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Congress on Evolutionary Computation

作者： Maoguo Gong Licheng Jiao Wenping Ma Ronghua Shang 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 study, we introduce a hybrid immune algorithm based on the intelligent recombination operator and clonal selection algorithm. The intelligent recombination operator uses orthogonal experimental design for factor analysis which identifies the potential gene segments from two individuals to improve their antigenic affinities. The new algorithm, termed as Hybrid Immune Algorithm with Recombination (HIAR), can avoid the decrease of gene diversity in evolutionary process. It evaluates the hamming distance before recombination and uses the two individuals which have the largest hamming distance between each other to implement intelligent recombination operator. It is shown empirically that HIAR has better performance in solving benchmark functions as compared with Intelligent Evolutionary Algorithm and Clonal Selection Algorithm.

关键词： Immune system Competitive intelligence Design for experiments Hamming distance Computational intelligence Approximation algorithms Design optimization Algorithm design and analysis Evolutionary computation Protection

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Quantum-inspired evolutionary multicast algorithm

Quantum-inspired evolutionary multicast algorithm

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yangyang Li Licheng Jiao Jingjing Zhao Qiuyi 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

As a global optimizing algorithm, genetic algorithm (GA) is applied to solve the problem of multicast more and more. GA has more powerful searching ability than traditional algorithm, however its property of ¿prematurity¿ makes it difficult to get a good multicast tree. A quantum-inspired evolutionary algorithm (QEA) to deal with multicast routing problem is presented in this paper, which saliently solves the ¿prematurity¿ problem in genetic based multicast algorithm. Furthermore, in QEA, the individuals in a population are represented by multistate gene quantum bits and this representation has a better characteristic of generating diversity in population than any other representations. In the individual's updating, the quantum rotation gate strategy is applied to accelerate convergence. The algorithm has the property of simple realization and flexible control. The simulation results show that QEA has a better performance than CS and conventional GA.

关键词： Multicast algorithms Competitive intelligence Laboratories Information processing Evolutionary computation Routing Genetic algorithms Quality of service Quantum computing Heuristic algorithms

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Hybrid multiobjective estimation of distribution algorithm by local linear embedding and an immune inspired algorithm

Hybrid multiobjective estimation of distribution algorithm b...

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Congress on Evolutionary Computation

作者： Dongdong Yang Licheng Jiao Maoguo Gong Hongxiao Feng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

A novel hybrid multiobjective estimation of distribution algorithm is proposed in this study. It combines an estimation of distribution algorithm based on local linear embedding and an immune inspired algorithm. Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise continuous (m-1)-dimensional manifold, where m is the number of objectives. By this regularity, a local linear embedding based manifold algorithm is introduced to build the distribution model of promising solutions. Besides, for enhancing local search ability of the EDA, an immune inspired sparse individual clone algorithm (SICA) is introduced and combined with the EDA. The novel hybrid multiobjective algorithm, named HMEDA, is proposed accordingly. Compared with three other state-of-the-art multiobjective algorithms, this hybrid algorithm achieves comparable results in terms of convergence and diversity. Besides, the tradeoff proportions of EDA to SICA in HMEDA are studied. Finally, the scalability to the number of decision variables of HMEDA is investigated too.

关键词： Electronic design automation and methodology Machine learning algorithms Probability distribution Evolutionary computation Cloning Hybrid power systems Genetic mutations Genetic algorithms Sampling methods Principal component analysis

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Reconstruction of images from compressive sensing based on the stagewise fast LASSO

Reconstruction of images from compressive sensing based on t...

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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： Wu, Jiao 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 College of Sciences China Jiliang University Hangzhou 310018 China

ISBN: (纸本)9780819478092

Compressive sensing (CS) is a theory about that one may achieve a nearly exact signal reconstruction from the fewer samples, if the signal is sparse or compressible under some basis. The reconstruction of signal can be obtained by solving a convex program, which is equivalent to a LASSO problem with l1-formulation. In this paper, we propose a stage-wise fast LASSO (StF-LASSO) algorithm for the image reconstruction from CS. It uses an insensitive Huber loss function to the objective function of LASSO, and iteratively builds the decision function and updates the parameters by introducing a stagewise fast learning strategy. Simulation studies in the CS reconstruction of the natural images and SAR images widely applied in practice demonstrate that the good reconstruction performance both in evaluation indexes and visual effect can be achieved by StF-LASSO with the fast recovered speed among the algorithms which have been implemented in our simulations in most of the cases. Theoretical analysis and experiments show that StF-LASSO is a CS reconstruction algorithm with the low complexity and stability. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Compressed sensing

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M-elitist evolutionary algorithm and its application to V-BLAST system

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Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 2009年第10期31卷 2443-2448页

作者： Mu, Cai-Hong Jiao, Li-Cheng Liu, Yi Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China State Key Laboratory of Integrated Service Networks Xidian University Xi'an 710071 China School of Electronic Engineering Xidian University Xi'an 710071 China

A new algorithm named as M-elitist Evolutionary Algorithm (MEA) is presented with low complexity and high performance to approach the performance of Maximum-Likelihood(ML) detection, for solving the problem of the high complexity of ML detection in real-time Vertical- Bell laboratories LAyered Space-Time (V-BLAST) communication system. The simulation of one knapsack problem validates the effectiveness of MEA to solve combinatorial optimization problems. Furthermore, the simulation of V-BLAST communication system shows that the MEA-based detection algorithm can approach the performance of ML well, and is superior to the detection algorithm based on standard genetic algorithm and that based on clonal selection algorithm as well as some classical ones.

