Longitudinal dispersion is the key hydrologic 'process that governs transport of pollutants in natural streams. It is critical for spill action centers to be able to predict the pollutant travel time and breakthro...
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Longitudinal dispersion is the key hydrologic 'process that governs transport of pollutants in natural streams. It is critical for spill action centers to be able to predict the pollutant travel time and breakthrough curves accurately following accidental spills in urban streams. This study presents a novel geneexpression model for longitudinal dispersion developed using 150 published data sets of geometric and hydraulic parameters in natural streams in the United States, Canada, Europe, and New Zealand. The training and testing of the model were accomplished using randomly-selected 67% (100 data sets) and 33% (50 data sets) of the data sets, respectively. gene expression programming (GEP) is used to develop empirical relations between the longitudinal dispersion coefficient and various control variables, including the Froude number which reflects the effect of reach slope, aspect ratio, and the bed material roughness on the dispersion coefficient. Two GEP models have been developed, and the prediction uncertainties of the developed GEP models are quantified and compared with those of existing models, showing improved prediction accuracy in favor of GEP models. Finally, a parametric analysis is performed for further verification of the developed GEP models. The main reason for the higher accuracy of the GEP models compared to the existing regression models is that exponents of the key variables (aspect ratio and bed material roughness) are not constants but a function of the Froude number. The proposed relations are both simple and accurate and can be effectively used to predict the longitudinal dispersion coefficients in natural streams. (C) 2015 Elsevier B.V. All rights reserved.
Precise evaluation of pure hydrocarbon and natural gas viscosity is vital for reliable reservoir characterization, simulation, transportation and optimum consumption. The most trustable sources of pure hydrocarbon and...
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Precise evaluation of pure hydrocarbon and natural gas viscosity is vital for reliable reservoir characterization, simulation, transportation and optimum consumption. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. The need of new methods becomes important when there is not enough experimental data for specific composition, pressure, and temperature conditions. In this study, a promising approach is utilized for the prediction of viscosities of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide, nitrogen, helium, and hydrocarbon sulfide using over 3800 data sets. gene expression programming (GEP) is employed to develop a general model for pure and natural gas viscosity. The proposed model is a function of pseudo reduced pressure, pseudo reduced temperature, molecular weight and density. In addition, comparative studies are performed between the results obtained by the GEP model and previously published empirical correlations. To this end, statistical and graphical error analyses are used simultaneously. The results obtained show a value of 4.9% for average absolute percent relative error which is a measure of relative absolute deviation from the experimental data. The results also propose that standard deviation as a sign of data scattering is only 0.0870. These observations illustrate that the GEP model is more robust, reliable and consistent than the existing correlations for prediction of pure and natural gas viscosity. Finally, the relevancy factor shows that molecular weight has the greatest effect on gas viscosity. (C) 2015 Elsevier B.V. All rights reserved.
gene expression programming (GEP) is a new technique of evolutionary algorithm that implements genome/phoneme representation in computing programs. Due to its power in global search, it is widely applied in symbolic r...
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gene expression programming (GEP) is a new technique of evolutionary algorithm that implements genome/phoneme representation in computing programs. Due to its power in global search, it is widely applied in symbolic regression. However, little work has been done to apply it to real parameter optimization yet. This paper proposes a real parameter optimization method named Uniform-Constants based GEP (UC-GEP). In UC-GEP, the constant domain directly participates in the evolution. Our research conducted extensive experiments over nine benchmark functions from the IEEE Congress on Evolutionary Computation 2005 and compared the results to three other algorithms namely Meta-Constants based GEP (MC-GEP), Meta-Uniform-Constants based GEP (MUC-GEP), and the Floating Point genetic Algorithm (FP-GA). For simplicity, all GEP methods adopt a one-tier index gene structure. The results demonstrate the optimal performance of our UC-GEP in solving multimodal problems and show that at least one GEP method outperforms FP-GA on all test functions with higher computational complexity. (C) 2008 Elsevier B. V. All rights reserved.
In this paper, two soft computing approaches, which are known as artificial neural networks and gene expression programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in...
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In this paper, two soft computing approaches, which are known as artificial neural networks and gene expression programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The parameters, "ultrasound pulse velocity", "water absorption", "dry density", "saturated density", and "bulk density" which are experimentally determined based on the procedures given in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict "uniaxial compressive strength" and "tensile strength" of Gaziantep basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The results obtained are also useful in characterizing the Gaziantep basalts for practical applications.
gene expression programming (GEP) is a powerful tool widely used in function mining. However, it is difficult for GEP to generate appropriate numeric constants for function mining. In this paper, a novel approach of c...
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ISBN:
(纸本)9781424445189
gene expression programming (GEP) is a powerful tool widely used in function mining. However, it is difficult for GEP to generate appropriate numeric constants for function mining. In this paper, a novel approach of creating numeric constants, GEPPSO, was proposed, which embedded Particle Swarm Optimization (PSO) into GEP. In the approach, the evolutionary process was divided into 2 phases: in the first phase, GEP focused on optimizing the structure of function expression, and in the second one, PSO focused on optimizing the constant parameters. The experimental results on function mining problems show that the performance of GEPPSO is better than that of the existing GEP Random Numerical Constants algorithm (GEP-RNC).
