruction using SVR

ABSTRACTA surface reconstruction framework based on support vector regression (SVR) to generate a three-dimensional (3D) model is proposed in this paper. It can reduce the noise in sampled data as well as repair the holes by handling the missing data during the acquisition phase. SVR is quite efficient for surface reconstruction using parameter tuning and selective data sampling. Automatic parameter tuning of SVR is proposed using two techniques: particle swarm optimization (PSO) and genetic algorithm (GA). Independent component analysis (ICA) is a feature-preserved non-uniform simplification method which is applied to simplify point set by optimal attribute selection. First, under-sample the data, remove the redundancy, reduce the features using ICA and construct the surface using SVR. Both theoretical analysis and experimental results show that the performance of the proposed method yields an average SVR error ≈ 3% on the publicly available datasets. For majority of standard datasets, PSO–SVR is found superior to GA–SVR in convergence speed. Details of the surface are also preserved well which makes it suitable for 3D surface reconstruction.

Load location identification of fiber optic smart structures based on a genetic algorithm–support vector regression

A load location identification method of fiber optic smart structures based on a prototype data acquisition system is proposed. It employs a genetic algorithm–support vector regression to estimate the X, Y coordinates of loads loading on a composite panel. The panel is embedded with 8 sensors. When loads act on any position of smart composite structures, the output values of embedded fiber near the loading position will change. The 8 changed signals are the features of the genetic algorithm–support vector regression. The data acquisition center collects the features as the data samples. Data samples are divided into a training set, a validation set, and a testing set. Then a support vector regression model is established by using the training set and validation set, and the parameters are optimized by the genetic algorithm. The study compares the prediction accuracy between the genetic algorithm–support vector regression model and the back propagation model. The comparison results show that the prediction accuracy of the testing set was 85.78% and it is better than the back propagation neural network prediction model. This paper demonstrates that using the genetic algorithm–support vector regression model to identify loads is not only stable and feasible but also has high precision. The presented method in this paper is significant for the health monitoring and damage identification of composite materials in the future.

Application of genetic algorithm-support vector regression model to predict damping of cantilever beam with particle damper

The performance of particle damper is strongly nonlinear, and the energy dissipation is derived from a combination of mechanisms including plastic collisions and friction between the particles and the walls and between the particles themselves. An optimized support vector regression model is built to predict the damping ratio of cantilever beam with particle damper. Then, the optimal parameters are adopted to construct the support vector regression models. In addition, genetic algorithm is used to select the optimal variables so as to improve the predictive ability of the models. Cross validation combined with support vector regression is used in this research and is compared with the genetic algorithm-support vector regression method. Genetic algorithm-support vector regression as research object to compare with the combination of cross validation and support vector regression. The experimental results demonstrate that the proposed genetic algorithm-support vector regression model provides better prediction capability. Therefore, the genetic algorithm-support vector regression model is proven to be an effective approach to predict the damping ratio of cantilever beam with particle damper.

Optimization enhanced genetic algorithm-support vector regression for the prediction of compound retention indices in gas chromatography

A new method using genetic algorithm and support vector regression with parameter optimization (GA–SVR–PO) was developed for the prediction of compound retention indices (RI) in gas chromatography. The dataset used in this work consists of 252 compounds extracted from the Molecular Operating Environment (MOE) boiling point database. Molecular descriptors were calculated by descriptor tools of the MOE software package. After removing redundant descriptors, 151 descriptors were obtained for each compound. A genetic algorithm (GA) was used to select the best subset of molecular descriptors and the best parameters of SVR to optimize the prediction performance of compound retention indices. A 10-fold cross-validation method was used to evaluate the prediction performance. We compared the performance of our proposed model with three existing methods: GA coupled with multiple linear regression (GA–MLR), the subset selected by GA–MLR used to train SVR (GA–MLR–SVR), and GA on SVR (GA–SVR). The experimental results demonstrate that our proposed GA–SVR–PO model has better predictive performance than other existing models with R2>0.967 and RMSE=49.94. The prediction accuracy of GA–SVR–PO model is 96% at 10% of prediction variation.

Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices

This paper concentrates on the modelling and trading of three daily market implied volatility indices issued on the Chicago Board Options Exchange (CBOE) using evolving combinations of prominent autoregressive and emerging heuristics models, with the aims of introducing an algorithm that provides a better approximation of the most popular U.S. volatility indices than those that have already been presented in the literature and determining whether there is the ability to produce profitable trading strategies. A heterogeneous autoregressive process (HAR) is combined with a genetic algorithm–support vector regression (GASVR) model in two hybrid algorithms. The algorithms’ statistical performances are benchmarked against the best forecasters on the VIX, VXN and VXD volatility indices. The trading performances of the forecasts are evaluated through a trading simulation based on VIX and VXN futures contracts, as well as on the VXZ exchange traded note based on the S&P 500 VIX mid-term futures index. Our findings indicate the existence of strong nonlinearities in all indices examined, while the GASVR algorithm improves the statistical significance of the HAR processes. The trading performances of the hybrid models reveal the possibility of economically significant profits.

Prediction of asphaltene precipitation using support vector regression tuned with genetic algorithms

Abstract Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry, developing a quick and accurate model, to predict the asphaltene precipitation under different conditions, seems crucial. In this study, a new model, namely genetic algorithm – support vector regression (GA-SVR) is proposed, which is applied to predict the amount of asphaltene precipitation. GA is used to select the best optimal values of SVR parameters and kernel parameter, simultaneously, to increase the generalization performance of the SVR. The GA-SVR model is trained and tested on the experimental data sets reported in literature. The performance of the GA-SVR model is compared with two scaling equation models, using statistical error measures and graphical analyses. The results show that the prediction performance of the proposed model, is highly reliable and satisfactory.

Artificial intelligence metamodel comparison and application to wind turbine airfoil uncertainty analysis

The Monte Carlo simulation method for turbomachinery uncertainty analysis often requires performing a huge number of simulations, the computational cost of which can be greatly alleviated with the help of metamodeling techniques. An intensive comparative study was performed on the approximation performance of three prospective artificial intelligence metamodels, that is, artificial neural network, radial basis function, and support vector regression. The genetic algorithm was used to optimize the predetermined parameters of each metamodel for the sake of a fair comparison. Through testing on 10 nonlinear functions with different problem scales and sample sizes, the genetic algorithm–support vector regression metamodel was found more accurate and robust than the other two counterparts. Accordingly, the genetic algorithm–support vector regression metamodel was selected and combined with the Monte Carlo simulation method for the uncertainty analysis of a wind turbine airfoil under two types of surface roughness uncertainties. The results show that the genetic algorithm–support vector regression metamodel can capture well the uncertainty propagation from the surface roughness to the airfoil aerodynamic performance. This work is useful to the application of metamodeling techniques in the robust design optimization of turbomachinery.

Forecasting of chlorophyll-a concentrations in South San Francisco Bay using five different models

Accurate and reliable eutrophication level forecasting models are necessary for characterizing complicated water quality processes in bays. In this study, the ability of coupled discrete wavelet transform (DWT) with artificial neural network (ANN) and multi linear regression (MLR) (WANN and WMLR), ANN, MLR and genetic algorithm-support vector regression (GA-SVR) models for chlorophyll-a level forecasting applications were considered. The data used to develop and validate the models were monthly chlorophyll-a (Chl-a) data recorded from January 1994 to December 2013 were obtained from the NO.36 station located in the South San Francisco bay, USA. In the proposed WANN and WMLR models, the observed time series of Chl-a were decomposed to sub time series at different scales by DWT. Afterwards, the sub time series were used as input data to the ANN and MLR systems to predict the 1 month ahead Chl-a. Also the genetic algorithm was linked to SVR models to search for the optimal SVR parameters. The relative performance of the proposed models was compared together and the results showed that the WANN models were found to provide more accurate monthly Chl-a forecasts compared to the other models. The determination coefficient was 0.87, −0.04, 0.31, −2.36 and 0.24 for the WANN, WMLR, ANN, MLR and GA-SVR models, respectively. In addition, the WANN model predicted extreme Chl-a values precisely. The results indicate that the WANN models are a promising new method for eutrophication level forecasting in bays such as those found in South San Francisco Bay.