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IEEE International Conference on Cybernetics (CYBCONF)

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat Wita Stwosza 57 PL-80952 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)9781467364690

The paper aims at comparing and evaluating combined classifiers constructed from expression trees induced through applying gene expression programming. Several combined classifiers proposed in earlier papers of the authors are briefly described and explained. An extensive computational experiment in which the proposed combined classifiers are compared with a number of well-known classification algorithms from WEKA Environment shows that constructing combined classifiers taking advantage of the collective computational techniques brings a very good result in terms of the solution quality.

关键词： combined classifier expression tree gene expression programming

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Study on Application of Fast Intrinsic Mode Decomposition to Short-term Load Forecasting

Study on Application of Fast Intrinsic Mode Decomposition to...

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4th International Conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Wang, Shuzhong Fan, Xinqiao State Grid Elect Power Res Inst Beijing 100192 Peoples R China

ISBN: (纸本)9783037857243

The forecasting technique is one of the key factors for load forecasting. According to analysis, the Fast Intrinsic Mode Decomposition (FIMD) method is applied to short-term load forecasting in this paper. The specific implementation process of the proposed short-term load forecasting method is in the following. The selected load sample data are decomposed into a number of stationary Intrinsic Mode Functions (IMFs) with respective single mode by the FIMD method firstly. Then, each obtained load component with different frequency band is forecasted according to the gene expression programming (GEP) method by time-sharing. The final forecasting models are obtained by rebuilding the forecasting model of each IMF. Lots of virtual forecasting tests are done, and it proves that the proposed load forecasting method based on the FIMD method in this paper is more accurate than the method based on Empirical Mode Decomposition (EMD).

关键词： fast intrinsic mode decomposition gene expression programming short-term load forecasting power system

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Estimation of dimension and time variation of local scour at short abutment

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INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT 2013年第1期11卷 121-135页

作者： Mohammadpour, Reza Ab Ghani, Aminuddin Azamathulla, Hazi Mohammad Univ Sains Malaysia River Engn & Urban Drainage Res Ctr REDAC Engn CampusSeri Ampangan Nibong Tebal 14300 Penang Malaysia

Accurate prediction of the local scour at abutments is an important criterion to design a safe depth for the bridge foundation. In this paper, the dimension and variation of local scour with time at a vertical-wall abutment were investigated experimentally under clear-water conditions. The multiple linear regression (MLR), gene expression programming (GEP) and artificial neural networks (ANNs), feed forward back propagation and radial basis function were used to predict the time variation of scour depth at a short abutment. Results indicated that the dimension of the scour hole in the x-direction ranged from 3L to 5L upstream and downstream of the abutment, respectively, and also 4L in the y-direction. Statistical analysis showed that, although the ANNs technique produced better results (R-2 = 0.997, RMSE = 0.0113 and MAE = 0.0071) in comparison with the GEP (R-2 = 0.959, RMSE = 0.068 and MAE = 0.044) and MLR techniques (R-2 = 0.958, RMSE = 0.059 and MAE = 0.041), both GEP and MLR are more practical methods. Finally, sensitivity analysis indicated that the local scour was greatly affected by the three studied parameters in the following order, time ratio (t/t(e)d). abutment length ratio (L/y). velocity ratio (U/U-c).

关键词： Abutment scour temporal scour scour hole dimension gene expression programming artificial neural networks

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Quantitative structure-activity relationship study on antitumour activity of a series of flavonoids

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MOLECULAR SIMULATION 2012年第1期38卷 38-44页

作者： Wang, Wei-Xuan Si, Hongzong Zhang, Ziding China Agr Univ Coll Biol Sci State Key Lab Agrobiotechnol Beijing 100193 Peoples R China Qingdao Univ Inst Computat Sci & Engn Lab New Fibrous Mat & Modern Text Growing Base State Key Lab Qingdao 266071 Shandong Peoples R China Lanzhou Univ Dept Chem Lanzhou 730000 Gansu Peoples R China

Flavonoids have been recognised as having antitumour activity for years, although the precise mechanism of this activity is not known. In this study, we used the quantitative structure-activity relationship (QSAR), a method of computer simulation, to investigate this mechanism. The heuristic method (HM), support vector machine (SVM) and gene expression programming (GEP) were employed to build the QSAR models between antitumour activity (i. e. survival percentage) of a series of flavonoids and five molecular descriptors derived from the CODESSA. The GEP, which was used for the first time to predict the antitumour activity of flavonoids, was found much more accurate than the HM and the SVM. The descriptors involved in the models are mainly electrical related, and the quantum chemical factors relevant to the oxygen atom may play an important role in antitumour activity of flavonoids. These models may provide helpful information for designing novel antitumoural compounds with higher bioactivity.

