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Computation of some generalized Ramsey numbers

Journal of Combinatorial Mathematics and Combinatorial Computing 2010年 75卷 217-228页

作者： Shao, Zehui Xu, Jin Bao, Qiquan Pan, Linqiang Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

For given graphs G1,G2, the 2-color Ramsey number R(G1,G2) is defined to be the least positive integer n such that every 2-coloring of the edges of complete graph Kn contains a copy of G1 colored with the first color or a copy of G2 colored with the second color. In this note, we obtained some new exact values of generalized Ramsey numbers such as cycle versus book, book versus book, complete bipartite graph versus complete bipartite graph.

关键词： Graph theory

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Ramsey multiplicities of graphs with five vertices

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Journal of Combinatorial Mathematics and Combinatorial Computing 2010年 75卷 3-9页

作者： Shao, Zehui Pan, Linqiang Xu, Jin Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

The Ramsey multiplicity M(G) of a graph G is defined to be the smallest number of monochromatic copies of G in any two-coloring of edges of K R(G), where R(G) is the smallest integer n such that every graph on n vertices either contains G or its complement contains G. With the help of computer algorithms, we obtain the exact values of Ramsey multiplicities for most of isolate-free graphs on five vertices, and establish upper bounds for a few others.

关键词： Graph theory

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Unsupervised evolutionary clustering algorithm for mixed type data

Unsupervised evolutionary clustering algorithm for mixed typ...

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Congress on Evolutionary Computation

作者： Zhi Zheng Maoguo Gong Jingjing Ma Licheng Jiao Qiaodi Wu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, we propose a novel unsupervised evolutionary clustering algorithm for mixed type data, evolutionary k-prototype algorithm (EKP). As a partitional clustering algorithm, k-prototype (KP) algorithm is a well-known one for mixed type data. However, it is sensitive to initialization and converges to local optimum easily. Global searching ability is one of the most important advantages of evolutionary algorithm (EA), so an EA framework is introduced to help KP overcome its flaws. In this study, KP is applied as a local search strategy, and runs under the control of the EA framework. Experiments on synthetic and real-life datasets show that EKP is more robust and generates much better results than KP for mixed type data.

关键词： Clustering algorithms Prototypes Algorithm design and analysis Partitioning algorithms Classification algorithms Evolutionary computation Encoding

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Optimizing detector distribution in V-detector negative selection using a constrained multiobjective immune algorithm

Optimizing detector distribution in V-detector negative sele...

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Congress on Evolutionary Computation

作者： Fang Liu Maoguo Gong Jingjing Ma Licheng Jiao Wei Zhang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

In this paper, a novel constrained multiobjective immune algorithm for optimizing detector distribution in V-detector negative selection is proposed. The theory of artificial immune system (AIS) and the spirit of population evolution are introduced to generate detectors. By combining the constraint handling technique and AIS-based multiobjective optimization, the algorithm is able to steadily maximize the anomaly coverage with little extra cost, which means the distribution with maximized coverage of the non-self space and minimized overlapping among detectors with fixed size will be well realized. Furthermore, the new approach is tested on some benchmark problems. The experimental results show that compared with some state-of-the-art methods, our algorithm can remarkably outperform them in terms of enhancing the detection rate by optimizing distribution without increasing the number of detectors.

关键词： Detectors Optimization Immune system Testing Shape Training data Aerospace electronics

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Immunodomaince based clonal selection clustering algorithm

Immunodomaince based clonal selection clustering algorithm

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Congress on Evolutionary Computation

作者： Ruochen Liu Zhengchun Shen Licheng Jiao Wei Zhang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. An immunodomaince operator is introduced to the clonal selection algorithm, which can realize on-line gaining prior knowledge and sharing information among different antibodies. The proposed method has been extensively compared with Fuzzy C-means (FCM), Genetic Algorithm based FCM (GAFCM) and Clonal Selection Algorithm based FCM (CSAFCM) over a test suit of several real life datasets and synthetic datasets. The result of experiment indicates the superiority of the ICSCA over FCM, GAFCM and CSAFCM on stability and reliability for its ability to avoid trapping in local optimum.

关键词： Clustering algorithms Variable speed drives Immune system Partitioning algorithms Algorithm design and analysis Accuracy Immunity testing

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Clonal Selection Algorithm for image Compression

Clonal Selection Algorithm for Image Compression

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Congress on Evolutionary Computation

作者： Ruochen Liu Licheng Jiao Wei Zhang Jingjing Ma Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Vector Quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Mean/residual vector quantization (M/RVQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, Clonal Selection Algorithm for image Compression (CSAIC) is proposed. In CSAIC, Based on M/RVQ algorithm, an improved clonal selection algorithm is used to cluster the data of compressed images in order to obtain the optimal codebook. The proposed method has been extensively compared with Linde-Buzo-Gray(LBG), Self-Organizing Mapping (SOM) and Modified K-means(Mod-KM) over a test suit of seven natural images. The experimental results show that CSAIC outperforms other three algorithms in terms of image compression performance.

