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Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds

EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY 2009年第10期44卷 4044-4050页

作者： Si, Hongzong Lian, Ning Yuan, Shuping Fu, Aiping Duan, Yun-Bo Zhang, Kejun Yao, Xiaojun Qingdao Univ Growing Base State Key Lab Lab New Fibrous Mat & Modern Text Inst Computat Sci & Engn Qingdao 266071 Peoples R China Jiangsu Teachers Univ Technol Sch Chem & Chem Engn Changzhou 213001 Jiangsu Peoples R China Zhejiang Univ Dept Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Lanzhou Univ Dept Chem Lanzhou 730000 Gansu Peoples R China

gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC50 for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones. Crown Copyright (C) 2009 Published by Elsevier Masson SAS. All rights reserved.

关键词： Anticoccidial drug gene expression programming cGMP-dependent protein kinase Quantitative structure-activity relationships

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High energy physics data analysis with gene expression programming

High energy physics data analysis with gene expression progr...

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Nuclear Science Symposium/Medical Imaging Conference

作者： Teodorescu, Liliana Brunel Univ London England

ISBN: (纸本)0780392213

gene expression programming is a new evolutionary algorithm that overcomes many limitations of the more established genetic Algorithms and genetic programming. Its first application to high energy physics data analysis is presented. The algorithm was successfully used for event selection on samples with both low and high background level. The signal/background classification accuracy was over 90% in all cases.

关键词： evolutionary algorithms gene expression programming event selection

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Combining clonal selection algorithm and gene expression programming for time series prediction

Combining clonal selection algorithm and gene expression pro...

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3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems

作者： Litvinenko, V. I. Bidyuk, P. I. Bardachov, J. N. Sherstjuk, V. G. Fefelov, A. A. Natl Tech Univ Ukraine Inst Appl Syst Anal Kiev Ukraine

ISBN: (纸本)9780780394452

Dynamic system identification algorithm is developed using the basic mechanisms of clonal selection and idea of a new, evolutionary computing paradigm - gene expression programming. On the basis of the algorithm developed a computer based system, is proposed for making decisions relevant to forecasting of single variable and multivariate lime series. The results of computing experiments achieved with the system developed show high quality of short and medium period forecasts.

关键词： artificial immune systems clonal selection algorithm gene expression programming time series

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Remnant Life Estimation of Power Transformer using Oil UV-Vis Spectral Response

Remnant Life Estimation of Power Transformer using Oil UV-Vi...

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IEEE/PES Power Systems Conference and Exposition

作者： Abu-Siada, A. Pin, Lai Sin Islam, Syed Curtin Univ Technol Dept Elect & Comp Engn Perth WA Australia

ISBN: (纸本)9781424438105

UV-Spectrophotometry is a non-intrusive test used to determine the transformer's integrity. Accurate interpretation of uv-spectrophotometry provides reasonable information on the health of power transformer that can be used to plan cost effective maintenance, retirement, relocation, and operational criteria. The Ultraviolet-to-Visible (UV-Vis) spectral response of transformer oil can be measured instantly with relatively cheap equipment and does not need an expert person to conduct the test. Results show that there is a good correlation between oil spectral response and its furan contents;consequently correlation between transformer aging and UV trend can be easily established. The paper introduces a novel fuzzy logic approach to estimate transformer aging using oil UV-Vis spectral response.

关键词： Transformer Diagnosis Furan analysis Ultraviolet to Visible Spectroscopy gene expression programming Fuzzy Logic

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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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A Novel Algorithm of gene expression programming Based on Simulated Annealing

A Novel Algorithm of Gene Expression Programming Based on Si...

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智能计算及其应用国际会议

作者： CAI Zhihua, JIANG Siwei, Zhu Li , GUO Yuanyuan (School of Computer , China University of Geosciences, Wuhan 430074, China)

In order to solve the prediction problems of non-linear, this paper presents a hybrid parallel gene expression programming algorithm, which is based on Simulated Annealing. The combination of Simulate

关键词： gene expression programming simulated annealing multi population strategy MPI

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Heuristic methods to test frequencies optimization for analogue circuits diagnosis

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2008年第1期56卷 29-38页

作者： Jantos, P. Grzechca, D. Golonek, T. Rutkowski, J. Silesian Tech Univ Fac Automat Control PL-44100 Gliwice Poland

This paper presents methods for optimal test frequencies search with the use of heuristic approaches. It includes a short summary of the analogue circuits fault diagnosis and brief introductions to the soft computing techniques like evolutionary computation and the fuzzy set theory. The reduction of both, test time and signal complexity are the main goals of developed methods. At the before test stage, a heuristic engine is applied for the principal frequency search. The methods produce a frequency set which can be used in the SBT diagnosis procedure. At the after test stage, only a few frequencies can be assembled instead of full amplitude response characteristic. There are ambiguity sets provided to avoid a fault tolerance masking effect.

