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Memetic computation based on regulation between neural and immune systems: the framework and a case study

Science China(Information Sciences) 2010年第8期53卷 1519-1527页

作者： GONG MaoGuo , JIAO LiCheng, LIU Fang & YANG Jie key lab of intelligent perception and image Understanding of Ministry of Education of China, Institute of intelligent Information Processing, Xidian University, Xi’an 710071, China 1. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

Lamarckian learning has been introduced into evolutionary computation to enhance the ability of local search. The relevant research topic, memetic computation, has received significant amount of interest. In this study, a novel memetic computational framework is proposed by simulating the integrated regulation between neural and immune systems. The Lamarckian learning strategy of simulating the unidirectional regulation of neural system on immune system is designed. Consequently, an immune memetic algorithm based on the Lamarckian learning is proposed for numerical optimization. The proposed algorithm combines the advantages of immune algorithms and mathematical programming, and performs well in both global and local search. The simulation results based on ten low-dimensional and ten high-dimensional benchmark problems show that the immune memetic algorithm outperforms the basic genetic algorithm-based memetic algorithm in solving most of the test problems.

关键词： memetic computation artificial immune system Lamarckian learning genetic algorithm numerical optimization

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A Special Point and Transfer Component Analysis Based Dynamic Multi-objective Optimization Algorithm 11th

A Special Point and Transfer Component Analysis Based Dynami...

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11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021

作者： Liu, Ruochen Li, Nanxi Peng, Luyao Wu, Kai Key Lab of Intelligent Perception and Image Understanding of Ministry of Education International Center of Intelligent Perception and Computation Xidian University Xi’an710071 China

ISBN: (纸本)9783030720612

To solve a dynamic multi-objective optimization problem better, algorithms need to quickly adapt to environmental changes and track its changing Pareto fronts fast. In this paper, an algorithm (SPTr-RMMEDA) based on special point and transfer component analysis is presented, in which, by using some special points and their neighborhoods solutions, the prediction strategy are used to generated the transfer learning prediction model, together with part new initial solutions to generate next population when change occurs. In order to better adapt to environmental changes, in addition to reusing some important historical information by transfer component analysis, the algorithm also introduces the adaptive diversity introduction strategy. The algorithm performs on 12 groups test problems. And the experimental results show that the proposed algorithm is superior to other four algorithms, such as Tr-NSGA-II, Tr-RMMEDA, DNSGA-II-A and DNSGA-II-B on most test problems in term of convergence and computation complexity. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

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Journal of Systems Engineering and Electronics 2009年第3期20卷 519-526页

作者： Zhang Wenge~(1,2),Liu Fang~(1,2),Jiao Licheng~(2,3)& Gao Xinbo~(2,3) 1.School of Computer Science and Technology,Xidian Univ.,Xi’an 710071,P.R.China 2.key lab.of intelligent perception and image Understanding of Ministry of Education of China,Xi’an 710071,P.R.China 3.Inst,of intelligent Information Processing,Xidian Univ.,Xi’an 710071,P.R.China School of Computer Science and Technology Xidian Univ. Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 P. R. China

To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.

关键词： computer image processing synthetic aperture radar speckle edge detection nonsubsampled second generation bandelet transform Canny operator threshold shrinkage.

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Two-phase clustering algorithm with density exploring distance measure

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CAAI Transactions on Intelligence Technology 2018年第1期3卷 59-64页

作者： Jingjing Ma Xiangming Jiang Maoguo Gong Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University PO Box 224 Xi'an 710071People's Republic of China

Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance （DED） measure. In the first phase, the fast global K-means clustering algorithm is used to obtain the cluster number and the prototypes. Then, the prototypes of all these clusters and representatives of points belonging to these clusters are regarded as the input data set of the second phase. Afterwards, all the prototypes are clustered according to a DED measure which makes data points locating in the same structure to possess high similarity with each other. In experimental studies, the authors test the proposed algorithm on seven artificial as well as seven UCI data sets. The results demonstrate that the proposed algorithm is flexible to different data distributions and has a stronger ability in clustering data sets with complex non-convex distribution when compared with the comparison algorithms.

关键词：密度探索距离聚类算法计算方法人工智能

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Hybrid immune algorithm with Lamarckian local search for multi-objective optimization

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Memetic Computing 2010年第1期2卷 47-67页

作者： Gong, Maoguo Liu, Chao Jiao, Licheng Cheng, Gang Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University 710071 Xi'an China

Lamarckian learning has been introduced into evolutionary computation as local search mechanism. The relevant research topic, memetic computation, has received significant amount of interests. In this study, a novel Lamarckian learning strategy is designed for improving the Nondominated Neighbor Immune Algorithm, a novel hybrid multi-objective optimization algorithm, Multi-objective Lamarckian Immune Algorithm (MLIA), is proposed. The Lamarckian learning performs a greedy search which proceeds towards the goal along the direction obtained by Tchebycheff approach and generates the improved progenies or improved decision vectors, so single individual will be optimized locally and the newcomers yield an enhanced exploitation around the nondominated individuals in less-crowded regions of the current trade-off front. Simulation results based on twelve benchmark problems show that MLIA outperforms the original immune algorithm and NSGA-II in approximating Pareto-optimal front in most of the test problems. When compared with the state of the art algorithm MOEA/D, MLIA shows better performance in terms of the coverage of two sets metric, although it is laggard in the hypervolume metric. © 2009 Springer-Verlag.

