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International Conference of Soft Computing and Pattern Recognition

作者： Azmi, Nurulhuda Firdaus Mohd Timmis, Jon Polack, Fiona Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

ISBN: (纸本)9781424453306

Within the context of information ltering, learning and adaptation of user pro les is a challenging research area and is, in part, addressed by work in Adaptive Information Filtering (AIF). In order to be effective in a dynamic context, maintaining ltering performance, information ltering systems need to adapt to changes. We argue that arti cial immune systems (AIS) exhibit the properties required by AIF, and have the potential to be exploited in the context of AIF. In this paper, we extract general features of immune systems and AIF, based on a principled meta-probe approach. We then propose an architecture for AIF incorporating ideas from AIS. Having such characteristics as adaptability, diversity and self-organised, we argue that AIS have suitable characteristics that are amenable to the task of AIF.

关键词： Adaptive Information Filtering Arti cial Immune Systems Pro le Adaptation clonal selection algorithm

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A Novel Artificial Immune algorithm for Job Shop Scheduling

A Novel Artificial Immune Algorithm for Job Shop Scheduling

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International Conference on Computational Intelligence and Natural Computing

作者： Hong, Lu Huaihai Inst Technol Dept Elect Engn Lianyungang Peoples R China

ISBN: (纸本)9780769536453

The job shop scheduling problem (JSSP) is one of the most difficult problems, as it is classified as an NP-complete one. In this paper, by integrating chaos mechanism and niche technique, a novel artificial immune algorithm based on the clonal selection principle and idiotypic immune network theory exhibited in biological immune system is proposed to solve classical job shop scheduling problem. Taking advantages of the ergodic and stochastic properties of chaotic variable, an adaptive chaos mutation operator is designed by the combination of prior knowledge of antibody and evolution iterations. The operator can avoid blind search and enhance the convergence speed effectively. Experimental results indicate that the proposed algorithm is highly competitive, being able to produce better solutions than GA and clonalG in several cases, and is a viable alternative for solving efficiently job shop scheduling problem.

关键词： artificial immune systems clonal selection algorithm chaos job shop scheduling problem

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Optimal path planning for autonomous airship based on clonal selection and direct collocation algorithm

Optimal path planning for autonomous airship based on clonal...

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IEEE International Conference on Networking, Sensing and Control

作者： Liu, Yu Zhang, Yachen Hu, Yueming S China Univ Technol Coll Automat Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9781424416851

A novel algorithm, based on clonal selection and direct collocation theories, is introduced to solve the three-dimensional optimal path problem of airship. Firstly, the six-DOF (Degree of Freedom) nonlinear dynamic model for a special kind of airship is presented. Then, the model of novel algorithm is designed from clonal selection and direct collocation aspects respectively, and the dissipative energy function of airship is designed as performance index function. So the optimal control problem has been converted into the nonlinear programming problem which must subject to constraint conditions and performance index. Finally, nonlinear programming problem is solved by presented algorithm, and gets satisfied solutions. Simulation results also demonstrate the validity and feasibility of the presented novel algorithm for solving the optimal path problem of airship.

关键词： airship direct collocation theory clonal selection algorithm optimal path planning

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A Novel Clone selection algorithm for High-dimensional Global Optimization Problems

A Novel Clone Selection Algorithm for High-dimensional Globa...

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2009 Asia-Pacific Conference on Information Processing

作者： Xingbao LIU Liangwu SHI Rongyuan CHEN Haijun CHEN Educational Center of Modern Technique Hunan College of Business Changsha,Hunan,China P.R.

The clone selection algorithm（CSA） is a stochastic,population-based evolutionary method that can be applied to the global optimization *** paper proposes a variation on the traditional CSA:clone selection algorithm with simplex crossover,or *** novel algorithm employs the randomized distribution scheme for clone individuals,bit hyper-mutation and simplex crossover to significantly improve the performance of the original *** of the CSAPX on 23 benchmark optimization problems shows a marked improvement in performance over the traditional CSA.

关键词： clonal selection algorithm simplex crossover global optimization differential evolution

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A fast clonal selection algorithm for constructing an immune neural network

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11th Joint International Computer Conference (JICC 2005)

作者： Liu, HB Gu, GC Shen, J Yan, F Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9812565329

Unsupervised learning strategies such as self-organizing map (SOM) may be more fascinating in some applications in which lacks of supervised signals. But the standard SOM network has a fixed number of outputs that must be pre-specified before training, which lacks of flexibility. In this paper, a fast clonal selection algorithm (FCSA) for constructing an immune neural network (INN) is presented based on clonal selection principle. The INN is a two-layer network whose number of outputs is adaptable according to the task and the affinity threshold. The constructed INN is similar to the ABNET proposed by de Castro et al, but the constructing algorithm FCSA is remarkably simpler, faster, and more facile than that of ABNET, which can be demonstrated in the simulation experiments.

