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2nd International Conference on Computer Engineering and Applications

作者： Javier, Oscar Lopez, Romero Fdn Univ Konrad Lorenz Software Engn Dept Cra 9 Bis 62-43 Bogota Colombia

ISBN: (纸本)9780769539829

Managing and arbitrating behaviours, processes and components in multilayered cognitive architectures when a huge amount of environmental variables are changing continuously with increasing complexity, ensue in a very comprehensive task. The presented framework proposes an hybrid cognitive architecture that relies on subsumption theory and includes some important extensions. These extensions can be condensed in inclusion of learning capabilities through bio-inspired reinforcement machine learning systems, an evolutionary mechanism based on gene expression programming to self-configure the behaviour arbitration between layers, a co-evolutionary mechanism to evolve behaviour repertories in a parallel fashion and finally, an aggregation mechanism to combine the learning algorithms outputs to improve the learning quality and increase the robustness and fault tolerance ability of the cognitive agent. The proposed architecture was proved in an animat environment using a multi-agent platform where several learning capabilities and emergent properties for self-configuring internal agent's architecture arise.

关键词： Intelligent Agents Cognitive Architectures Evolutionary Approaches Artificial Immune Systems gene expression programming

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Light-Weight Target Tracking in Dense Wireless Sensor Networks

Light-Weight Target Tracking in Dense Wireless Sensor Networ...

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21st International Joint Conference on Artificial Intelligence (IJCAI-09)

作者： Dai, Shucheng Chen, Chun Tang, Changjie Qiao, Shaojie Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China Sichuan Normal Univ Sch Business Chengdu 610101 Peoples R China

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.

关键词： gene expression programming Target Tracking Location Prediction Network Lifetime Wireless Sensor Network

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Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning

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TRANSPORTATION INFRASTRUCTURE GEOTECHNOLOGY 2024年第4期11卷 1989-2022页

作者： Khatti, Jitendra Grover, Kamaldeep Singh Rajasthan Tech Univ Dept Civil Engn Kota Rajasthan India

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.

关键词： Intact rock Multicollinearity Extreme learning machine Uniaxial compressive strength gene expression programming

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Effective Mining of Contrast Hybrid Patterns from Nominal-numerical Mixed Data 18th

Effective Mining of Contrast Hybrid Patterns from Nominal-nu...

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18th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Fu, Min Duan, Lei Yu, Zhenyang Sichuan Univ Sch Comp Sci Chengdu Peoples R China Sichuan Univ Med X Ctr Informat Chengdu Peoples R China

ISBN: (纸本)9783031220630;9783031220647

Contrast pattern mining, which finds patterns describing differences between two classes of data, is an important task in various scenarios. As real-world data is usually a mixture of nominal and numerical attributes (e.g., electronic medical records), contrast pattern mining algorithms over nominal-numerical mixed data are in great demand. Existing algorithms on contrast pattern mining either can only handle a single type of attribute or transform numerical attributes into nominal attributes with prior knowledge. However, these algorithms may result in limited discrimination of contrast patterns due to the failure to exploit the original data information and inflexible pattern forms. In this paper, we propose a novel algorithm, CHPMiner, which mines a new kind of contrast pattern called contrast hybrid pattern (CHP) that contains nominal attributes and numerical relationships among numerical attributes based on extended gene expression programming (GEP). Specifically, CHPMiner develops two sub-expressions and a novel structure to combine nominal and numerical attributes. Moreover, CHPMiner leverages a specific fitness function to guide the evolution direction for mining CHPs that are highly discriminating. Experiments on four realworld datasets show that CHPMiner outperforms baselines. The case study further demonstrates the effectiveness of CHPMiner.

关键词： Contrast pattern mining Contrast hybrid pattern gene expression programming

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Assessment of flow characteristics through a grassed canal

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2022年第1期7卷 1-18页

作者： Gad, Manal Sobeih, Mohamed F. Rashwan, I. M. H. Helal, Esam Delta Higher Inst Engn & Technol Civil Engn Dept Mansoura Egypt Menoufiya Univ Fac Engn Hydraul Civil Engn Dept Mansoura Egypt Tanta Univ Fac Engn Hydraul Irrigat & Hydraul Engn Dept Tanta Egypt El Menoufiya Univ Fac Engn Civil Engn Dept Al Minufiyah Egypt

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

关键词： Froude number gene expression programming Grassed canal Manning's coefficient Specific energy Submerged Un-grassed canal Un-submerged

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Determination of the Factor of Safety against Sliding of Finite Slopes Using Classical Regression and Soft Computing Approaches

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2024年第2期49卷 1715-1732页

作者： Nourani, Bahram Salmasi, Farzin Abbaspour, Akram Arvanaghi, Hadi Abraham, John Department of Water Engineering University of Tabriz Tabriz Iran School of Engineering University of St. Thomas 2115 Summit Avenue St. Paul 55105 MN United States