关键词： Combinatorial optimization

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A Parallel Artificial Immune Model for Optimization

A Parallel Artificial Immune Model for Optimization

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International Conference on Computational Intelligence and Security

作者： Yutao Qi Fang Liu Licheng Jiao School of Computer Science And Technology Key Laboratory of Intelligent Perception and Imagee Understanding of Ministry of Education Xidian University Xi'an China Institute of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

This paper presents a parallel artificial immune model termed as tower master-slave model (TMSM) for solving optimising problems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than individual migration as has been done in parallel genetic algorithms. It is a good balance between population diversity and the convergent speed. Experimental results on the numerical optimization and TSP problems show that PIMCSA achieves good performance in terms of both solution quality and computation time.

关键词： Immune system Genetic algorithms Artificial immune systems Competitive intelligence Master-slave Laboratories Computer science education Poles and towers Vaccines Parallel processing

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M-elite coevolutionary algorithm for numerical optimization

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Ruan Jian Xue Bao/Journal of Software 2009年第11期20卷 2925-2938页

作者： Mu, Cai-Hong Jiao, Li-Cheng Liu, Yi Key Laboratory of Intelligent Perception and Image Understanding Xidian University Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710071 China School of Electronic Engineering Xidian University Xi'an 710071 China

The M-elite coevolutionary algorithm (MECA) is proposed for high-dimensional unconstrained numerical optimization problems based on the concept of coevolutionary algorithm and elitist strategy. In the MECA, the individuals with high fitness, called elite population, is considered to play dominant roles in the evolutionary process. The whole population is divided into two subpopulations which are elite population composed of M elites and common population including other individuals, and team members are selected to form M teams by M elites acting as the cores of the M teams (named as core elites) respectively. If the team member selected is another elite individual, it will exchange information with the core elite with the cooperating operation defined in the paper;If the team member is chosen from the common population, it will be led by the core elite with the leading operation. The cooperating and leading operation above are defined by different combinations of several crossover operators or mutation operators. The algorithm is proved to converge to the global optimization solution with probability one. Tests on 15 benchmark problems show that the algorithm can find the global optimal solution or near-optimal solution for most problems tested. Compared with three existing algorithms, MECA achieves an improved accuracy with the same number of function evaluations. Meanwhile, the runtime of MECA is less, even compared with the standard genetic algorithm with the same parameter setting. Moreover, the parameters of the MECA are analyzed in experiments and the results show that MECA is insensitive to parameters and easy to use. © by Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Global optimization

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Three-dimensional source reconstruction for bioluminescence tomography using the LSQR method with preconditioning

Three-dimensional source reconstruction for bioluminescence ...

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MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Yu, Jingjing Liu, Fang Jiao, Licheng He, Xiaowei School of Computer Science and Technology Xidian University Xi'an 710071 China Scholl of Physics and Information Technology Shaanxi Normal University Xi'an 710062 China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Information Science and Technology Northwest University Xi'an 710069 China

ISBN: (纸本)9780819478085

As a novel optical molecular imaging modality, Bioluminescence Tomography (BLT) aims at quantitative reconstruction of the bioluminescent source distribution inside the biological tissue from the optical signals measured on the living animal surface, which is a highly ill-posed inverse problem. In this paper, with the finite element method solving the diffusion equation, an iterative regularization algorithm, referred to as least square QR-factorization (LSQR), is applied to the inverse problem in BLT. The affect of the preconditioning strategy on the LSQR method (PLSQR) for BLT is also investigated. The Simulations with a heterogeneous mouse chest phantom demonstrate that by incorporating a priori knowledge of the permissible source region, the LSQR method can reconstruct the source accurately and stably. Moreover, by employing preconditioning strategy, PLSQR outperforms LSQR in terms of source power density and convergence speed. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Inverse problems

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Multisensor image adaptive fusion based on nonsubsampled contourlet

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Jisuanji Xuebao/Chinese Journal of Computers 2009年第11期32卷 2227-2238页

作者： Chang, Xia Jiao, Li-Cheng Jia, Jian-Hua Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Information Engineering Jingdezhen Ceramic Institute Jingdezhen Jiangxi 333002 China

An adaptive fusion method of multisensor images based on nonsubsampled contourlet transform is proposed in this paper, which can select the fusion weights of the low-frequency coefficients adaptively via golden section algorithm. The nonsubsampled contourlet transform is a flexible multi-scale, multi-direction and shift-invariant image decomposition, which is suitable for representing images bearing abundant detail and directional information. This is employed for fusing the directional high-frequency coefficients. For the directional high-frequency coefficients, the higher adding level of the directional subbands is used to select the better coefficient for fusion. The nonsubsampled contourlet transform can also avoids introducing ringing artifacts to fused images compared to ordinary method. Experimental results show that the proposed method achieves better fusion efficiency compared to image fusion methods based on Laplacian pyramid transform, wavelet transform, stationary wavelet transform and contourlet transform respectively.

关键词： Contourlet transform

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Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

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Science in China(Series F) 2008年第8期51卷 1064-1082页

作者： GONG MaoGuo JIAO LiCheng MA WenPing DU HaiFeng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Public and Administration Xi'an Jiaotong University Xi'an 710049 China

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.

关键词： multiobjective optimization immunodominance clonal selection artificial immune systems evolutionaryalgorithms

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