关键词： Artificial immune system Lamarckian learning Memetic algorithm Multi-objective optimization

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Community detection in dynamic social networks based on multiobjective immune algorithm

Community detection in dynamic social networks based on mult...

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作者： Gong, Mao-Guo Zhang, Ling-Jun Ma, Jing-Jing Jiao, Li-Cheng Key Lab of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071 China

Community structure is one of the most important properties in social networks, and community detection has received an enormous amount of attention in recent years. In dynamic networks, the communities may evolve over time so that pose more challenging tasks than in static ones. Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms. In this paper, a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks. It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method. Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms. © 2012 Springer Science+Business Media, LLC & Science Press, China.

关键词： Multiobjective optimization

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Visual tracking based on ensemble learning with logistic regression 11th

Visual tracking based on ensemble learning with logistic reg...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Tian, Xiaolin Zhao, Sujie Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center of Intelligent Perception and Computation International Collaboration Joint Lab in Intelligent Perception and Computation Xidian University XianShaanxi710071 China

ISBN: (纸本)9789811036101

In this paper, we propose a novel visual tracking method based on ensemble learning using logistic regression model. We adopt logistic regression to achieve ensemble classifier to deal with object tracking problem. By using fast computable features, our approach learns the appearance of the target during tracking. And thus, the proposed method is able to adapt online to target appearance changes and its surrounding background. Moreover, ensemble learning converts rough rules of thumb into highly accurate prediction rule. Experimental results show that our method outperforms relative trackers. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Regression analysis

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Tracking algorithm based on dual residual network and kernel correlation filters 2nd

Tracking algorithm based on dual residual network and kernel...

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2nd CCF International Conference on Artificial Intelligence, CCF-ICAI 2019

作者： Tian, Xiaolin Li, Fang Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center of Intelligent Perception and Computation International Collaboration Joint Lab in Intelligent Perception and Computation Xidian University XianShaanxi Province710071 China

ISBN: (纸本)9789813292970

Visual target tracking is a target detection task for a period of time. During this period, the tracking target will undergo significant appearance changes due to deformation, sudden movement, complex background and occlusion. These changes make visual target tracking challenging. In this paper, a target tracking algorithm based on dual residual neural network and kernel correlation filters is proposed, which mainly solves the problems of inaccurate tracking. This method combines depth residual neural network with kernel correlation filter tracking algorithm. The template matches the depth residual feature to determine the location of the target and the kernel correlation filter based on residual feature is designed to detect the target, finally, the template matching result and kernel correlation filtering result are fused to determine the location of the target. The experimental results on a large-scale standard data set show that the proposed algorithm has the advantages of high accuracy. Compared with the previous algorithm, the proposed algorithm has good performance in tracking visual deformation, occlusion and fuzzy video objects. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Target tracking

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New optical flow approach for motion segmentation based on gamma distribution

New optical flow approach for motion segmentation based on g...

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16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010

作者： Jung, Cheolkon Jiao, Licheng Gong, Maoguo Key Lab of Intelligent Perception and Image Understanding Xidian University Ministry of Education of China Xi'an 710071 China

ISBN: (纸本)3642113001

This paper provides a new motion segmentation algorithm in image sequences based on gamma distribution. Conventional methods use a Gaussian mixture model (GMM) for motion segmentation. They also assume that the number of probability density function (PDF) of velocity vector's magnitude or pixel difference values is two. Therefore, they have poor performance in motion segmentation when the number of PDF is more than three. We propose a new and accurate motion segmentation method based on the gamma distribution of the velocity vector's magnitude. The proposed motion segmentation algorithm consists of pixel labeling and motion segmentation steps. In the pixel labeling step, we assign a label to each pixel according to the magnitude of velocity vector by optical flow analysis. In the motion segmentation step, we use energy minimization method based on a Markov random field (MRF) for noise reduction. Experimental results show that our proposed method can provide fine motion segmentation results compared with the conventional methods. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Probability density function

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Fine-grained image categorization with fisher vector 11th

Fine-grained image categorization with fisher vector

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Tian, Xiaolin Ding, Xin Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center of Intelligent Perception and Computation International Collaboration Joint Lab in Intelligent Perception and Computation Xidian University Xi’anShaanxi Province710071 China

ISBN: (纸本)9789811036132

Fine-grained image categorization is a categorization task, where classifying objects should be the same basic-level class and have similar shape or visual appearances. Generally, the bag-of-words (BoW) model is popular in image categorization. However, in BoW model, the feature quantization for image representation is also a lossy process, which severely limits the descriptive power of the image representation. Fisher vectors employ soft assignments and reduce information loss due to quantization by calculating the gradient for each parameter separately, which have been shown to outperform other global representations on most benchmark datasets. In this paper, the acquired template is represented by Fisher Vector (FV). Combing FV with improved spatial pyramid matching (SPM) respectively, we use an approach, i.e., FV+SPM, to obtain feature representation. Experimental results show that our method outperforms state-of-the-art categorization approaches on the Caltech-UCSD Birds dataset. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Template matching

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