Determining the factor of safety against sliding of slopes in engineering projects is a major challenges for civil engineers. A method that can provide an accurate estimation of sliding likelihood can be a significant aid to designers. In the first part of this study, formulae based on classical regression methods such as multiple linear regression (MLR), multiple non-linear regression (MNLR), and multivariate adaptive regression splines (MARS) to calculate the factor of safety (F_s¯_LEM) of finite slopes are developed. In the second part, in order to develop soft computing methods for estimating F_s¯_LEM, from soft computing methods (boosted trees (BT) and gene expression programming (GEP)) and two regression methods (MLR and MNLR) data-driven based methods are used. Values of F_s¯_LEM for development of classical regression and soft computing models are generated using the limit equilibrium methods (LEMs). To assess the performance of the proposed models, different statistical metrics such as R², RMSE, RE%, MAE and NSE, and graphical diagrams such as scatter plots, box plots, RE% plots and Taylor plots are used. Classical regression methods indicate that the results obtained from the MARS model is closer to the extracted results of the MNLR model. Moreover, the results showed that the performance of the GEP model with R² = 0.994, RMSE = 0.0381, RE% = 1.66%, MAE = 0.027 and NSE = 0.992 is better than the other soft computing models for estimating F_s¯_LEM. Designers of simple slopes with homogenous and dry soils could consider using the proposed approaches as an alternative to traditional stability charts and limit equilibrium methods (LEM). © The Author(s), under exclusive licence to Shiraz University 2024.

关键词： Boosted Trees Factor of Safety gene expression programming Limit Equilibrium Method Slope Stability

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Proposing new relationships to estimate the pressuremeter modulus of cohesive and cohesionless media

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2018年第1期3卷 1-9页

作者： Razavi, Shahin Goshtasbi, Kamran Noorzad, Ali Ahangari, Kaveh Islamic Azad Univ Sci & Res Branch Dept Min Engn Tehran Iran Tarbiat Modares Univ Dept Min Engn Jalal AleAhmad StPOB 14115-111 Tehran Iran Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

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.

关键词： In situ test Pressuremeter Soil modulus Artificial neural network gene expression programming

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Estimating Cement Compressive Strength from Microstructural Images using GEP with Probabilistic Polarized Similarity Weight Tournament Selection 18

Estimating Cement Compressive Strength from Microstructural ...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Zhang, Liangliang Yue, Xinya Wang, Lin Yang, Bo Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Linyi Univ Sch Informat Linyi 276000 Shandong Peoples R China

ISBN: (纸本)9781450356183

The safety of building facilities is directly affected by the physical properties of cement, among which cement compressive strength plays the most important role in evaluating them. Therefore, the investigation of cement compressive strength is helpful in improving the cement properties. Traditionally, chemical composition, curing condition, and water-cement ratio are used to estimate the strength of cement paste. However, this approach is limited by the extreme complexity of physical changes and chemical reactions during cement hydration. Considering the cement microstructure contains information related to strength microscopically, microtomography, which can image three-dimensional microstructure, provides scientists with another way to study cement compressive strength nondestructively. This study estimates cement compressive strength using microstructure features extracted from microtomography images and gene expression programming. A probabilistic polarized similarity weight tournament selection operator is also proposed to balance the exploration and exploitation. Experimental results corroborate that the obtained relationship possesses higher estimation accuracy, good interpretability and the evolutionary capability performs well.

关键词： Cement compressive strength gene expression programming microtomography images

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ImmunoOptiDrone-Towards Re-Factoring an Evolutionary Drone Control Model for Use in Immunological Optimization Problems 7th

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7th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Downs, Kevin Coulter, Duncan Univ Johannesburg Johannesburg South Africa

ISBN: (纸本)9783319274003;9783319273990

The work explores the potential for re-purposing an existing evolutionary model, originally intended for the generation of flight control software for low cost search and rescue drones. Freed from realworld physical constraints inherent. The re-purposed model is suitable for use in clonal expansion based optimization contexts. An argument is then made for greater adoption of re-usable component oriented evolutionary systems.

关键词： Clonal expansion gene expression programming Drone control

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Prediction of flow duration curve in ungauged catchments using genetic expression programming 12th

Prediction of flow duration curve in ungauged catchments usi...

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12th International Conference on Hydroinformatics (HIC) - Smart Water for the Future

作者： Razaq, Salaudeen Abdul Shahid, Shamsuddin Ismail, Tarmizi Chung, Eun-Sung Mohsenipour, Morteza Wang, Xiao-jun Univ Teknol Malaysia Fac Civil Engn Johor Baharu 81310 Malaysia Seoul Natl Univ Sci & Technol Dept Civil Engn Seoul 139743 South Korea Nanjing Hydraul Res Inst State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210029 Jiangsu Peoples R China Minist Water Resources Res Ctr Climate Change Nanjing 210029 Jiangsu Peoples R China

A set of multivariate equations have been developed using gene expression programming (GEP) based symbolic regression technique to generate the flow quantiles of flow duration curve (FDC) in the ungauged catchments in the East Coast of Peninsular Malaysia. The equations were derived from four to seven candidate explanatory variables prepared from climatic, geomorphologic, geographic characteristics, soil properties, and land use and land cover information. Support vector machine (SVM) was used to optimize the best combinations for calibration and validation of GEP models from the data available in thirteen gauged catchments in the study area. Seven flow percentiles namely 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95 as well as extreme, maximum, minimum and mean annual flows were identified to develop a framework for predicting various flow metrics. Obtained results revealed that nonlinear regression equations developed using GEP can generate FDCs in ungauged catchments of East Coast of Peninsular Malaysia with an efficiency of up to 0.92. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： flow duration curve gene expression programming multivariate equations symbolic regression ungauged basin

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