The present investigation introduces an optimal computational model by comparing gene expression programming (GEP), least square support vector machine (LSSVM), and extreme learning machine (ELM) models in predicting ...
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The present investigation introduces an optimal computational model by comparing gene expression programming (GEP), least square support vector machine (LSSVM), and extreme learning machine (ELM) models in predicting the uniaxial compressive strength of intact rocks (RUCS). This research employs linear, polynomial, and radial basis function (RBF) kernel-based LSSVM models and compares them. Furthermore, this investigation reveals the effect of chromosomes on the performance of GEP models. One hundred four and 27 results of RUCS have trained and tested RUCS models. In addition, the multicollinearity has been computed for an overall database to analyze its effect on the model's performance and accuracy. This research uses the area and mass of rock specimens, along with Young's modulus, for the first time in predicting the rock UCS. The performance metric comparison reveals that model ELM (mentioned by RUCS7) has predicted rock UCS with a correlation coefficient of 0.9642, root mean square error of 0.0479 MPa, performance index of 1.8067, variance accounted for of 92.49, and agreement index of 0.8681, comparatively higher than LSSVM and GEP models and close to ideal values. The performance analysis reveals that weak multicollinearity affects the prediction capabilities of the linear LSSVM model. Conversely, it has been observed that the multicollinearity effect can be controlled at a certain level by implementing more chromosomes in the GEP model. The area, mass, and Young's modulus of rock specimens highly influence the prediction of RUCS. Finally, the Wilcoxon test (confidence interval = 0.1450), uncertainty analysis (rank = 1), score analysis (overall = 238), and generalizability (approximate to ideal values) demonstrate the ELM model as an optimal computational model for assessing the rock UCS.
Wireless Sensor Networks (WSNs) are widely used in detecting, locating and tracking the moving objects. However, Some of the cheap, low-powered and energy-limited sensors that are deployed in large areas may use up th...
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ISBN:
(纸本)9781424454686
Wireless Sensor Networks (WSNs) are widely used in detecting, locating and tracking the moving objects. However, Some of the cheap, low-powered and energy-limited sensors that are deployed in large areas may use up their energy, which leads to the whole network failure finally. In order to reduce the energy consumption and prolong the network lifetime, (a) a new light-weight and energy-efficient locating scheme is proposed to estimate the current target location;(b) an energy-efficient parallel target tracking algorithm based on gene expression programming (P-GEP) is put forward for collaboratively mining the trajectory of the moving target, then, the future locations where the target will appear can be predicted within a given prediction accuracy, and sensor nodes that are far away from the predicted locations can be scheduled to be on/off finally;(c) the sliding window technique is adopted to discard some of the historical locations to balance the trade-off between the prediction accuracy and the energy consumption during the trajectory mining process. Extensive simulations show that the proposed methods can greatly improve the tracking efficiency and extend the network lifetime by around 39.4% and 94.2% compared with other tracking algorithms, i.e., EKF and ECPA.
In this paper we propose a new incremental gene expression programming (GEP) ensemble classifier. Our base classifiers are induced from a chunk of data instances using GEP. Size of the chunk controls the number of ins...
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ISBN:
(纸本)9783319594217;9783319594200
In this paper we propose a new incremental gene expression programming (GEP) ensemble classifier. Our base classifiers are induced from a chunk of data instances using GEP. Size of the chunk controls the number of instances with known class labels used to induce base classifiers iteratively. Instances with unknown class label are classified in sequence, one by one. It is assumed that after a decision as to the class label of the new instance has been taken its true class label is revealed. From a set of base classifier a metagene is induced and used to predict class label of instances with unknown class labels. To validate the approach an extensive computational experiment has been carried-out.
In this paper, a new gene expression programming (GEP) algorithm is proposed, which increase "inverted series" and "extract" operator. The new algorithm can effectively increase the rate of utiliza...
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ISBN:
(纸本)9789811003561;9789811003554
In this paper, a new gene expression programming (GEP) algorithm is proposed, which increase "inverted series" and "extract" operator. The new algorithm can effectively increase the rate of utilization of genes, with convergence speed and solution precision is higher. Taking the Chinese vegetables price change trend of mooli, scallion as example, and discuss the way to solve the forecasting modeling problem by adopting GEP. The experimental results show that the new GEP Algorithm can not only increase the diversity of population but overcome the shortage of primitive GEP. In addition, it can improve convergence accuracy compared to original GEP.
Vegetation is defined as a kind of surface roughness, which reduces the capacity of the channel and retards the flow by causing loss of energy through turbulence and drag forces of moving water. In water channels, veg...
