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An Improved Artificial Immune Algorithm with a Dynamic Threshold

Journal of Bionic engineering 2006年第2期3卷 93-97页

作者： Zhang Qiao Xu Xu Liang Yan-chun Key Laboratory of Symbol Computation and Knowledge Engineering （Ministry of Education P. R. China） Qianwei Campus Jilin University Changchun 130012 P. R. China College of Mathematics Qianwei Campus Jilin University Changchun 130012 P. R. China

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.

关键词： dynamic threshold artificial immune algorithm genetic algorithm antibody

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Identification of Transcription Factor Binding Sites Using GA and PSO

Identification of Transcription Factor Binding Sites Using G...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Xiao-yu Chang Chun-guang Zhou Yan-wen Li Ping Hu College of Computer Science Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University China College of Computer Science Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education department of computer science Northeast Normal University Changchun China

Identification of transcription factor binding sites from the upstream regions of genes is a highly important and unsolved problem. In this paper, we propose a novel framework for using evolutionary algorithm to solve this challenging issue. Under this framework, we use two prevalent evolutionary algorithms: genetic algorithm (GA) and particle swarm optimization (PSO) to find unknown sites in a collection of relatively long intergenic sequences that are suspected of being bound by the same factor. This paper represents binding sites motif to position weight matrix (PWM) and introduces how to code PWM to genome for GA and how to code it to particle for PSO. We apply these two algorithms to 5 different yeast saccharomyces cerevisiae transcription factor binding sites and CRP binding sites. The results on saccharomyces cerevisiae show that it can find the correct binding sites motifs, and the result on CRP shows that these two algorithms can achieve more accuracy than MEME and Gibbs sampler

关键词： Evolutionary computation Pulse width modulation Genomics Bioinformatics Genetic algorithms Particle swarm optimization Biology computing Evolution (biology) Educational institutions Computer science

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Combining Greedy Method and Genetic Algorithm to Identify Transcription Factor Binding Sites

Combining Greedy Method and Genetic Algorithm to Identify Tr...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Wanyu Wang Xiaoyu Chang Chunguang Zhou College of Computer Science Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

Identification of Transcription Factor Binding Sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a novel approach to identify transcription factor binding sites. This approach combines greedy method and genetic algorithm (CGGA) to search conserved segment in the given sequence set. A new greedy method which can efficiently search a local optimal result is proposed. In order to solve the high complexity of this algorithm, we also give an effective improvement for this method. Then, we describe how to combine genetic algorithm with this greedy method to find the more optimal results. Greedy method is combined to the fitness function of the genetic algorithm. We apply this approach on two different TFBS sets and the results show that it can find correct result both effective and efficient, and for CRP binding sites, it get a more accurate result than Gibbs Sampler, AlignACE and MDGA.

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Prediction of Transcription Factor Binding Sites Using Genetic Algorithm

Prediction of Transcription Factor Binding Sites Using Genet...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiaoyu Chang Wengang Zhou Chunguang Zhou Yanchun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

Identification of transcription factor binding sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a new approach to predict TFBS. This approach uses position weight matrix (PWM) to represent binding sites and uses genetic algorithm (GA) to search the best matrix. A new coding method so called multiple-variable coding is proposed in GA. We apply it on two transcription factors rebl and mgl. The result shows that this approach can find most of the known sites, which indicates that this method is very effective

关键词： Genetic algorithms Pulse width modulation Sequences Genomics Bioinformatics Hidden Markov models Proteins Computer science Educational technology knowledge engineering

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An Ontology-based Method in Information Translation

An Ontology-based Method in Information Translation

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International Conference on Semantics, knowledge and Grid (SKG)

作者： Bai Yan Bai Yan Liu Dayou Liu Dayou Open Symbol Computation and Knowledge Engineering Laboratory of State Education Ministry College of Computer Science and Technology Jilin University China

An ontology-based method named AOBM is proposed in this paper. It fully takes into account the factors that will afect the communication, and using ontology can be represented in agent's knowledge base. Pmvided ontology did not belong to the public data or have any public ontology, AOBM establishes a layering algorithm, which is able to avoid logical exception handling policies, ensure the right understanding of the communication entity concept, and resolve the consistency problem of the concept translation. Experimental results show that AOBM is superior to other methods, and can effectively improve the communication consistency of a mobile agent system under certain conditions.

