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检索条件"主题词=Gene Expression Programming"

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gene expression programming and Artficial Neural Network Approaches for Event Selection in High Energy Physics

Gene Expression Programming and Artficial Neural Network App...

引用

15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors/ 2006 IEEE Nuclear Science Symposium

作者： Teodorescu, Liliana Reid, Ivan D. Brunel Univ London England

ISBN: (纸本)9781424405619

gene expression programming is a new evolutionary algorithm found to be very efficient for solving benchmark problems from computer science. The algorithm was also successfully tested for event selection in high energy physics data analysis. This paper presents an extended event selection analysis with this algorithm, as well as a comparison of its results with those obtained with an Artificial Neural Network. Both methods produced selection functions that allowed high classification accuracies, around 95%.

关键词： evolutionary algorithms gene expression programming artificial neural network event selection classification

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gene expression programming with DAG chromosome

引用

2nd International Symposium on Intelligence Computation and Application (ISICA 2007)

作者： Quan, Hui-yun Yang, Guangyi Hunan Normal Univ Coll Math & Comp Sci Changsha 410081 Peoples R China Hunan Coll Informat Changsha 410200 Hunan Peoples R China

ISBN: (纸本)9783540745808

GEP(gene expression programming) is applied to comprehensive fields such as Symbolic Regression, Parameter Optimization, Cellular Automate etc[2]. With Kara-style chromosome,GEP can only express tree phynotype. This limits the expressiveness of the program that can be evolved. In this paper, a DAG(Directed Acyclic Graph) chromosome is integrated into GEP without increasing the computational complexity of fitness evaluation while improving the expressiveness of gene expression programming.

关键词： gene expression programming directed acyclic graph symbolic regression

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gene expression programming with A Local Search Operator 19

Gene Expression Programming with A Local Search Operator

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19th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Safavi, Asghar Amir Kelarestaghi, Manoochehr Eshghi, Farshad Natl Iranian Oil Co West Oil & Gas Prod Co Ahvaz Iran Kharazmi Univ Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781538625859

gene expression programming (GEP) is one of the newest evolutionary algorithms, the linear model of genetic programming that have been much attention to it, in recent years. In this article this algorithm and memetic algorithms are discussed. Here we are tried to improve its efficiency by combining gene expression programming with a local search method. The proposed algorithm called GEP-LS and it is applicable for all problems in the field of evolutionary computation. Random Mutation Hill-Climbing (RMHC) and Simulated Annealing (SA) methods are separately used to implement local search and their results are compared with each other. Finally, a comparison with the conventional gene expression programming algorithm is performed. These comparisons is performed on problems of symbolic regression, sequence induction with constants creation and robotic planning. The results show that performance of the proposed algorithm with RMHC method is relatively better than other algorithms and is able to solve all problems used here with higher accuracy and lower error.

关键词： genetic programming gene expression programming memetic algorithms local search operator Random Mutation Hill climbing Simulated Anealing

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gene expression programming-Fuzzy Logic Method For Crop Type Classification

Gene Expression Programming-Fuzzy Logic Method For Crop Type...

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6th International Conference on genetic and Evolutionary Computing (ICGEC)

作者： Omkar, S. N. Ramaswamy, Nikhil Senthilnath, J. Bharath, S. Anuradha, N. S. Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India Dr Ambedkar Inst Technol Dept Comp Sci & Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9780769547633;9781467321389

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.

关键词： crop classification gene expression programming fuzzy logic

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gene expression programming Based Matching Suitability Analysis in Geomagnetic Aided Navigation

Gene Expression Programming Based Matching Suitability Analy...

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9th International Conference on Natural Computation (ICNC)

作者： Wang, Peng Hui, Xiaoping Wul, Meiping Mul, Hua Deng, Lei Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781467347143

In geomagnetic aided navigation (GAN), matching suitability denotes the navigability of candidate matching areas (CMAs) and can be characterized by the suitable-matching features extracted from geomagnetic map. However, the consistency between the single suitable-matching feature and matching probability is not satisfactory. Therefore the suitable-matching features are considered to be synthesized in order to analyze the matching suitability more effectively. In this study, gene expression programming (GEP) is utilized for feature synthesis, and correlation coefficient is treated as the fitness function. Experimental results show that the evolutionary synthetical feature is effective and owns more excellent performance than the single suitable-matching feature. The conclusions of this article can be used for selecting suitable-matching areas and further afford guidance for trajectory planning.