关键词： immune neural network clonal selection algorithm unsupervised learning

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Combining clonal selection algorithm and gene expression programming for time series prediction

Combining clonal selection algorithm and gene expression pro...

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3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems

作者： Litvinenko, V. I. Bidyuk, P. I. Bardachov, J. N. Sherstjuk, V. G. Fefelov, A. A. Natl Tech Univ Ukraine Inst Appl Syst Anal Kiev Ukraine

ISBN: (纸本)9780780394452

Dynamic system identification algorithm is developed using the basic mechanisms of clonal selection and idea of a new, evolutionary computing paradigm - gene expression programming. On the basis of the algorithm developed a computer based system, is proposed for making decisions relevant to forecasting of single variable and multivariate lime series. The results of computing experiments achieved with the system developed show high quality of short and medium period forecasts.

关键词： artificial immune systems clonal selection algorithm gene expression programming time series

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A Novel Artificial Immune algorithm for Job Shop Scheduling

A Novel Artificial Immune Algorithm for Job Shop Scheduling

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2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Lu Hong Department of Electronic Engineering Huaihai Institute of Technology Lianyungang, China

关键词： artificial immune systems clonal selection algorithm chaos job shop scheduling problem

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Prototype reduction using an artificial immune model

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PATTERN ANALYSIS AND APPLICATIONS 2008年第3-4期11卷 353-363页

作者： Garain, Utpal Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata India

Artificial immune system (AIS)-based pattern classification approach is relatively new in the field of pattern recognition. The study explores the potentiality of this paradigm in the context of prototype selection task that is primarily effective in improving the classification performance of nearest-neighbor (NN) classifier and also partially in reducing its storage and computing time requirement. The clonal selection model of immunology has been incorporated to condense the original prototype set, and performance is verified by employing the proposed technique in a practical optical character recognition (OCR) system as well as for training and testing of a set of benchmark databases available in the public domain. The effect of control parameters is analyzed and the efficiency of the method is compared with another existing techniques often used for prototype selection. In the case of the OCR system, empirical study shows that the proposed approach exhibits very good generalization ability in generating a smaller prototype library from a larger one and at the same time giving a substantial improvement in the classification accuracy of the underlying NN classifier. The improvement in performance has been statistically verified. Consideration of both OCR data and public domain datasets demonstrate that the proposed method gives results better than or at least comparable to that of some existing techniques.

关键词： nearest neighbor classification prototype selection artificial immune system clonal selection algorithm statistical significance

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IDENTIFICATION OF PRESTRESS FORCE IN CONCRETE BEAMS BY FINITE ELEMENT MODEL UPDATING

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10th International Symposium on Structural Engineering for Young Experts

作者： Wang, Ying Zhu, Xin-Qun Hao, Hong Ou, Jin-Ping Univ Western Australia CRC Integrated Engn Asset Management Crawley WA 6009 Australia

ISBN: (纸本)9787030227058

This paper presents a numerical study of Finite element model updating to identify prestress force in a concrete beam. A finite element model for prestressed and reinforced concrete beam is developed. A newly developed optimization method, clonal selection algorithm (CSA), is adopted for model updating calculations. Several objective functions, defined in terms of the static response, vibration frequencies of the beam, changes in the vibration frequencies, or the weighted combination of the static response and vibration frequencies, are used for finite element model updating. It is found that the changes in the vibration frequencies, among the four considered objective functions, give the best identification results of the prestress force in the beam. By smearing the vibration frequencies with different level of noises, the updating still successfully identified the prestress force. Numerical simulations demonstrate to the reliability of the method in identifying the prestress force in concrete beams.

关键词： Prestress force Finite element clonal selection algorithm Model updating

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Immune memory clonal selection algorithms for designing stack filters

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NEUROCOMPUTING 2007年第4-6期70卷 777-784页

作者： Dong, Weisheng Shi, Guangming Zhang, Li Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

Stack filters are a class of non-linear filters for suppressing the noise that is uncorrelated with the signal. Their design is formulated as a highly nonlinear optimization problem. A modified immune clonal selection algorithm, called immune memory clonal selection algorithm, is employed to perform the configuration of filters design. The new algorithm has the advantage of preventing from prematurity and fast convergence speed. As an experiment, the stack filters are used to restore images corrupted by uncorrelated additive noise with the level from 10% to 50%. The filters are trained on the small regions of the noise-free and noisy image and then applied to the whole image. The new algorithm has faster convergence speed than that of genetic algorithm. The results are compared with that using the median filter. It turns out that, with our proposed algorithm, a smaller MAE for all noise levels is achieved and much detailed information of the images is preserved. The results show that the new algorithm is effective and feasible. (c) 2006 Elsevier B.V. All rights reserved.

关键词： enhancement nonlinear filter median filters stack filters clonal selection algorithm

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