关键词： image coding Clustering algorithms Algorithm design and analysis Training Encoding Vector quantization

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A hybrid multiobjective immune algorithm with region preference for decision makers

A hybrid multiobjective immune algorithm with region prefere...

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Congress on Evolutionary Computation

作者： Licheng Jiao Wei Zhang Ruochen Liu Fang Liu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Recently, one of the main tools of decision maker (DM) preference incorporation in the multiobjective optimization (MOO) has been using reference points and achievement scalarizing functions (ASF). The core idea of these methods is converting the original multiobjective problem (MOP) into single objective problem by using ASF to find a single preferred point. However, many DMs not only interest in a single point but also a set of efficient points in their preferred region. In this paper, we introduce a hybrid multiobjective immune algorithm (HMIA) for DM. It combines the immune inspired algorithm and region preference based on a novel dominance concept called region-dominance without ASF. The new algorithm can let DMs flexibly decide the number of reference points and accurately determine the preferred region with its simple and effective interactive methods. To exemplify its advantages, simulated results of HMIA are shown with some well-known problems.

关键词： Delta modulation Neodymium Immune system Cloning Encoding Computational complexity

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Quantum-inspired immune clonal clustering algorithm based on watershed

Quantum-inspired immune clonal clustering algorithm based on...

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Congress on Evolutionary Computation

作者： Yangyang Li Nana Wu Jingjing Ma Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing and State Key Laboratory of Integrated Services Network Xidian University Xi'an China

Based on the concepts and principles of quantum computing, a novel clustering algorithm, called a quantum-inspired immune clonal clustering algorithm based on watershed (QICW), is proposed to deal with the problem of image segmentation. In QICW, antibody is proliferated and divided into a set of subpopulation groups. Antibodies in a subpopulation group are represented by multi-state gene quantum bits. In the antibody's updating, the quantum mutation operator is applied to accelerate convergence. The quantum recombination realizes the information communication between the subpopulation groups so as to avoid premature convergences. In this paper, the segmentation problem is viewed as a combinatorial optimization problem, the original image is partitioned into small blocks by watershed algorithm, and the quantum-inspired immune clonal algorithm is used to search the optimal clustering centre, and make the sequence of maximum affinity function as clustering result, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with the genetic clustering algorithm based on watershed (W-GAC), and the k-means algorithm based on watershed (W-KM).

关键词： Clustering algorithms image segmentation Error analysis Radiative recombination Feature extraction Wavelet transforms Partitioning algorithms

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Algorithm of Partition based Network Boosting for imbalanced data classification

Algorithm of Partition based Network Boosting for imbalanced...

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International Joint Conference on Neural Networks (IJCNN)

作者： Gou Shuiping Yang Hui Jiao Licheng Zhuang Xiong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China and Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

Network Boosting (NB) is an ensemble learning method which combines weak learners together based on a network and can learn the target hypothesis asymptotically. NB has higher generalization ability compared to Bagging and AdaBoost. But, when datasets are class-imbalanced, the performance of NB will decrease quickly. In order to solve this problem, we present a Partition based Network Boosting method (PNB) to classify imbalanced data. For PNB method, every classifier node of the classifier network is provided with the same number of training data which are all of same weights. The classifier in the network is built by the balanced training set sampled from the training data according to the weights record of the training data it holds. And then, the weights of the instances of every node classifier are updated based on the classification results of self-node and its neighbor nodes. The classifier network is trained repeatedly in such a way. Weight factor of hypothesis in the training progress is introduced to improve the performance. The final classification is formed by all the hypotheses of the classifier network learned during the training progress so that the label of new instances can be decided by the weight voting. The experimental results on UCI data and imbalanced biomedical data show that the PNB algorithm has better AUC and recall performance compared with NB learning machine.

关键词： Laboratories Impedance matching Breast Boosting Liver

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A new roadmap constitution method based on dividing grid and level-set

A new roadmap constitution method based on dividing grid and...

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International Symposium on Intelligence Information processing and Trusted Computing

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Information Engineering of Hubei University for Nationalities Enshi Hubei China Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies HUST Wuhan China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China HUST Wuhan China

ISBN: (纸本)9780769541969

Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of start and goal of route, so the roadmap can not be reused when anyone of start and goal changed. Absence of reusability reduces the efficiency of route planning by using roadmap. A new roadmap constitution method was proposed in this paper, which is build on dividing the 2D configuration space into multi-scale grids and setting nodes of roadmap on grid boundary using Level-Set method, same method was used to construct the map cells of roadmap between any two nodes lays on same grid . The roadmap constituted with new method can be reused when the start or goal was changed;it is efficient for replanning problem. © 2010 IEEE.

关键词： Reusability

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