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Mutual conversion of regression and classification based on least squares support vector machines

Mutual conversion of regression and classification based on ...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Jiang, Jing-Qing Song, Chu-Yi Wu, Chun-Guo Liang, Yang-Chun Yang, Xiao-Wei Hao, Zhi-Feng Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China School of Mathematical Science South China University of Technology Guangzhou 510640 China

ISBN: (纸本)354034439X

Classification and regression are most interesting problems in the fields of pattern recognition. The regression problem can be changed into binary classification problem and least squares support vector machine can be used to solve the classification problem. The optimal hyperplane is the regression function. In this paper, a one-step method is presented to deal with the multi-category problem. The proposed method converts the problem of classification into the function regression problem and is applied to solve the converted problem by least squares support vector machines. The novel method classifies the samples in all categories simultaneously only by solving a set of linear equations. Demonstrations of numerical experiments are performed and good performances are obtained. Simulation results show that the regression and classification can be converted each other based on least squares support vector machines. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning systems

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Fuzzy kernel clustering based on particle swarm optimization

Fuzzy kernel clustering based on particle swarm optimization

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IEEE International Conference on Granular Computing (GRC)

作者： Libiao Zhang Chunguang Zhou Ming Ma Xiaohua Liu Chunxia Li Caitang Sun Miao Liu College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China Jilin University Changchun China

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Intelligent Sub-Object Image Retrieval System

Intelligent Sub-Object Image Retrieval System

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World Congress on Intelligent Control and Automation (WCICA)

作者： Wenhui Li Dongfeng Han Yi Wang Xiaosuo Lu Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science&Technology Jilin University Changchun Jilin China

This paper describes a sub-object retrieval system based on a segmentation method. We also use dynamic partial function (DPF) and indexing by locality sensitive hashing (LSH) for improving system performance. Such a system is useful for finding a sub-object from a large image database. In order to obtain the sub-object from a sample image, we use a segmentation method to cut out the object. The system utilizes the segmentation results to capture the higher-level concept of images and gets a stable and accurate result. Experimental and comparison results, which are performed using a general purpose database containing 20,000 images, are encouraging

关键词： Image retrieval Image segmentation Image databases Vehicle dynamics Indexing Information retrieval Content based retrieval Spatial databases Humans Laboratories

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An Efficient Method for Very Large Scale Out-of-Core Terrain Visualization

An Efficient Method for Very Large Scale Out-of-Core Terrain...

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International Conference on Artificial Reality and Telexistence (ICAT)

作者： Huijie Zhang Jigui Sun Haihong Yu Changsong Qi College of Computer Science Northeast Normal University China College of Computer Science and Technology Jilin University China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education China

Large scale terrain visualization with high- resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method based on multi-resolution storage techniques. In external memory, the terrain data set is subdivided from top to bottom to build a multi-resolution hierarchical structure based on a quad-tree. The hierarchical structure can decimate the elevation data that must be loaded into internal memory. Thus it can improve the efficiency of I/O access greatly. Moreover, in order to implement rapid data retrieval of the real time terrain flyover, an efficient indexing algorithm is proposed, in which those nodes in the hierarchical structure will be divided into several clusters in terms of the similarities of static error and the closed space constraint. In addition, a method for crack-free is also proposed here. The comprehensive experiment conducted on the GTOP30 data set shows that this approach outperforms the Block and the Hierarchy algorithms in the both ways of efficiency and simplification ratio.

关键词： Large-scale systems Clustering algorithms Information retrieval Educational institutions Computer science Data visualization Educational technology Costs Data structures Partitioning algorithms

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The TPSO algorithm to solve geometric constraint problems

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Journal of computational Information Systems 2006年第4期2卷 1311-1316页

作者： Yuan, Hua Li, Wenhui Yi, Rongqin Zhao, Kong Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China School of Computer Science and Engineering Changchun University of Technology Changchun 130012 China Suzhou Top Institute of Information Technology Suzhuo 215311 China

The constraint problem can be transformed to an optimization problem. Particle swarm optimization (PSO) is a new evolutionary computation technique. Even PSO has many attractive properties, but it lacks global search ability at the end of the run. This paper introduce a hybrid approach called the TPSO that simultaneously applies particle swarm optimization (PSO), and tabu search (TS) to create a generally well-performing search heuristics, and combat the problem of premature convergence. The new algorithm considers candidate solutions and their fitness as individuals, which are based on their recent search progress. The tabu search makes each particle to reset its record of its best position, to avoid making direction and velocity decisions on the basis of outdated information. The feasibility of the proposed method is demonstrated on Solving Geometric Constraint Problems.

关键词： Computer aided design

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