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An immune memory clone algorithm for constrained multi-objective optimization

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2009年第6期37卷 1289-1294页

作者： Shang, Rong-Hua Jiao, Li-Cheng Ma, Wen-Ping Gong, Mao-Guo Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi 710071 China

A new algorithm for constrained multi-objective optimization is presented. The algorithm treats the constraints as an objective and the immune clone and immune memory mechanism are introduced. Therefore, the new algorithm could find the Pareto-optimal solutions from the feasible region and the edge of the infeasible region, which assures both the convergence and diversity of the obtained solutions. Simulation results show that the new algorithm has much better performance in finding a much better spread of solutions, in maintaining a better uniformity of the solutions and in obtaining a better convergence.

关键词： Multiobjective optimization

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Graph based co-training algorithm for web page classification

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2009年第10期37卷 2173-2180+2219页

作者： Hou, Cui-Qin Jiao, Li-Cheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an Shaanxi 710071 China

This paper proposes a novel inductive semi-supervised algorithm for web page classification named GCo-training, exploiting texts in web pages and hyperlinks among them. GCo-training iteratively trains two classifiers-a graph-based semi-supervised classifier based on hyperlinks among web pages and a Bayes classifier based on texts in web pages, under the framework of Co-training. On the one hand, the graph-based semi-supervised classifier obtains high accuracy based on a small set of labeled examples through exploiting links among web pages and can augment labeled examples for the Bayes classifier. On the other hand, the Bayes classifier can also provide labeled example for the graph-based classifier after it learning on labeled set augmented by the graph-based classifier. Therefore, the two classifiers help each other and improve their respective performance during the process of training. Finally, the Bayes classifier can classify a large number of unseen examples. We test GCo-training algorithm, Co-training algorithm based on words occurring on web pages and words occurring in hyperlinks and Bayes algorithm based on EM on the Web&KB dataset. Experimental results show GCo-training performs much better than the other algorithms.

关键词： Websites

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Research on evolutionary multi-objective optimization algorithms

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Ruan Jian Xue Bao/Journal of Software 2009年第2期20卷 271-289页

作者： Gong, Mao-Guo Jiao, Li-Cheng Yang, Dong-Dong Ma, Wen-Ping Institute of Intelligent Information Processing Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding Xidian University Xi'an 710071 China

Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Multiobjective optimization

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image enhancement based on intrascale dependencies of the second generation curvelet transform

Image enhancement based on intrascale dependencies of the se...

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Hao, Hongxia Liu, Fang Jiao, Licheng School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478092

This paper presents a strong noise image enhancement method based on intrascale dependencies of the second generation curvelet transform. Observing that the immediate four neighbor coefficients bear the most important dependencies, we use spatial clustering property of the intrascale neighbor coefficients to separate noise and signal of interest, and to deal with them differently, i.e. to suppress noise and strengthen edges. Comparing our approach with Starck's enhancement model (Starck et al., 2003), we experimentally find that for high noise level images, our method outperforms the starck's system in noise suppression and signal strengthening and produces better enhancement results. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image enhancement

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An automatic seed selection in infrared image based on mountain clustering

An automatic seed selection in infrared image based on mount...

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MIPPR 2009 - Multispectral image Acquisition and processing: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Li, Tao Liu, Jian Lei, Kaibin College of Computer Science and Technology Southwest University for Nationalities Chengdu 610041 China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478054

Because of noise and clutter, the infrared target detection even becomes more difficult. In this paper, we present an automatic seed selection method based on an improved mountain cluster algorithm to be employed in infrared target segmentation. The original image was projected to x and y coordination firstly. Then the modified mountain clustering algorithm is employed on the projection data to obtain the cluster center which is applied as the seed point for region growing. This approach has been performed on a field infrared image. The experimental results show that the proposed method can provide satisfactory segmentation result. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Clustering algorithms

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Clonal selection algorithm based on anti-idiotype

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Ruan Jian Xue Bao/Journal of Software 2009年第5期20卷 1269-1281页

作者： Zhang, Li-Ning Gong, Mao-Guo Jiao, Li-Cheng Ma, Wen-Ping Institute of Intelligent Information Processing Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi'an 710071 China