关键词： matching suitability gene expression programming geomagnetic aided navigation feature synthesis

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gene expression programming Based on Subexpression Library and Clonal Selection

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3rd International Conference on Intelligence Computation and Applications

作者： Xue, Siqing Wu, Jie China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9783540921363

gene expression programming (GEP) is a recently developed evolutionary computation method for model learning and knowledge discovery. Sometimes it is not easy when use GEP to solve too complex problem, so enhancing the algorithm learning capability is necessary. This paper proposes all immune principle based GEP algorithm (iGEP), which combines gene library and clonal election algorithm. The gene library is composed of subexpressions of GEP expression selected from the process of evolution. The proposed algorithm introduces some new features. including the best subexpression of GEP expression is selected as the solution of the problem, and some segments of gene library are used for hypermutation and receptor editing. In terms of convergence rate and computational efficiency, the experimented results on some benchmark problems of the UCI repository show that iGEP outperforms the standard GEP.

关键词： gene expression programming clonal selection gene library best subexpression

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gene expression programming for Static Security Assessment of Power Systems

Gene Expression Programming for Static Security Assessment o...

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general Meeting of the IEEE-Power-and-Energy-Society

作者： Khattab, H. M. Abdelaziz, A. Y. Mekhamer, S. F. Badr, M. A. L. El-Saadany, E. F. Engn Petr & Proc Ind ENPPI Cairo Egypt Ain Shams Univ Fac Engn Dept Elect Power & Machine Cairo Egypt Univ Waterloo Elect & Comp Engn Dept Waterloo ON Canada

ISBN: (纸本)9781467327299

In this paper, a novel gene expression programming (GEP) algorithm is introduced for power system static security assessment. The GEP algorithms as evolutionary algorithms for pattern classification have recently received attention for classification problems because they can perform global searches. The proposed methodology introduces the GEP for the first time in static security assessment problems. The proposed algorithm is examined using different IEEE standard test systems. Different contingency case studies have been used to test the proposed methodology. The GEP based algorithm formulates the problem as a multi-class classification problem using the one-against-all binarization method. The algorithm classifies the security of the power system into three classes, normal, alert and emergency. Performance of the algorithm is compared with other neural network based algorithm classifiers to show its superiority in static security assessment.

关键词： Static security gene expression programming probabilistic neural network radial basis function neural network line outage voltage level power system classifier

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gene expression programming Software Application for High Energy Physics Data Analysis

Gene Expression Programming Software Application for High En...

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15th International Workshop on Room-Temperature Semiconductor X- and Gamma-Ray Detectors/ 2006 IEEE Nuclear Science Symposium

作者： Teodorescu, Liliana Brunel Univ London England

ISBN: (纸本)9781424405619

gene expression programming is an evolutionary algorithm developed in 2001. For a number of benchmark applications, it is a few order of magnitude more efficient that the better known genetic Algorithms and genetic programming. The algorithm was recently successfully tested in particle physics for signal and background classification. The present paper presents a software implementation of this algorithm, adequate for the analysis of particle physics data.

关键词： gene expression programming evolutionary algorithms software applications

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gene expression programming Model for Determining Location in Wireless LANs

Gene Expression Programming Model for Determining Location i...

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6th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM)

作者： Zhao, Yongxiang Zhou, Huaibei Li, Meifang Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Wuhan Univ Technol Sch Management Wuhan 430070 Peoples R China

ISBN: (纸本)9781424437092

The traditional path loss model of log distance assumes that the distance between users and access points has the exponential decline relationship with the received signal strength. However, a large number of experiments show that the model is not accurate. Therefore, the relationship between the distance and the RSS can not be summarized by a simple function. We can investigate the hidden principle of data by all kinds of evolutionary algorithms such as gene expression programming or GEP in order to further improve the accuracy of the model. In this paper, an indoor positioning model based on gene expression programming or GEP is proposed, and it makes full use of GEP to dig up the hidden non-linear relationship between the distance and received signal strength. Experimental results demonstrated that the proposed GEP model can improve the average error of 20.1 meters of traditional path loss model to 4.4 meters when using trilateration method.

关键词： Wireless LANs indoor positioning received signal strength path loss model gene expression programming

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gene expression programming based on simulated annealing

Gene Expression Programming based on simulated annealing

引用

International Conference on Wireless Communications, Networking and Mobile Computing

作者： Jiang, SW Cai, ZH Zeng, D Liu, YD Li, Q China Univ Geosci Coll Comp Wuhan 430074 Peoples R China

ISBN: (纸本)078039335X

gene expression programming (GEP) is a genotype/phenotype system that evolves computer programs of different sizes and shapes encoded in linear chromosomes of fixed length. However, the performance of basic GEP is highly dependent on the genetic operators' rate. In this work, we present a new algorithm called GEPSA that combines GEP and Simulated Annealing (SA), and GEPSA decreases the dependence on genetic operators' rate without impairing the performance of GEP. Three function finding problems, including a benchmark problem of prediction sunspots, are tested on GEPSA, results shows that importing Simulated Annealing can improve the performance of GEP.

关键词： gene expression programming simulated annealing function finding regression analysis

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