关键词： flavonoids gene expression programming heuristic method quantitative structure-activity relationship support vector machine

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Novel Approach to Strength Modeling of Concrete under Triaxial Compression

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JOURNAL OF MATERIALS IN CIVIL ENGINEERING 2012年第9期24卷 1132-1143页

作者： Gandomi, Amir Hossein Babanajad, Saeed Karim Alavi, Amir Hossein Farnam, Yaghoob Univ Akron Dept Civil Engn Akron OH 44325 USA Univ Tehran Coll Engn Sch Civil Engn Construct Mat Inst Tehran Iran Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

In this study, a robust variant of genetic programming, namely gene expression programming (GEP) was utilized to build a prediction model for the strength of concrete under triaxial compression loading. The proposed model relates the concrete triaxial strength to mix design parameters. A comprehensive database used for building the model was established on the basis of the results of 330 tests on concrete specimens under triaxial compression. To verify the predictability of the GEP model, it was employed to estimate the concrete strength of the specimens that were not included in the modeling process. Further, the model was externally validated using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting the concrete strength. The proposed model is effectively capable of evaluating the ultimate strength of concrete under triaxial compression loading. The derived model performs superior when compared with other empirical models found in the literature. The GEP-based design equation can readily be used for predesign purposes or may be used as a fast check on solutions developed by more in-depth deterministic analyses. DOI: 10.1061/(ASCE)MT.1943-5533.0000494. (c) 2012 American Society of Civil Engineers.

关键词： Compressive strength Triaxial compression Ultimate strength gene expression programming

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Soft computing approach to fault diagnosis of centrifugal pump

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APPLIED SOFT COMPUTING 2012年第5期12卷 1574-1581页

作者： Sakthivel, N. R. Nair, Binoy B. Sugumaran, V. Amrita Vishwa Vidyapeetham Dept Mech Engn Amrita Sch Engn Coimbatore 641105 Tamil Nadu India Karpagam Univ Coimbatore Tamil Nadu India Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Coimbatore 641105 Tamil Nadu India VIT Univ Dept Mech Engn Madras Tamil Nadu India

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Centrifugal pump gene expression programming Fault diagnosis Statistical features Decision tree algorithm Support vector machine Proximal support vector machine

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Predicting axial capacity of driven piles in cohesive soils using intelligent computing

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第3期25卷 618-627页

作者： Alkroosh, Iyad Nikraz, Hamid Curtin Univ Technol Dept Civil Engilneering Perth WA Australia

An accurate prediction of pile capacity under axial loads is necessary for the design. This paper presents the development of a new model to predict axial capacity of pile foundations driven into cohesive soils. gene expression programming technique (GEP) has been utilized for this purpose. The data used for development of the GEP model is collected from the literature and comprise a series of in-situ driven piles load tests as well as cone penetration test (CPT) results. The data are divided into two subsets: training set for model calibration and independent validation set for model verification. Predictions from the GEP model are compared with experimental data and with predictions of number of currently adopted CPT-based methods. The results have demonstrated that the GEP model performs well with coefficient of correlation, mean and probability density at 50% equivalent to 0.94, 0.96 and 1.01, respectively, indicating that the proposed model predicts pile capacity accurately. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Pile Capacity gene expression programming Training Validation CPT

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Prediction of splitting tensile strength from the compressive strength of concrete using GEP

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NEURAL COMPUTING & APPLICATIONS 2012年第8期21卷 1937-1945页

作者： Severcan, Metin Hakan Nigde Univ Dept Civil Engn TR-51240 Nigde Turkey

Splitting tensile strength is one of the important mechanical properties of concrete that is used in structural design. In this paper, it is aimed to propose formulation for predicting cylinder splitting tensile strength of concrete by using gene expression programming (GEP). The database used for training, testing, and validation sets of the GEP models is obtained from the literature. The GEP formulations are developed for prediction of splitting tensile strength of concrete as a function of water-binder ratio, age of specimen, and 100-mm cube compressive strength. The training and testing sets of the GEP models are randomly selected from the complete experimental data. The GEP formulations are also validated with additional experimental data except from the data used in training and testing sets of the GEP models. GEP formulations' results are compared with experimental results. Results of this study revealed that GEP formulations exhibited better performance to predict the splitting tensile strength of concrete.