Based on the antibody clonal selection theory of immunology, an artificial immune system algorithm, clonal selection algorithm based on anti-idiotype (AICSA), is proposed to deal with complex multi-modal optimization problems by introducing the anti-idiotype. This algorithm evolves and improves the antibody population through clonal proliferation, anti-idiotype mutation, anti-idiotype recombination and clonal selection operation, which can perform global search and local search in many directions rather than one direction around the identical antibody simultaneously. Theoretical analysis proves that AICSA can converge to the global optimum. By introducing the anti-idiotype, AICSA can make the most of the structure information of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, AICSA is tested on four different types of functions and compared with the clonal selection algorithm and other optimization methods. Theoretical analysis and experimental results indicate that AICSA achieves a good performance, and is also an effective and robust technique for optimization. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Antibodies

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Clustering-based selection for evolutionary multi-objective optimization

Clustering-based selection for evolutionary multi-objective ...

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IEEE International Conference on intelligent Computing and intelligent Systems (ICIS)

作者： Maoguo Gong Licheng Jiao Gang Cheng Chao Liu Key Laboratory of Intelligent Perception and Image Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9781424447374;9781424447541

In this study, a novel clustering-based selection strategy of nondominated individuals for evolutionary multi-objective optimization is proposed. The new strategy partitions the nondominated individuals in current Pareto front adaptively into desired clusters. Then one representative individual will be selected in each cluster for pruning nondominated individuals. In order to evaluate the validity of the new strategy, we apply it into one state of the art multi-objective evolutionary algorithm. The experimental results based on thirteen benchmark problems show that the new strategy improves the performance obviously in terms of breadth and uniformity of nondominated solutions.

关键词： Genetic algorithms Evolutionary computation Information processing Sorting Chaos Nearest neighbor searches Particle swarm optimization Design optimization

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Granular agent evolutionary algorithm for classification

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2009年第3期37卷 628-633页

作者： Pan, Xiao-Ying Jiao, Li-Cheng Liu, Fang Key Laboratory of Intelligent Perception and Image Understanding Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Computer Science and Engineering Xidian University Xi'an 710071 China

By inspiration of the granular evolutionary algorithm, a Granular Agent Evolutionary algorithm for Classification (GAEC) is proposed. The method uses the granular agent to denote the set of examples that have similar attributions and the knowledge base guides the evolutionary of granular agent. Also some granular evolutionary operators are designed for classification problem. Assimilation operator, exchange operator, and differentiation operator reflect the competitive, cooperative and self-learning ability of agent respectively. Finally, some classification rules are extracted from granular agents by some strategies to forecast the sort of new data. Empirical studies show that the algorithm has a good classification prediction, and only need a small price for the training time. In most UCI datasets, the performance of GAEC is better than G-NET, OCEC and C4.5, which have good performance.

关键词： Evolutionary algorithms

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Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling

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Chinese Physics Letters 2009年第6期26卷 61-64页

作者：陈建芮焦李成吴建设王晓华 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligence Information Processing Xidian University Xi'an 710071 College of Science Inner Mongolia University of Technology Inner Mongolia 010051

A new general network model for two complex networks with time-varying delay coupling is presented. Then we investigate its synchronization phenomena. The two complex networks of the model differ in dynamic nodes, the number of nodes and the coupling connections. By using adaptive controllers, a synchronization criterion is derived. Numerical examples are given to demonstrate the effectiveness of the obtained synchronization criterion. This study may widen the application range of synchronization, such as in chaotic secure communication.

关键词： gamma-ray bursts gamma-rays relativity

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image Compression with Downsampling and Overlapped Transform at Low Bit Rates

Image Compression with Downsampling and Overlapped Transform...

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International Conference on image processing

作者： Jiaji Wu Yan Xing Guangming Shi Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University

ISBN: (纸本)9781424456536;9781424456543

This paper proposes an image coding method based on adaptive downsampling which not only uses the pixel redundancy but also considers visual redundancy. At the encoder side, codec adaptively chooses some smooth regions of the original image to downsample, and then overlapped transform with selectivity, block DCT and adaptive-shape DCT (SA-DCT) are used against the image after being downsampled. For the incomplete transformed image, OB-SPECK is adopted to code. At the decoder side, in order to reduce the computational complexity, we use the simple cubic interpolation which not only is very suitable to the downsampled regions but also enhances greatly the real time of this coding system. Experimental results show the proposed method outperforms JPEG2000, SPECK, SPIHT, and LT+SPECK at low bit rates.

关键词： Adaptive downsampling Interpolation image compression Visual redundancy Bit rate image compression Data compression downsampling Interpolation image coding method original image Transform

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