关键词： analog electronic circuits analog fault diagnosis fuzzy logic gene expression programming genetic algorithm simulated annealing

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An application of artificial intelligence for rainfall-runoff modeling

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JOURNAL OF EARTH SYSTEM SCIENCE 2008年第2期117卷 145-155页

作者： Aytek, Ali Asce, M. Alp, Murat Gazi Univ Dept Civil Engn Hydraoul Div TR-27310 Gaziantep Turkey State Hydraul Works TR-34696 Istanbul Turkey

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, gene expression programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R-2) are used to measure the performance of the models. The results indicate that the proposed, genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.

关键词： artificial intelligence artificial neural networks evolutionary computation genetic programming gene expression programming rainfall runoff

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genetic programming-Based Empirical Model for Daily Reference Evapotranspiration Estimation

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CLEAN-SOIL AIR WATER 2008年第10-11期36卷 905-912页

作者： Guven, Aytac Aytek, Ali Yuce, Mehmet Ishak Aksoy, Hafzullah Gaziantep Univ Dept Civil Engn Hydraul Div TR-27310 Gaziantep Turkey Istanbul Tech Univ Dept Civil Engn Hydraul Div TR-80626 Istanbul Turkey

genetic programming (GP) is presented as a new tool for the estimation of reference evapotranspiration by using daily atmospheric variables obtained from the California Irrigation Management Information System (CIMIS) database. The variables employed in the model are daily solar radiation, daily mean temperature, average daily relative humidity and wind speed. The results obtained are compared to seven conventional reference evapotranspiration models including: (1) the Penman-Monteith equation modified by CIMIS, (2) the Penman-Monteith equation modified by the Food and Agricultural Organization (FAO 56), (3) the Hargreaves-Samani equation, (4) the solar radiation-based ET0 equation, (5) the Jensen-Haise equation, (6) the Jones-Ritchie equation, and (7) the Turc method. Statistical measures such as average, standard deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient are used to measure the performance of the model developed by employing GP. Statistics and scatter plots indicate that the new equation produces quite satisfactorily results and can be used as an alternative to the conventional models.

关键词： Evapotranspiration genetic programming Artificial intelligence gene expression programming

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Dictionary Method for Multiple Soft and Catastrophic Fault Diagnosis Based on Evolutionary Computation

Dictionary Method for Multiple Soft and Catastrophic Fault D...

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International Conference on Signals and Electronic Systems (ICSES 2008)

作者： Korzybski, Marek Tech Univ Lodz Fac Elect Elect Comp & Control Engn PL-90924 Lodz Poland

ISBN: (纸本)9788388309472

In this paper a new method for detection, location and identification of multiple soft and catastrophic faults in linear and nonlinear analog circuits is described. It is based on the node approach and two evolutionary methods: gene expression programming (GEP) and genetic algorithm (GA). On the before test stage the algorithm needs repeated analyses of circuit-under-test (CUT) with different values of parameters which can be faulty. The results form the training set for constructing a dictionary. It consists of all sets created with groups of possible faulty parameters, associated with them formulas for computing these parameters, the lower and the upper limits of node voltages of the circuit with faulty parameters. These formulas are determined using GEP, one of the new evolutionary methods. On the after test stage the method obtains the set of possible faulty elements and calculates the values of possible faulty elements. The accuracy of the fault identification of this results is usually unsatisfactory. The requested precision of fault detection can be with the already known method for soft fault diagnosis accomplished. The time consumed for obtaining formulas enabling us to calculate faulty parameters increases with the number of possible faulty elements and the number of simultaneously faulty elements. The results of the method depend on parameters of GEP process.

关键词： electric circuit diagnosis parametric faults catastrophic faults multiple faults evolutionary methods gene expression programming genetic algorithms

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