关键词： gene expression programming Compressive and splitting tensile strength Water-binder ratio Age of specimen

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Analysis of Two Soft Computing Modeling Methodologies for Predicting Thickness Loss of Persian Hand-knotted Carpets

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FIBERS AND POLYMERS 2012年第5期13卷 675-683页

作者： Moghassem, A. R. Gharehaghaji, A. A. Najar, S. Shaikhzadeh Islamic Azad Univ Dept Text Engn Sci & Res Branch Tehran Iran Amirkabir Univ Technol Dept Text Engn Tehran Iran

Thickness loss caused by static and dynamic loads is one of the most important quality factors in carpets. This property is influenced by pile yarn characteristics and carpet construction parameters. Exploring the relationship between thickness loss and effective factors is highly significant to optimize the selection of the variables. Soft computing approaches, which are potent data-modeling tools in capturing complex input-output relationships, seem to be the powerful technique to decipher the above-mentioned relationship. This paper presents two different modeling methodologies for predicting thickness loss of Persian hand-knotted carpets. At first, several topologies with different architectures were used to get the best neural-network model. Since, ANN model is a black box and did not succeed in indicating inter-relationship between input and output parameters, gene expression programming (GEP) is presented here as another intelligent algorithm to predict thickness loss of the carpets. Our study showed that, GEP model has a significant priority over the ANN. The correlation coefficient (R-value) and mean square error (MSE) for GEP model were 0.950 and 0.219 respectively, while these parameters were 0.707 and 0.510 for ANN model. This indicates the desirable predictive power of GEP algorithm. Based on the proposed models, the dominant parameter on thickness loss found to be knot density and content of slipe wool.

关键词： Artificial neural network gene expression programming Soft computing approach Hand-knotted carpet Thickness loss

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Artificial intelligence based modeling for predicting the disinfection by-products in water

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2012年 114卷 122-131页

作者： Singh, Kunwar P. Gupta, Shikha CSIR Indian Inst Toxicol Res Div Environm Chem Lucknow 226001 Uttar Pradesh India

Formation of trihalomethanes (THMs) in chlorinated waters poses high risk to humans. Strategies for the THM control require pre-knowledge of their levels in the water. Determination of the THMs in the laboratory experiments is very tedious, expensive, and time consuming. Hence, the development of new predictive models for the THM formation in the chlorinated water will be of immense help. This research presents the potential of the artificial neural network (ANN), support vector machine (SVM), and gene expression programming (GEP) modeling approaches to forecast the THM formation due to chlorination. To develop the models, a total of 63 data collected from the literature were used, wherein five parameters, such as dissolved organic carbon normalized chlorine dose, water pH, temperature, bromide concentration, and contact time were used as the input variables. The predictive and generalization abilities of the models were comprehensively evaluated using several statistical tests. The results revealed that the ANN, SVM, and GEP models are capable of capturing the complex nonlinear relationship between the water disinfection conditions and the corresponding THM formation in the chlorinated water. The optimal ANN, SVM, and GEP models yielded the root mean square error and coefficient of determination values of 0.09 and 0.998;0.70 and 0.998;and 3.07 and 0.990 for training and 4.05 and 0.918;3.66 and 0.935;and 3.63 and 0.933 for validation set, respectively. Sensitivity analysis results revealed that initial pH, contact time and temperature were the most significant factors that influence the THM formation during chlorination process. All the three models provide fairly promising approach for the prediction of the THM formation in water during the disinfection process. SVM model performed relatively better than the ANN and GEP models. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Trihalomethanes Predictive modeling Artificial neural networks Support vector machines gene expression programming Sensitivity analysis

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