There has been number of measurement techniques proposed in the literature. These metrics can be used in assessing quality of software products, thereby controlling costs and schedules. ne empirical validation of obje...
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ISBN:
(纸本)9783642021510
There has been number of measurement techniques proposed in the literature. These metrics can be used in assessing quality of software products, thereby controlling costs and schedules. ne empirical validation of object-oriented (OO) metrics is essential to ensure their practical relevance in industrial settings. In this paper, we empirically validate OO metrics given by Chidamber and Kemerer for their ability to predict software quality in terms of fault proneness. In order to analyze these metrics we use gene expression programming (GEP). Here, we explore the ability of OO metrics using defect data for open source software. Further, we develop a software quality metric and suggest ways in which software professional may use this metric for process improvement. We conclude that GEP can be used in detecting fault prone classes. We also conclude that the proposed metric may be effectively used by software managers tin predicting faulty classes in earlier phases of software development.
Finding functions whose accuracies change significantly between two classes is an interesting work. In this paper, this kind of functions is defined as class contrast functions. As gene expression programming (GEP) ca...
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ISBN:
(纸本)9783642033476
Finding functions whose accuracies change significantly between two classes is an interesting work. In this paper, this kind of functions is defined as class contrast functions. As gene expression programming (GEP) can discover essential relations from data and express them mathematically, it is desirable to apply GEP to mining such class contrast functions from data. The main contributions of this paper include: (1) proposing a new data mining task - class contrast function mining, (2) designing a GEP based method to find class contrast functions, (3) presenting several strategies for finding multiple class contrast functions in data, (4) giving an extensive performance study on both synthetic and real world datasets. The experimental results show that the proposed methods are effective. Several class contrast functions are discovered from the real world datasets. Some potential works on class contrast function mining are discussed based on the experimental results.
In previous studies, artificial neural networks have been used to develop a model that combines simulated river flows from several individual rainfall-runoff models e. g. Shamseldin et al., 1997;Abrahart & See, 20...
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ISBN:
(纸本)9780975840078
In previous studies, artificial neural networks have been used to develop a model that combines simulated river flows from several individual rainfall-runoff models e. g. Shamseldin et al., 1997;Abrahart & See, 2002;Shamseldin, O'Connor, & Nasr, 2007. The combined runoff estimate model was found to perform better than the individual models in most of the cases. However, no attempts have been made to explain the inner workings of the combined models or the drivers for their success. The research presented in this study investigates the use of gene expression programming (GEP) to develop a combination rainfall-runoff model through the process of symbolic regression. One of the additional advantages of this approach over the neural combination method is the model's ability to represent itself in the form of mathematical expressions. The GEP model is developed using the daily simulated river flows of four other rainfall runoff models for the Chu catchment which is located in Vietnam. The four models are the linear perturbation model (LPM), the linearly varying gain factor model (LVGFM), the probability-distributed interacting storage capacity (PDISC) model, and the soil moisture accounting and routing (SMAR) models. In this paper, geneXproTools 4.0, a powerful soft computing software package, is used to develop the combined model. The program provides transparent modeling solutions in the sense that it provides the users with the mathematical equation describing the combined model. The results reveal that combination using symbolic regression is successful and that a superior combined model can be developed using outputs from other individual models. The structure of the combined model is also investigated in this study. The results show that the combined model is dominated by input information from the PDISC model forming the baseline estimate, to which different permutations and combinations of the remaining inputs from the other models are added. This research, limit
An Improved gene expression programming(IGEP) is proposed in this paper. It has some new features: 1) introducing a new individual coding;2) introducing a new way of creating constants;3) introducing a hybrid self-ada...
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ISBN:
(纸本)9780769536453
An Improved gene expression programming(IGEP) is proposed in this paper. It has some new features: 1) introducing a new individual coding;2) introducing a new way of creating constants;3) introducing a hybrid self-adaptive crossover-mutation operator, which can enhance the search ability and exploit the optimum offspring. To validate the performance of IGEP, this paper applies IGEP into the solution of the macro-economic predictions. The experimental results demonstrate that Improved GEP can automatically find better Optimization Model, based on which prediction will be generated much more exactly.
In this study, a nonlinear quantitative structure–activity relationship model for the prediction of the IC of 6-alkenylamides of 4-anilinothieno [2, 3-d] pyrimidine as epidermal growth factor receptor（EGFR） inhibit...
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In this study, a nonlinear quantitative structure–activity relationship model for the prediction of the IC of 6-alkenylamides of 4-anilinothieno [2, 3-d] pyrimidine as epidermal growth factor receptor（EGFR） inhibitors was developed by the gene expression programming（GEP）. This model is based on five descriptors which were selected from all the descriptors by heuristic method（HM）. GEP rendered a good correlation between the experimental and predicted log IC values with a correlation coefficient and a mean error of 0.87 and 0.14 for the training set, 0.71 and 0.41 for the test set, respectively. The results indicate that this QSAR model has good predictive capability and it shows that there is further study we can operate on EGFR inhibitors.
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