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| 1 | Immune secondary response and clonal selection inspired optimizers显示文摘The immune system's ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen,immune system usually calls for a more rapid and larger response to the antigen,where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response,two novel artificial immune system algorithms,secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA),are presented for solving single and multi-objective optimization problems,respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the clonal selection theory,CSO reproduces individuals and selects their improved maturated progenies after the affinity mat-uration process. SRO copies certain antibodies to a secondary pool,whose members do not participate in CSO,but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand,decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimiza-tion problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor edit-ing process. Compared with some existing evolutionary algorithms,such as OGA/Q,IEA,IMCPA,BGA and AEA,SRCPA is shown to be able to solve complex optimization problems,such as high-dimensional function optimizations,with better performance. On the other hand,SRCMOA combines the Pareto-strength based fitness assignment strategy,CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA,NSGA-Ⅱ,SPEA,and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto-optimal fronts with wide distributions. | Maoguo Gong Licheng Jiao Lining Zhang Haifeng Du | 2009 | Progress in Natural Science:Materials International2009，19，2： | 24 |
| 2 | Optimal approximation of linear systems by artificial immune response显示文摘This paper puts forward a novel artificial immune response algorithm for op-timal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm. | GONG Maoguo DU Haifeng JIAO Licheng | 2006 | Science in China(Series F)2006，49，1： | 21 |
| 3 | Memetic computation based on regulation between neural and immune systems: the framework and a case study显示文摘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. | 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 | 2010 | Science China(Information Sciences)2010，53，8： | 16 |
| 4 | Multiobjective optimization using an immunodominance and clonal selection inspired algorithm显示文摘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 affin-ity,all dominated individuals(antibodies) are divided into two kinds,subdominant antibodies and cryptic antibodies.Moreover,local search only applies to the sub-dominant antibodies,while the cryptic antibodies are redundant and have no func-tion 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. | GONG MaoGuo JIAO LiCheng MA WenPing DU HaiFeng | 2008 | Science in China(Series F)2008，51，8： | 6 |
| 5 | Adaptive chaos clonal evolutionary programming algorithm显示文摘Based on the chaos movement and the clonal selection theory, a novel artificial immune system algorithm, Adaptive Chaos Clonal Evolutionary Programming Algorithm (ACCEP), is proposed in this paper. The new algorithm uses the Logistic Sequence to control the mutation scale and uses the Chaos Mutation Operator to control the clonal selection. Compared with SGA and Clonal Selection Algorithm, ACCEP can enhance the precision and stability, avoid prematurity to some extent, and have the high convergence speed. The results of the experiment indicate that ACCEP has the capability to solve complex machine learning tasks, like Multimodal Function Optimization. | DU Haifeng GONG Maoguo LIU Ruochen JIAO Licheng | 2005 | Science in China(Series F)2005，48，5： | 5 |
| 6 | Intelligent multi-user detection using an artificial immune system显示文摘Artificial immune systems (AIS) are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations. | GONG MaoGuo, JIAO LiCheng, MA WenPing & MA JingJing 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 | 2009 | Science in China(Series F)2009，52，12： | 5 |
| 7 | Immunodominance and clonal selection inspired multiobjective clustering显示文摘The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested. | Wenping Ma , Licheng Jiao, Maoguo Gong Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China | 2009 | Progress in Natural Science:Materials International2009，19，6： | 3 |
| 8 | An efficient shortest path approach for social networks based on community structure显示文摘 | Maoguo Gong Guanjun Li Zhao Wang Lijia Ma Dayong Tian | 2016 | CAAI Transactions on Intelligence Technology2016，1，1： | 2 |
| 9 | Community detection in networks by using multiobjective evolutionary algorithm with decomposition显示文摘 | Maoguo Gong Lijia Ma Qingfu Zhang Licheng Jiao | 2012 | Physica A: Statistical Mechanics and its Applications2012，，15： | 2 |
| 10 | SAR changedetection based on intensity and texture changes 显示文摘 | Gong Maoguo Li Yu Jiao Licheng | 2014 | ISPRSJournal of Photogrammetry and Remote Sensing2014，93，： | 1 |
| 11 | Niching clonal selection algorithm for multimodal function optimization显示文摘 | LIN Hao GONG Maoguo SUN Yifei | 2006 | Lecture Notes in Computer Science2006，4221，： | 1 |
| 12 | Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition显示文摘 | GONG Maoguo CAI Qing CHEN Xiaowei | 2014 | IEEE Transactions on Evolutionary Computation2014，18，1： | 1 |
| 13 | Multi-objective Immune Algorithm with Non-dominated Neighbor-based 3election显示文摘 | Gong Maoguo Jiao Licheng DuHaiteng | 2008 | Evo- lutionary Computation2008，16，2： | 1 |
| 14 | Multiobjective immune algorithm with nondominated neighbor based selection显示文摘 | Gong Maoguo Jiao Licheng Du Haifeng | 2008 | Evolutionary Computa- tion2008，16，2： | 1 |
| 15 | Baldwinian learning in clonal selection algorithm for op- timization显示文摘 | Gong Maoguo Jiao Licheng Zhang Lining | 2010 | Information Sciences2010，180，8： | 1 |
| 16 | Multiobjective immune algorithm with nondominated neighbor-based selection显示文摘 | Gong Maoguo Jiao Licheng Du Haifeng | 2008 | Evolutionary Computation2008，16，2： | 1 |
| 17 | Multi-objec- tive immune algorithm with nondominated neighbor-based selec- tion显示文摘 | GONG Maoguo JIAO Licheng DU Haifeng | 2008 | Evolutionary Computation2008，16，2： | 1 |
| 18 | Change detection in syn- thetic aperture radar images based on image fusion and fuzzy cluste- ring显示文摘 | Gong Maoguo Zhou Zhiqiang Ma Jingjing | 2012 | IEEE Trans on Image Processing2012，21，4： | 1 |
| 19 | Fuzzy c-means clustering with local information and kernelmetric for image segmentation显示文摘 | Gong Maoguo Liang Yan Shi Jiao | 2013 | IEEE Trans on Im-age Processing2013，22，2： | 1 |
| 20 | Multiobjective immune algorithm with nondominated neighbor-based selection evolutionary computation显示文摘 | Gong Maoguo Jiao Licheng Du Haifeng | 2008 | Evolutionary Computation2008，16，2： | 1 |
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