This study presents the development of a new model obtained from the correlation of dynamic input and SPT data with pile capacity. An evolutionary algorithm, gene expression programming (GEP), was used for modelling t...
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This study presents the development of a new model obtained from the correlation of dynamic input and SPT data with pile capacity. An evolutionary algorithm, gene expression programming (GEP), was used for modelling the correlation. The data used for model development comprised 24 cases obtained from existing literature. The modelling was carried out by dividing the data into two sets: a training set I'm model calibration and a validation set for verifying the generalization capability of the model. The performance of the model was evaluated by comparing its predictions of pile capacity with experimental data and with predictions of pile capacity by two commonly used traditional methods and the artificial neural networks (ANNs) model. It was found that the model performs well with a coefficient of determination, mean, standard deviation and probability density at 50% equivalent to 0.94, 1.08, 0.14, and 1.05, respectively, for the training set, and 0.96, 0.95, 0.13, and 0.93, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the model is accurate in predicting pile capacity. The results of comparison also showed that the model predicted pile capacity more accurately than traditional methods including the,ANNs model. (C) 2014 The Japanese Geotechnical Society. Production and hosting by Elsevier By. All rights reserved.
This study presents the gene expression programming (GEP) soft computing technique. as a new tool for the formulation of the Austenite finish (A(f)) temperature of Cu-based shape memory alloys (SMA) for various compos...
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This study presents the gene expression programming (GEP) soft computing technique. as a new tool for the formulation of the Austenite finish (A(f)) temperature of Cu-based shape memory alloys (SMA) for various compositions and heat treatments. The objective of this study is to provide an alternative formulation to related design composition and verify the robustness of GEP for the formulation of such structural problems. The training and testing patterns of the proposed GEP formulation is based on well established experimental results from the literature. The GEP based formulation results are compared with the experimental results and found to be quite reliable.
Small-data problems are commonly encountered in the early stages of a new manufacturing procedure, presenting challenges to both academics and practitioners, as good performance is difficult to achieve with learning m...
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Small-data problems are commonly encountered in the early stages of a new manufacturing procedure, presenting challenges to both academics and practitioners, as good performance is difficult to achieve with learning models when there is a lack of sufficient data. Virtual sample generation (VSG) has been shown to be an effective method to overcome this issue in a wide range of studies in various fields. Such works usually assume that the relations among attributes are independent of each other, and produce synthetic data by using sample distributions of these. However, the VSG technique may be ineffective if the real data has interrelated attributes. Therefore, this research provides a novel procedure to generate related virtual samples with non-linear attribute dependency. To construct a relational model between the independent and dependent attributes, we employ gene expression programming (GEP) to find the most suitable mathematical model. One practical dataset and three real UCI datasets are presented in this paper to verify the effectiveness of the proposed method, and the results show that the proposed approach has better learning accuracy with regard to a back-propagation neural (BPN) network than that of the well-known mega-trend-diffusion (MTD) and the multi regression analysis (MRA) approaches. (C) 2014 Elsevier B.V. All rights reserved.
Maternal obesity and overconsumption of saturated fats during pregnancy have profound effects on offspring health, ranging from metabolic to behavioral disorders in later life. The influence of high-fat diet (HFD) exp...
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Maternal obesity and overconsumption of saturated fats during pregnancy have profound effects on offspring health, ranging from metabolic to behavioral disorders in later life. The influence of high-fat diet (HFD) exposure on the development of brain regions implicated in anxiety behavior is not well understood. We previously found that maternal HFD exposure is associated with an increase in anxiety behavior and alterations in the expression of several genes involved in inflammation via the glucocorticoid signaling pathway in adult rat offspring. During adolescence, the maturation of feedback systems mediating corticosteroid sensitivity is incomplete, and therefore distinct from adulthood. In this study, we examined the influence of maternal HFD on several measures of anxiety behavior and geneexpression in adolescent offspring. We examined the expression of corticosteroid receptors and related inflammatory processes, as corticosteroid receptors are known to regulate circulating corticosterone levels during basal and stress conditions in addition to influencing inflammatory processes in the hippocampus and amygdala. We found that adolescent animals perinatally exposed to HFD generally showed decreased anxiety behavior accompanied by a selective alteration in the expression of the glucocorticoid receptor and several downstream inflammatory genes in the hippocampus and amygdala. These data suggest that adolescence constitutes an additional period when the effects of developmental programming may modify mental health trajectories. (C) 2014 IBRO. Published by Elsevier Ltd. All rights reserved.
This paper addresses the problem of creating a new classifier as highly interpretable fuzzy rule-based system, based on the analytical theory of fuzzy modeling and gene expression programming. This approach is applied...
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ISBN:
(纸本)9783319071756;9783319071763
This paper addresses the problem of creating a new classifier as highly interpretable fuzzy rule-based system, based on the analytical theory of fuzzy modeling and gene expression programming. This approach is applied to solve the prediction problem of peri-operative complications of radical hysterectomy in patients with cervical cancer. The developed classifier has the form of the set of fuzzy metarules, which are readable for the medical community, and additionally, is accurate enough. The consequents of the metarules describe the presence or absence of peri-operative complications. For the construction of the classifier we can use the fuzzified, binarized or both types of the attributes. We also compare the efficiency of our model with the decision trees and C5 algorithm.
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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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.
Clustering is one of the main methods in data mining. Many clustering algorithms have been proposed so far. Among them, GEP-Cluster, a single-objective clustering algorithm, can automatically cluster with unknown clus...
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ISBN:
(纸本)9781479966219
Clustering is one of the main methods in data mining. Many clustering algorithms have been proposed so far. Among them, GEP-Cluster, a single-objective clustering algorithm, can automatically cluster with unknown clustering number. However, it is difficult for GEP-Cluster to find the high-quality solution in the limited search space. Aiming at the problems, a multi-objective clustering algorithm based on gene expression programming, MOGEP-Cluster, is proposed in this paper. To validate the effectiveness of MOGEP-Cluster, a set of experiments are performed on 5 benchmark datasets. The experimental results show that MOGEP-Cluster can find better solutions than GEP-Cluster.
Time series prediction has been widely used in various fields. GEP is one of the popular methods for time series analysis. However, the GEP-based prediction models contain only one single function. To accurately captu...
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ISBN:
(纸本)9781479958252
Time series prediction has been widely used in various fields. GEP is one of the popular methods for time series analysis. However, the GEP-based prediction models contain only one single function. To accurately capture the dynamic behavior of time series, this study develops a system which integrates multiple functions in a GEP-based model for time series prediction. The weight of each function is determined by the accuracy of its last prediction. In addition, a light local search is applied to adjust the function weights. The experimental results show that the proposed system outperforms several GEP-based approaches.
gene expression programming(GEP) is a powerful tool widely used in function ***,it is difficult for GEP to generate appropriate numeric constants for function mining,In this paper,a novel approach of creating numeric ...
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gene expression programming(GEP) is a powerful tool widely used in function ***,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).
In this paper we aim to infer a model of genetic networks from time series data of geneexpression profiles by using a new gene expression programming algorithm. geneexpression networks are modelled by differential e...
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ISBN:
(纸本)9781920682729
In this paper we aim to infer a model of genetic networks from time series data of geneexpression profiles by using a new gene expression programming algorithm. geneexpression networks are modelled by differential equations which represent temporal geneexpression relations. gene expression programming is a new extension of genetic programming. Here we combine a local search method with gene expression programming to form a memetic algorithm in order to find not only the system of differential equations but also fine tune its constant parameters. The effectiveness of the proposed method is justified by comparing its performance with that of conventional genetic programming applied to this problem in previous studies.
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