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Immunodominance and clonal selection inspired multiobjective clustering

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Progress in Natural Science:materials International 2009年第6期19卷 751-758页

作者： 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 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

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.

关键词： Artificial immune systems Multiobjective optimization Clustering Unsupervised learning

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

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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 key Lab of intelligent perception and image understanding of ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an 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

Immune secondary response and clonal selection inspired optimizers

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Progress in Natural Science:materials International 2009年第2期19卷 237-253页

作者： maoguo gong a,licheng jiao a,Lining Zhang a,Haifeng Du b a 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 b School of Public Policy and Administration,Xi’an jiaotong University,Xi’an 710049,china 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 Policy and Administration Xi’an Jiaotong University Xi’an 710049 China

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

关键词： Clonal selection Secondary response Artificial immune systems Evolutionary algorithms

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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Neuronal population dynamic model: An analytic approach

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Progress in Natural Science:materials International 2009年第9期19卷 1159-1163页

作者： Wentao Huang a, licheng jiao a, Yuelei Xub, Shiping ma b, Jianhua Jia a a Institute of intelligent Information Processing, key laboratory of intelligent perception and image understanding of ministry of education, Xidian University, P.O. Box 224, Xi’an 710071, china b Department of Avionics Engineering, Engineering College of Air Force Engineering University, Xi’an 710038, china Institute of intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University P.O. Box 224 Xi’an 710071 China Department of Avionics Engineering Engineering College of Air Force Engineering University Xi’an 710038 China

A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differential equation. A new function is proposed to characterize the population of excitatory and inhibitory spiking neurons, which is different from the population density function discussed by most researchers. And a novel evolution equation, which is a nonhomogeneous parabolic type equation, is derived. From this, the stationary solution and the firing rate of the stationary states are given. Last, by the Fourier transform, the time dependent solution is also obtained. This method can be used to analyze the various dynamic behaviors of neuronal populations.

关键词： Analytic approach Neuronal population Dynamic model Fourier transform

Reference direction based immune clone algorithm for many-objective optimization

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Frontiers of Computer Science 2014年第4期8卷 642-655页

作者： Ruochen LIU Chenlin ma Fei HE wenping ma licheng jiao key laboratory of intelligent perception and image understanding of ministry of education International Research Center for Intelligent Perception and Computation Xidian University Xi'an 710071 China

In this paper, a new preference multi-objective optimization algorithm called immune clone algorithm based on reference direction method （RD-ICA） is proposed for solving many-objective optimization problems. First, an intelligent recombination operator, which performs well on the functions comprising many parameters, is introduced into an immune clone algorithm so as to explore the potentially excellent gene segments of all individuals in the antibody pop- ulation. Second, a reference direction method, a very strict ranking based on the desire of decision makers （DMs）, is used to guide selection and clone of the active population. Then a light beam search （LBS） is borrowed to pick out a small set of individuals filling the external population. The proposed method has been extensively compared with other recently proposed evolutionary multi-objective optimization （EMO） approaches over DTLZ problems with from 4 to 100 objectives. Experimental results indicate RD-ICA can achieve competitive results.

关键词： many-objective optimization preference multiobjective optimization artificial immune system reference direction method light beam search intelligent recombination operator

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

image segmentation based on differential immune clone clustering algorithm

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International Journal of intelligent Computing and Cybernetics 2013年第1期6卷 83-102页

作者： wenping ma Feifei Ti Congling Li licheng jiao key Lab of intelligent perception and image understanding of ministry of education of china Xidian UniversityXi’anChina

Purpose–The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm(DICCA)to solve image ***/methodology/approach–DICCA combines immune clone selection and differential evolution,and two populations are used in the evolutionary *** reproduction and selection,differential mutation,crossover and selection are adopted to evolve two populations,which can increase population diversity and avoid local *** extracting the texture features of an image and encoding them with real numbers,DICCA is used to partition these features,and the final segmentation result is ***–This approach is applied to segment all sorts of images into homogeneous regions,including artificial synthetic texture images,natural images and remote sensing images,and the experimental results show the effectiveness of the proposed ***/value–The method presented in this paper represents a new approach to solving clustering *** novel method applies the idea two populations are used in the evolutionary *** proposed clustering algorithm is shown to be effective in solving image segmentation.

关键词： Differential evolution Clone selection Clustering image segmentation image processing Cluster analysis

A memetic algorithm based on hyper-heuristics for examination timetabling problems

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International Journal of intelligent Computing and Cybernetics 2015年第2期8卷 139-151页

作者： Yu Lei maoguo gong licheng jiao Yi Zuo key laboratory of intelligent perception and image understanding of ministry of education International Research Center for Intelligent Perception and ComputationXidian UniversityXi’anChina

Purpose–The examination timetabling problem is an NP-hard problem.A large number of approaches for this problem are developed to find more appropriate search ***-heuristic is a kind of representative *** hyper-heuristic,the high-level search is executed to construct heuristic lists by traditional methods(such as Tabu search,variable neighborhoods and so on).The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global ***/methodology/approach–This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling ***,four graph coloring heuristics are employed to construct heuristic *** the evolutionary algorithm framework,the iterative initialization is utilized to improve the number of feasible solutions in the population;meanwhile,the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space(high-level search).At last,two local search methods are combined to optimize the feasible solutions in the solution space(low-level search).Findings–Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark ***/value–The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.

关键词： Evolutionary computation Examination timetabling problem Hyper-heuristic Memetic algorithm