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Vegetation is defined as a kind of surface roughness, which reduces the capacity of the channel and retards the flow by causing loss of energy through turbulence and drag forces of moving water. In water channels, vegetation maybe used to stabilize the water surface and prevent erosion caused by concentrated water flow. In terms of channel lining, grass offers the least expensive option and is the most esthetically pleasing. Most previous studies on this subject were conducted using artificial vegetation in laboratory experiments;research conducted in canals with natural vegetation is lacking. In this study, flow characteristics through a natural grassed canal are studied. In addition, the Manning coefficient and specific energy were determined based on field measurements for both grassed and un-grassed canals. The fieldwork was conducted on Ganabia 9B, southeast of El-Mahalla El Kubra, El-Gharbia, Egypt. The average heights of vegetation in the grassed canal ranged from (28 to 100) cm and the values of flow rate range from (0.0504 to 0.1127) m(3)/s. The number of tests is 42 tests for the un-grassed canal and 205 tests for the grassed canal. This study indicated that the Manning coefficient increased with an increase in energy and momentum coefficients. The energy coefficient increased with a decrease in relative specific energy and an increase in grass height. The un-grassed canal exhibited a smaller relative specific energy than the grassed canal. The momentum coefficients for the non-grassed and grassed canals were 1.015-1.517 and 1.003-1.655, respectively. Therefore, the un-grassed canal showed a higher momentum coefficient than the grassed canal at a ratio of 0.264-1.196%. The relative specific energy was calculated in two ways: (i) using the actual energy coefficient values and (ii) setting it equal to one. As such, the error percentages of the specific energy for the grassed and un-grassed canals were 0.000105-0.1652% and 0.0048-0.2194%, respectively. The re
In the field of swarm intelligence, it is usually complicated to express the logic of swarm behaviors. Behavior tree has drawn a lot of attention to be a practical approach to solving this problem in recent years. How...
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ISBN:
(纸本)9781728185262
In the field of swarm intelligence, it is usually complicated to express the logic of swarm behaviors. Behavior tree has drawn a lot of attention to be a practical approach to solving this problem in recent years. However, how to automatically design the logic of swarm behaviors according to the target of a task is the focus of swarm intelligence. Hence, we propose an automatic optimizing framework named Benign which is capable of using gene expression programming (GEP) to optimize the logic of swarm behaviors. In Benign, the basic swarm behaviors and the relationships among those behaviors are mapped to nodes of behavior tree by the method named Matt firstly. With these nodes, we design an artificial behavior tree. After that, the artificial behavior tree is transformed into an expression tree in GEP according to the method named Meet. Finally, GEP is used for optimization to generate the expected logic of swarm behaviors. We conduct simulation experiments to validate the efficiency of Benign. The experimental results show the superiority of Benign. Compared with the logic of the artificial behavior tree before optimization, the conduction of the optimized logic of swarm behaviors increases efficiency by more than 50%.
Neuro-encoded expressionprogramming (NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. genetic programming with linear rep...
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ISBN:
(纸本)9783030304843;9783030304836
Neuro-encoded expressionprogramming (NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. genetic programming with linear representation uses nature-inspired operators (e.g., crossover, mutation) to tune expressions and finally search out the best explicit function to simulate data. The encoding mechanism is essential for genetic programmings to find a desirable solution efficiently. However, the linear representation methods manipulate the expression tree in discrete solution space, where a small change of the input can cause a large change of the output. The unsmooth landscapes destroy the local information and make difficulty in searching. The neuro-encoded expressionprogramming constructs the gene string with recurrent neural network (RNN) and the weights of the network are optimized by powerful continuous evolutionary algorithms. The neural network mappings smoothen the sharp fitness landscape and provide rich neighborhood information to find the best expression. The experiments indicate that the novel approach improves training efficiency and reduces test errors on several well-known symbolic regression problems.
Pressuremeter is one of the most reliable geotechnical in situ tests to be utilized in estimating different soil properties. The main parameters that can be obtained from this test are soil deformation modulus. In gen...
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Pressuremeter is one of the most reliable geotechnical in situ tests to be utilized in estimating different soil properties. The main parameters that can be obtained from this test are soil deformation modulus. In general, the pressuremeter test is time-consuming and costly that requires suitable equipment and experienced operators. With these limitations, it is necessary to introduce models for indirect determination of the pressuremeter modulus (E-PM). Based on the literature, various models and equations have been proposed to predict the modulus of pressuremeter;however, most of them are for fine-grained soils. In this paper, multiple linear regression (MLR), artificial neural network (ANN), and gene expression programming (GEP) have been used to design new relationships for prediction of the pressuremeter modulus. The main difference between this research and other studies is the use of 62 pre-bored pressuremeter tests results in cohesive and granular soils for training and testing models. The study area is in Tehran where soils show a variation from low plasticity clay to clayey gravel. The moisture content, depth of test, and grain size distribution of soils are considered as independent variables. Comparison of the predicted E-PM with the actual modulus obtained from the pressuremeter tests indicates that the proposed relationships are able to estimate the pressuremeter modulus well. The results showed that the relationships obtained from nonlinear analyses performed by smart methods are more accurate and have less error in comparison to MLR method. Comparison of the coefficient of determination (R-2) values and errors related to them in the testing phase show that the obtained results from the GEP method are more reliable than the ANN method. Also, sensitivity analysis revealed that the soil moisture content is the most effective parameter among the variables on the pressuremeter modulus.
This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective...
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With the widespread access of all kinds of distributed equipment including distributed energy, and components, distribution network is moving in the direction of the active distribution network. However, compared to d...
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ISBN:
(纸本)9781538619568
With the widespread access of all kinds of distributed equipment including distributed energy, and components, distribution network is moving in the direction of the active distribution network. However, compared to distributed network, complex access environment, flexible access mode, massive access terminal, dynamic and distributed mass data in active distribution network will bring new challenge to security of data transmission. Security protection on data transmission cannot well meet the needs of many practical applications. To this end, based on the characteristics of fusion and interaction between information system and physical system for active distribution network, we analyzed the domestic and international related research condition, explored research framework and technical route of secure and efficient data transmission for active distribution networks. On this basis, we discussed key technologies and challenges in depth, and pointed that the related research will improve security and efficiency of data transmission in active distribution network and have an important research value and social significance.
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